{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:53:25Z","timestamp":1762300405904,"version":"build-2065373602"},"reference-count":169,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1016\/j.asoc.2023.110591","type":"journal-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T21:22:44Z","timestamp":1688592164000},"page":"110591","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Mass movement susceptibility prediction and infrastructural risk assessment (IRA) using GIS-based Meta classification algorithms"],"prefix":"10.1016","volume":"145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7488-5591","authenticated-orcid":false,"given":"Sk Ajim","family":"Ali","sequence":"first","affiliation":[]},{"given":"Meriame","family":"Mohajane","sequence":"additional","affiliation":[]},{"given":"Farhana","family":"Parvin","sequence":"additional","affiliation":[]},{"given":"Antonietta","family":"Varasano","sequence":"additional","affiliation":[]},{"given":"Sliman","family":"Hitouri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3910-9076","authenticated-orcid":false,"given":"Ewa","family":"\u0141upikasza","sequence":"additional","affiliation":[]},{"given":"Quoc Bao","family":"Pham","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2023.110591_b1","doi-asserted-by":"crossref","DOI":"10.1007\/s12665-021-10152-4","article-title":"A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (Case study: Yozidar-Degaga Route in Kurdistan Province, Iran)","volume":"81","author":"Asadi","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"10.1016\/j.asoc.2023.110591_b2","doi-asserted-by":"crossref","first-page":"5291","DOI":"10.1007\/s10064-020-01915-7","article-title":"A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran)","volume":"79","author":"Emami","year":"2020","journal-title":"Bull. Eng. Geol. Environ."},{"issue":"211","key":"10.1016\/j.asoc.2023.110591_b3","article-title":"A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods","volume":"14","author":"Zhao","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.asoc.2023.110591_b4","first-page":"1","article-title":"Landslide occurrences in the hilly areas of Rwanda, their causes and protection measures","volume":"1","author":"Bizimana","year":"2015","journal-title":"Disaster Sci. Eng."},{"key":"10.1016\/j.asoc.2023.110591_b5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41062-018-0175-y","article-title":"Social and environmental impacts of landslides","volume":"3","author":"Turner","year":"2018","journal-title":"Innov. Infrastruct. Solut."},{"key":"10.1016\/j.asoc.2023.110591_b6","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.geomorph.2010.10.016","article-title":"Evidence of debris flow occurrence after wildfire in upland catchments of south-east Australia","volume":"125","author":"Nyman","year":"2011","journal-title":"Geomorphology"},{"key":"10.1016\/j.asoc.2023.110591_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.110544","article-title":"An image recognition method for the deformation area of open-pit rock slopes under variable rainfall","volume":"188","author":"Li","year":"2022","journal-title":"Measurement"},{"issue":"2801","key":"10.1016\/j.asoc.2023.110591_b8","article-title":"Geospatial analysis of mass-wasting susceptibility of four small catchments in mountainous area of Miyun county, Beijing","volume":"16","author":"Cao","year":"2019","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.asoc.2023.110591_b9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-022-10410-z","article-title":"Integrated analysis of geophysical approaches for slope failure characterisation","volume":"81","author":"Zakaria","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"10.1016\/j.asoc.2023.110591_b10","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-319-77377-3_1","article-title":"Mass wasting: An overview","author":"Pradhan","year":"2019","journal-title":"Landslides Theory Pract. Model."},{"issue":"9096","key":"10.1016\/j.asoc.2023.110591_b11","article-title":"Highway proneness appraisal to landslides along taiping to Ipoh segment Malaysia, using MCDM and GIS techniques","volume":"14","author":"Yamusa","year":"2022","journal-title":"Sustainability"},{"key":"10.1016\/j.asoc.2023.110591_b12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-022-10389-7","article-title":"Application of an ensemble learning model based on random subspace and a J48 decision tree for landslide susceptibility mapping: A case study for Qingchuan, Sichuan, China","volume":"81","author":"Li","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"10.1016\/j.asoc.2023.110591_b13","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s10346-021-01830-2","article-title":"Triggering factors, behavior, and social impact of the 2021 hail-debris flows at the Central Valley of Chile","volume":"19","author":"Romero","year":"2022","journal-title":"Landslides"},{"key":"10.1016\/j.asoc.2023.110591_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.earscirev.2019.103003","article-title":"Lahars and debris flows: Characteristics and impacts","volume":"201","author":"Thouret","year":"2020","journal-title":"Earth Sci. Rev."},{"issue":"6","key":"10.1016\/j.asoc.2023.110591_b15","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1007\/s10346-023-02030-w","article-title":"Elevation dependence of landslide activity induced by climate change in the eastern Pamirs","volume":"20","author":"Pei","year":"2023","journal-title":"Landslides"},{"key":"10.1016\/j.asoc.2023.110591_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-75476-w","article-title":"Mass wasting susceptibility assessment of snow avalanches using machine learning models","volume":"10","author":"Choubin","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2023.110591_b17","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.scitotenv.2019.03.461","article-title":"Sediment mobility and connectivity in a catchment: A new mapping approach","volume":"672","author":"Zingaro","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.asoc.2023.110591_b18","doi-asserted-by":"crossref","DOI":"10.5772\/intechopen.95641","article-title":"Centrifuge modeling of multi-row stabilizing piles reinforced reservoir landslide with different row spacings","author":"Zhang","year":"2022","journal-title":"Landslides"},{"key":"10.1016\/j.asoc.2023.110591_b19","first-page":"1","article-title":"Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning","author":"Costache","year":"2021","journal-title":"Geocarto Int."},{"key":"10.1016\/j.asoc.2023.110591_b20","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1080\/13658816.2021.1968407","article-title":"Enriching the metadata of map images: A deep learning approach with GIS-based data augmentation","volume":"36","author":"Hu","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.asoc.2023.110591_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolind.2020.106620","article-title":"GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, na\u00efve Bayes tree, bivariate statistics and logistic regression: A case of Topl\u2019a basin, Slovakia","volume":"117","author":"Ali","year":"2020","journal-title":"Ecol. Indic."},{"key":"10.1016\/j.asoc.2023.110591_b22","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1007\/s40808-019-00593-z","article-title":"Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India","volume":"5","author":"Ali","year":"2019","journal-title":"Model. Earth Syst. Environ."},{"key":"10.1016\/j.asoc.2023.110591_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2019.104223","article-title":"Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling","volume":"183","author":"Arabameri","year":"2019","journal-title":"Catena"},{"key":"10.1016\/j.asoc.2023.110591_b24","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1080\/19475705.2021.1944330","article-title":"A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping","volume":"12","author":"Pham","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"10.1016\/j.asoc.2023.110591_b25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40677-021-00192-0","article-title":"Mass movements susceptibility mapping by using heuristic approach. Case study: Province of T\u00e9touan (North of Morocco)","volume":"8","author":"Elmoulat","year":"2021","journal-title":"Geoenvironmental Disaster."},{"key":"10.1016\/j.asoc.2023.110591_b26","doi-asserted-by":"crossref","first-page":"245","DOI":"10.3166\/RIG.25.245-265","article-title":"A linear indexing approach to mass movements susceptibility mapping-the case of the Chefchaouen province (Morocco)","volume":"25","author":"Mastere","year":"2015","journal-title":"Rev. Int. de G\u00e9omatique"},{"key":"10.1016\/j.asoc.2023.110591_b27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-021-10013-0","article-title":"An integrated approach for evaluating the flash flood risk and potential erosion using the hydrologic indices and morpho-tectonic parameters","volume":"80","author":"Abu El-Magd","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"10.1016\/j.asoc.2023.110591_b28","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1007\/s12145-021-00653-y","article-title":"Spatial modeling and susceptibility zonation of landslides using random forest, na\u00efve bayes and K-nearest neighbor in a complicated terrain","volume":"14","author":"Abu El-Magd","year":"2021","journal-title":"Earth Sci. Inform."},{"key":"10.1016\/j.asoc.2023.110591_b29","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.gsf.2020.09.004","article-title":"GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms","volume":"12","author":"Ali","year":"2021","journal-title":"Geosci. Front."},{"issue":"280","key":"10.1016\/j.asoc.2023.110591_b30","article-title":"Flash-flood potential mapping using deep learning, alternating decision trees and data provided by remote sensing sensors","volume":"21","author":"Costache","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.asoc.2023.110591_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijdrr.2022.103009","article-title":"Mass movement susceptibility assessment of alpine infrastructure in the Salzkammergut area, Austria","volume":"76","author":"Abad","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"10.1016\/j.asoc.2023.110591_b32","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geomorph.2019.04.006","article-title":"Sensitivity of rainstorm-triggered shallow mass movements on gully slopes to topographical factors on the Chinese Loess Plateau","volume":"337","author":"Guo","year":"2019","journal-title":"Geomorphology"},{"issue":"4475","key":"10.1016\/j.asoc.2023.110591_b33","article-title":"Debris-flow susceptibility assessment in China: A comparison between traditional statistical and machine learning methods","volume":"14","author":"Huang","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.asoc.2023.110591_b34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10064-021-02546-2","article-title":"Evaluation of debris flow and landslide hazards using ensemble framework of Bayesian-and tree-based models","volume":"81","author":"Pal","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"10.1016\/j.asoc.2023.110591_b35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-021-09747-8","article-title":"An integrated landslide susceptibility model to assess landslides along linear infrastructure for environmental management","volume":"80","author":"Sujatha","year":"2021","journal-title":"Environ. Earth Sci."},{"issue":"2","key":"10.1016\/j.asoc.2023.110591_b36","doi-asserted-by":"crossref","first-page":"294","DOI":"10.3390\/atmos13020294","article-title":"Evaluation of empirical atmospheric models using swarm-C satellite data","volume":"13","author":"Yin","year":"2022","journal-title":"Atmosphere"},{"key":"10.1016\/j.asoc.2023.110591_b37","doi-asserted-by":"crossref","DOI":"10.1071\/MF22167","article-title":"Remote sensing and geostatistics in urban water-resource monitoring: a review","author":"Liu","year":"2023","journal-title":"Mar. Freshwater Res."},{"key":"10.1016\/j.asoc.2023.110591_b38","series-title":"The Sustainability of Agro-Food and Natural Resource Systems in the Mediterranean Basin","first-page":"373","article-title":"Satellite technologies to support the sustainability of agricultural production","author":"D\u2019Antonio","year":"2015"},{"issue":"5437","key":"10.1016\/j.asoc.2023.110591_b39","article-title":"Tool for the establishment of agro-management zones using GIS techniques for precision farming in Egypt","volume":"14","author":"Elsharkawy","year":"2022","journal-title":"Sustainability"},{"key":"10.1016\/j.asoc.2023.110591_b40","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1007\/s11069-018-3326-8","article-title":"Spatio-temporal drought risk mapping approach and its application in the drought-prone region of south-east Queensland, Australia","volume":"93","author":"Dayal","year":"2018","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.asoc.2023.110591_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.116817","article-title":"Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales","volume":"291","author":"Gassar","year":"2021","journal-title":"Appl. Energy"},{"key":"10.1016\/j.asoc.2023.110591_b42","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1080\/09640568.2018.1427561","article-title":"Sensitivity and uncertainty analysis of agro-ecological modeling for saffron plant cultivation using GIS spatial decision-making methods","volume":"62","author":"Shokati","year":"2019","journal-title":"J. Environ. Plan. Manag."},{"key":"10.1016\/j.asoc.2023.110591_b43","first-page":"1","article-title":"Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridization with FAHP, XGBoost and deep learning neural network","author":"Costache","year":"2021","journal-title":"Geocarto Int."},{"issue":"401","key":"10.1016\/j.asoc.2023.110591_b44","article-title":"Hybrid machine learning approach for gully erosion mapping susceptibility at a watershed scale","volume":"11","author":"Hitouri","year":"2022","journal-title":"IJGI"},{"key":"10.1016\/j.asoc.2023.110591_b45","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s11069-021-04852-0","article-title":"Hazard zonation mapping of earthquake-induced secondary effects using spatial multi-criteria analysis","volume":"109","author":"Karpouza","year":"2021","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.asoc.2023.110591_b46","first-page":"1","article-title":"Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms","author":"Parvin","year":"2022","journal-title":"Theor. Appl. Climatol."},{"key":"10.1016\/j.asoc.2023.110591_b47","first-page":"1","article-title":"Flood vulnerability and buildings\u2019 flood exposure assessment in a densely urbanised city: Comparative analysis of three scenarios using a neural network approach","author":"Pham","year":"2022","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.asoc.2023.110591_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2021.148738","article-title":"Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India","volume":"794","author":"Tiwari","year":"2021","journal-title":"Sci. Total Environ."},{"issue":"315","key":"10.1016\/j.asoc.2023.110591_b49","article-title":"Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models","volume":"10","author":"Ahmad","year":"2021","journal-title":"ISPRS Int. J. Geo-Inf."},{"issue":"171","key":"10.1016\/j.asoc.2023.110591_b50","article-title":"Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models","volume":"9","author":"Chen","year":"2019","journal-title":"Appl. Sci."},{"key":"10.1016\/j.asoc.2023.110591_b51","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/6645007","article-title":"Evaluation of Shannon entropy and weights of evidence models in landslide susceptibility mapping for the Pithoragarh district of Uttarakhand state, India","author":"Dam","year":"2022","journal-title":"Adv. Civ. Eng."},{"issue":"364","key":"10.1016\/j.asoc.2023.110591_b52","article-title":"Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process","volume":"11","author":"Vojtek","year":"2019","journal-title":"Water"},{"key":"10.1016\/j.asoc.2023.110591_b53","first-page":"1","article-title":"An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India","author":"Ali","year":"2022","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.asoc.2023.110591_b54","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.scitotenv.2019.02.422","article-title":"Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods","volume":"668","author":"Bui","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.asoc.2023.110591_b55","article-title":"Flood susceptibility modelling using advanced ensemble machine learning models","volume":"12","author":"Islam","year":"2021","journal-title":"Geosci. Front."},{"key":"10.1016\/j.asoc.2023.110591_b56","article-title":"Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory","volume":"590","author":"Nachappa","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.asoc.2023.110591_b57","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.scitotenv.2019.02.436","article-title":"Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms","volume":"668","author":"Gayen","year":"2019","journal-title":"Sci. Total Environ."},{"issue":"1572","key":"10.1016\/j.asoc.2023.110591_b58","article-title":"Landslide and wildfire susceptibility assessment in southeast Asia using ensemble machine learning methods","volume":"13","author":"He","year":"2021","journal-title":"Remote Sens."},{"key":"10.1016\/j.asoc.2023.110591_b59","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2020.104805","article-title":"Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping","volume":"195","author":"Pham","year":"2020","journal-title":"Catena"},{"key":"10.1016\/j.asoc.2023.110591_b60","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1080\/19475705.2019.1710580","article-title":"A geospatial analysis of multi-hazard risk in Dharan, Nepal","volume":"11","author":"Aksha","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"issue":"2079","key":"10.1016\/j.asoc.2023.110591_b61","article-title":"Spatial predictions of debris flow susceptibility mapping using convolutional neural networks in Jilin Province, China","volume":"12","author":"Chen","year":"2020","journal-title":"Water"},{"issue":"1176","key":"10.1016\/j.asoc.2023.110591_b62","article-title":"Snow avalanche assessment in mass movement-prone areas: Results from climate extremization in relationship with environmental risk reduction in the Prati di Tivo area (Gran Sasso Massif, Central Italy)","volume":"10","author":"Fazzini","year":"2021","journal-title":"Land"},{"key":"10.1016\/j.asoc.2023.110591_b63","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1002\/ldr.3296","article-title":"Spatial variation in the frequency and magnitude of mass movement in a semiarid, complex-terrain agricultural watershed on the Loess Plateau of China","volume":"30","author":"Zhu","year":"2019","journal-title":"Land Degrad. Dev."},{"year":"2022","series-title":"Landslides","author":"World Health Organization\u00a0(WHO)","key":"10.1016\/j.asoc.2023.110591_b64"},{"year":"1993","series-title":"Essai de cartographie g\u00e9omorphologique et \u00e9tude des mouvements de terrain dans la vall\u00e9e de l\u2019oued el kbir:(Province de Tetouan; Rif Occidental: Maroc Septentrional)","author":"Kirat","key":"10.1016\/j.asoc.2023.110591_b65"},{"year":"2008","series-title":"G\u00e9ophysique, hydrog\u00e9ologie et cartographie de la vuln\u00e9rabilit\u00e9 et du risque de pollution de l\u2019aquif\u00e8re de Ghis-Nekor (Al Hoceima, Maroc)","author":"Salhi","key":"10.1016\/j.asoc.2023.110591_b66"},{"year":"1988","series-title":"Tectonique de la remont\u00e9e du manteau: les p\u00e9ridotites des Beni Bousera et leur enveloppe m\u00e9tamorphique, Rif Interne, Maroc","author":"Saddiqi","key":"10.1016\/j.asoc.2023.110591_b67"},{"year":"1994","series-title":"Petrologia y geoqu\u00edmica de los macizos ultram\u00e1ficos de Oj\u00e9n (andalucia) y de Beni Bouzera (Rif Septentrional, Marruecos)","author":"Targuisti","key":"10.1016\/j.asoc.2023.110591_b68"},{"key":"10.1016\/j.asoc.2023.110591_b69","article-title":"Age des gneiss du Hacho de Ceuta: Un \u00e9v\u00e9nement thermique Hercynien dans filezone interne du Rif","volume":"64","author":"Bernard-Griffith","year":"1977","journal-title":"RAST Rennes Abstr."},{"key":"10.1016\/j.asoc.2023.110591_b70","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jog.2015.07.002","article-title":"Polyphase deformation of the Dorsale Calcaire complex and the Maghrebian Flysch basin units in the Jebha area (Central Rif Morocco): New insights into the Miocene tectonic evolution of the Central Rif belt","volume":"90","author":"Vitale","year":"2015","journal-title":"J. Geodyn."},{"key":"10.1016\/j.asoc.2023.110591_b71","unstructured":"A. Chalouan, Les nappes Ghomarides (Rif Septentrional, Maroc), un terrain varisque dans la cha\u00eene alpine, (Ph.D. thesis), Strasbourg 1, 1986."},{"year":"1971","series-title":"Contribution \u00e0 l\u2019\u00e9tude p\u00e9trographique et structurale de la zone interne du Rif. \u00e9diteur non identifi\u00e9","author":"Kornprobst","key":"10.1016\/j.asoc.2023.110591_b72"},{"key":"10.1016\/j.asoc.2023.110591_b73","first-page":"1","article-title":"G\u00e9ologie de la dorsale calcaire entre t\u00e9touan et assifane (rif interne maroc)","volume":"233","author":"Nold","year":"1981","journal-title":"Notes et M\u00e9moires du Service g\u00e9ologique du Maroc"},{"key":"10.1016\/j.asoc.2023.110591_b74","first-page":"371","article-title":"La dorsale calcaire entre tetouan et assifane (Rif interne Maroc)","volume":"70","author":"Wildi","year":"1977","journal-title":"Eclogae Geol. Helv."},{"key":"10.1016\/j.asoc.2023.110591_b75","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.geobios.2006.04.005","article-title":"Tectono-sedimentary setting of the Oligocene-early Miocene deposits on the Betic-Rifian internal zone (Spain and Morocco)","volume":"40","author":"Serrano","year":"2007","journal-title":"Geobios"},{"key":"10.1016\/j.asoc.2023.110591_b76","first-page":"517","article-title":"Stratigraphy and provenance of Lower and Middle Miocene strata within the External Tanger Unit (Intrarif sub-domain External domain, Rif, Morocco): First evidence","volume":"56","author":"Zaghloul","year":"2005","journal-title":"Geol. Carpath."},{"year":"1961","series-title":"Donn\u00e9es actuelles sur la structure du Rif. En vente \u00e0 la soci\u00e9t\u00e9 g\u00e9ologique de France","author":"Durand-Delga","key":"10.1016\/j.asoc.2023.110591_b77"},{"year":"2007","series-title":"Les dolomies triasiques de la dorsale externe (Bokoya, Rif Interne, Maroc): Un complexe tectono-s\u00e9dimentaire du Rift oblique Alboran-Iberie","author":"Azzouz","key":"10.1016\/j.asoc.2023.110591_b78"},{"year":"1996","series-title":"Estratigrafia y evoluci\u00f3n paleogeogr\u00e1fica alpina del dominio gom\u00e1ride (Rif Interno, Marruecos)","author":"Maat\u00e9","key":"10.1016\/j.asoc.2023.110591_b79"},{"key":"10.1016\/j.asoc.2023.110591_b80","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0229153","article-title":"GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan","volume":"15","author":"Ullah","year":"2020","journal-title":"Plos One"},{"key":"10.1016\/j.asoc.2023.110591_b81","doi-asserted-by":"crossref","first-page":"4642","DOI":"10.1109\/JSTARS.2020.3014143","article-title":"Potential of ensemble learning to improve tree-based classifiers for landslide susceptibility mapping","volume":"13","author":"Song","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.asoc.2023.110591_b82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-60191-3","article-title":"Assessing and mapping multi-hazard risk susceptibility using a machine learning technique","volume":"10","author":"Pourghasemi","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2023.110591_b83","doi-asserted-by":"crossref","first-page":"4594","DOI":"10.1080\/10106049.2021.1892210","article-title":"Decision tree based ensemble machine learning approaches for landslide susceptibility mapping","volume":"37","author":"Arabameri","year":"2022","journal-title":"Geocarto Int."},{"key":"10.1016\/j.asoc.2023.110591_b84","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2019.135161","article-title":"Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method","volume":"711","author":"Hosseini","year":"2020","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.asoc.2023.110591_b85","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0224365","article-title":"Machine learning algorithm validation with a limited sample size","volume":"14","author":"Vabalas","year":"2019","journal-title":"PLoS One"},{"issue":"5659","key":"10.1016\/j.asoc.2023.110591_b86","article-title":"Performance evaluation of the GIS-based data-mining techniques decision tree, random forest, and rotation forest for landslide susceptibility modeling","volume":"11","author":"Park","year":"2019","journal-title":"Sustainability"},{"key":"10.1016\/j.asoc.2023.110591_b87","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1007\/s11269-020-02603-7","article-title":"Improvement of best first decision trees using bagging and dagging ensembles for flood probability mapping","volume":"34","author":"Yariyan","year":"2020","journal-title":"Water Resour. Manag."},{"key":"10.1016\/j.asoc.2023.110591_b88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catena.2007.01.003","article-title":"GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations","volume":"72","author":"Yalcin","year":"2008","journal-title":"Catena"},{"key":"10.1016\/j.asoc.2023.110591_b89","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10346-008-0117-4","article-title":"An implementation of rock engineering system for ranking the instability potential of natural slopes in Greek territory. An application in Karditsa county","volume":"5","author":"Rozos","year":"2008","journal-title":"Landslides"},{"key":"10.1016\/j.asoc.2023.110591_b90","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s12517-012-0807-z","article-title":"Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya","volume":"7","author":"Regmi","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"10.1016\/j.asoc.2023.110591_b91","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2019.124482","article-title":"Flood susceptibility mapping using convolutional neural network frameworks","volume":"582","author":"Wang","year":"2020","journal-title":"J. Hydrol."},{"issue":"8","key":"10.1016\/j.asoc.2023.110591_b92","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1080\/13658816.2020.1833016","article-title":"An augmented representation method of debris flow scenes to improve public perception","volume":"35","author":"Li","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.asoc.2023.110591_b93","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11069-015-1703-0","article-title":"Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: Advantages and limitations","volume":"79","author":"G\u00f3mez-Guti\u00e9rrez","year":"2015","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.asoc.2023.110591_b94","first-page":"71","article-title":"Lithology effects on gully erosion in Ghoori chay Watershed using RS & GIS","volume":"4","author":"Golestani","year":"2014","journal-title":"Int. J. Biosci."},{"key":"10.1016\/j.asoc.2023.110591_b95","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1080\/13658816.2020.1808897","article-title":"A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping","volume":"35","author":"Fang","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.asoc.2023.110591_b96","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-015-4950-1","article-title":"Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran","volume":"75","author":"Pourghasemi","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"10.1016\/j.asoc.2023.110591_b97","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1080\/19475705.2018.1505666","article-title":"Landslides susceptibility mapping using GIS and weights of evidence model in Tetouan-Ras-Mazari area (northern Morocco)","volume":"9","author":"Elmoulat","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"10.1016\/j.asoc.2023.110591_b98","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-03585-1","article-title":"Deep learning-based landslide susceptibility mapping","volume":"11","author":"Azarafza","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2023.110591_b99","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1126\/science.1178256","article-title":"Sustainable floodplains through large-scale reconnection to rivers","volume":"326","author":"Opperman","year":"2009","journal-title":"Science"},{"key":"10.1016\/j.asoc.2023.110591_b100","doi-asserted-by":"crossref","DOI":"10.1016\/j.uclim.2023.101562","article-title":"Risk assessment and zoning of flood disaster in Wuchengxiyu Region, China","volume":"49","author":"Gao","year":"2023","journal-title":"Urban Clim."},{"year":"2021","series-title":"A multi-hazard map-based flooding","author":"Pouyan","key":"10.1016\/j.asoc.2023.110591_b101"},{"key":"10.1016\/j.asoc.2023.110591_b102","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.scitotenv.2019.07.203","article-title":"Multi-hazard probability assessment and mapping in Iran","volume":"692","author":"Pourghasemi","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.asoc.2023.110591_b103","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.patrec.2018.11.004","article-title":"A new nested ensemble technique for automated diagnosis of breast cancer","volume":"132","author":"Abdar","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.asoc.2023.110591_b104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-021-08869-4","article-title":"Experimental investigation and prediction of strength development of GGBFS-LFS-and SCBA-based green concrete using soft computing techniques","volume":"14","author":"Rani","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"10.1016\/j.asoc.2023.110591_b105","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1080\/19475705.2021.1914753","article-title":"Spatial prediction of shallow landslide: Application of novel rotational forest-based reduced error pruning tree","volume":"12","author":"Arabameri","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"10.1016\/j.asoc.2023.110591_b106","first-page":"1","article-title":"Integrating the Particle Swarm Optimization (PSO) with machine learning methods for improving the accuracy of the landslide susceptibility model","author":"Saha","year":"2022","journal-title":"Earth Sci. Inform."},{"key":"10.1016\/j.asoc.2023.110591_b107","doi-asserted-by":"crossref","DOI":"10.1016\/j.gsf.2020.10.007","article-title":"Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm","volume":"12","author":"Shahabi","year":"2021","journal-title":"Geosci. Front."},{"key":"10.1016\/j.asoc.2023.110591_b108","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2022.127963","article-title":"Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach","author":"Khosravi","year":"2022","journal-title":"J. Hydrol."},{"key":"10.1016\/j.asoc.2023.110591_b109","first-page":"240","article-title":"Feature based classification of voice based biometric data through machine learning algorithm","volume":"51","author":"Shakil","year":"2022","journal-title":"Mater. Today: Proc."},{"key":"10.1016\/j.asoc.2023.110591_b110","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TIFS.2011.2175919","article-title":"Ensemble classifiers for steganalysis of digital media","volume":"7","author":"Kodovsky","year":"2011","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"1683","key":"10.1016\/j.asoc.2023.110591_b111","article-title":"Coastal wetland mapping using ensemble learning algorithms: A comparative study of bagging, boosting and stacking techniques","volume":"12","author":"Wen","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.asoc.2023.110591_b112","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.jrmge.2020.05.011","article-title":"Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data","volume":"13","author":"Kardani","year":"2021","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"10.1016\/j.asoc.2023.110591_b113","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0146116","article-title":"Heterogeneous ensemble combination search using genetic algorithm for class imbalanced data classification","volume":"11","author":"Haque","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.asoc.2023.110591_b114","article-title":"Enhanced bagging (eBagging): A novel approach for ensemble learning","volume":"17","author":"T\u00fcys\u00fczo\u011flu","year":"2020","journal-title":"Int. Arab J. Inf. Technol."},{"key":"10.1016\/j.asoc.2023.110591_b115","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10346-019-01286-5","article-title":"Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan","volume":"17","author":"Dou","year":"2020","journal-title":"Landslides"},{"key":"10.1016\/j.asoc.2023.110591_b116","doi-asserted-by":"crossref","first-page":"5201","DOI":"10.1080\/10106049.2021.1912195","article-title":"Spatial prediction of landslides along National Highway-6, Hoa Binh province, Vietnam using novel hybrid models","volume":"37","author":"Hang","year":"2022","journal-title":"Geocarto Int."},{"key":"10.1016\/j.asoc.2023.110591_b117","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.acalib.2019.02.013","article-title":"Application of adaptive boosting (AdaBoost) in demand-driven acquisition (DDA) prediction: A machine-learning approach","volume":"45","author":"Walker","year":"2019","journal-title":"J. Acad. Librariansh."},{"year":"2019","series-title":"Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification","author":"Yaman","key":"10.1016\/j.asoc.2023.110591_b118"},{"key":"10.1016\/j.asoc.2023.110591_b119","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00349-y","article-title":"Boosting methods for multi-class imbalanced data classification: An experimental review","volume":"7","author":"Tanha","year":"2020","journal-title":"J. Big Data"},{"key":"10.1016\/j.asoc.2023.110591_b120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-022-10620-5","article-title":"Feature elimination and comparison of machine learning algorithms in landslide susceptibility mapping","volume":"81","author":"Jennifer","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"10.1016\/j.asoc.2023.110591_b121","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1080\/19475705.2020.1745902","article-title":"Evaluation of tree-base data mining algorithms in land used\/land cover mapping in a semi-arid environment through Landsat 8 OLI image; Shiraz, Iran","volume":"11","author":"Moayedi","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"10.1016\/j.asoc.2023.110591_b122","doi-asserted-by":"crossref","first-page":"28","DOI":"10.4314\/ijest.v14i1.4","article-title":"Design of software-oriented technician for vehicle\u2019s fault system prediction using AdaBoost and random forest classifiers","volume":"14","author":"Thomas","year":"2022","journal-title":"Int. J. Eng. Sci. Technol."},{"key":"10.1016\/j.asoc.2023.110591_b123","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-022-10389-7","article-title":"Application of an ensemble learning model based on random subspace and a J48 decision tree for landslide susceptibility mapping: A case study for Qingchuan, Sichuan, China","volume":"81","author":"Li","year":"2022","journal-title":"Environ. Earth Sci."},{"issue":"1995","key":"10.1016\/j.asoc.2023.110591_b124","article-title":"Susceptibility mapping of soil water erosion using machine learning models","volume":"12","author":"Mosavi","year":"2020","journal-title":"Water"},{"key":"10.1016\/j.asoc.2023.110591_b125","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1007\/s13042-020-01138-y","article-title":"Human posture recognition based on multiple features and rule learning","volume":"11","author":"Ding","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.asoc.2023.110591_b126","doi-asserted-by":"crossref","first-page":"2116","DOI":"10.1080\/02626667.2020.1754419","article-title":"Monthly suspended sediment load prediction using artificial intelligence: Testing of a new random subspace method","volume":"65","author":"Nhu","year":"2020","journal-title":"Hydrol. Sci. J."},{"key":"10.1016\/j.asoc.2023.110591_b127","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1007\/s10064-021-02275-6","article-title":"Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace\u2013based na\u00efve Bayes tree in Zigui County of the Three Gorges Reservoir Area","volume":"80","author":"Hu","year":"2021","journal-title":"Bull. Eng. Geol. Environ."},{"key":"10.1016\/j.asoc.2023.110591_b128","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2021.126266","article-title":"Ensemble machine learning paradigms in hydrology: A review","volume":"598","author":"Zounemat-Kermani","year":"2021","journal-title":"J. Hydrol."},{"key":"10.1016\/j.asoc.2023.110591_b129","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1016\/j.jhydrol.2019.05.089","article-title":"Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles","volume":"575","author":"Chen","year":"2019","journal-title":"J. Hydrol."},{"issue":"1373","key":"10.1016\/j.asoc.2023.110591_b130","article-title":"A new modeling approach for spatial prediction of flash flood with biogeography optimized CHAID tree ensemble and remote sensing data","volume":"12","author":"Nguyen","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.asoc.2023.110591_b131","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-51941-z","article-title":"Coupling logistic model tree and random subspace to predict the landslide susceptibility areas with considering the uncertainty of environmental features","volume":"9","author":"Luo","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2023.110591_b132","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolind.2020.106825","article-title":"Modeling livelihood vulnerability in erosion and flooding induced river island in Ganges riparian corridor, India","volume":"119","author":"Singha","year":"2020","journal-title":"Ecol. Indic."},{"key":"10.1016\/j.asoc.2023.110591_b133","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.jmir.2019.11.001","article-title":"Machine learning methods for computer-aided breast cancer diagnosis using histopathology: A narrative review","volume":"51","author":"Saxena","year":"2020","journal-title":"J. Med. Imaging Radiat. Sci."},{"issue":"3281","key":"10.1016\/j.asoc.2023.110591_b134","article-title":"Landslide susceptibility modeling: An integrated novel method based on machine learning feature transformation","volume":"13","author":"Al-Najjar","year":"2021","journal-title":"Remote Sens."},{"issue":"11539","key":"10.1016\/j.asoc.2023.110591_b135","article-title":"Deep learning approaches for detection of breast adenocarcinoma causing carcinogenic mutations","volume":"23","author":"Shah","year":"2022","journal-title":"Int. J. Mol. Sci."},{"issue":"6330","key":"10.1016\/j.asoc.2023.110591_b136","article-title":"Application of tree-based ensemble models to landslide susceptibility mapping: A comparative study","volume":"14","author":"Wei","year":"2022","journal-title":"Sustainability"},{"key":"10.1016\/j.asoc.2023.110591_b137","doi-asserted-by":"crossref","first-page":"44","DOI":"10.18520\/cs\/v121\/i1\/44-55","article-title":"Multi-hazard analysis and design guidelines: Recommendations for structure and infrastructure systems in the Indian context","volume":"121","author":"Roy","year":"2021","journal-title":"Curr. Sci."},{"key":"10.1016\/j.asoc.2023.110591_b138","series-title":"IOP Conference Series: Earth and Environmental Science","first-page":"012054","article-title":"Green and blue infrastructure as a tool to support decision-making in the spatial planning process. the case of Lake Trichonida, Greece","author":"Tasopoulou","year":"2021"},{"issue":"89","key":"10.1016\/j.asoc.2023.110591_b139","article-title":"Green may be nice, but infrastructure is necessary","volume":"11","author":"Mertens","year":"2022","journal-title":"Land"},{"key":"10.1016\/j.asoc.2023.110591_b140","series-title":"Handbook of Sustainable Development Through Green Engineering and Technology","first-page":"119","article-title":"Towards urban sustainability: Impact of blue and Green infrastructure on building smart, climate resilient and livable cities","author":"Roy","year":"2022"},{"key":"10.1016\/j.asoc.2023.110591_b141","first-page":"1","article-title":"Comparison between common statistical modeling techniques used in research, including: Discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest","volume":"9","author":"Abdulhafedh","year":"2022","journal-title":"Open Access Libr. J."},{"key":"10.1016\/j.asoc.2023.110591_b142","first-page":"190","article-title":"A review of the logistic regression model with emphasis on medical research","volume":"7","author":"Boateng","year":"2019","journal-title":"J. Data Anal. Inf. Process."},{"issue":"9","key":"10.1016\/j.asoc.2023.110591_b143","article-title":"Random forest vs logistic regression: Binary classification for heterogeneous datasets","volume":"1","author":"Kirasich","year":"2018","journal-title":"SMU Data Sci. Rev."},{"key":"10.1016\/j.asoc.2023.110591_b144","doi-asserted-by":"crossref","DOI":"10.1016\/j.compedu.2019.103676","article-title":"An overview and comparison of supervised data mining techniques for student exam performance prediction","volume":"143","author":"Tomasevic","year":"2020","journal-title":"Comput. Educ."},{"key":"10.1016\/j.asoc.2023.110591_b145","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1007\/s12553-020-00446-1","article-title":"Machine learning prediction of susceptibility to visceral fat associated diseases","volume":"10","author":"Aldraimli","year":"2020","journal-title":"Health Technol."},{"key":"10.1016\/j.asoc.2023.110591_b146","doi-asserted-by":"crossref","DOI":"10.1016\/j.jvolgeores.2020.107009","article-title":"Exploring the unsupervised classification of seismic events of Cotopaxi volcano","volume":"403","author":"Duque","year":"2020","journal-title":"J. Volcanol. Geotherm. Res."},{"key":"10.1016\/j.asoc.2023.110591_b147","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1007\/s11227-018-2326-5","article-title":"A dimensionality reduction-based efficient software fault prediction using Fisher linear discriminant analysis (FLDA)","volume":"74","author":"Kalsoom","year":"2018","journal-title":"J. Supercomput."},{"key":"10.1016\/j.asoc.2023.110591_b148","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.108185","article-title":"Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors","volume":"219","author":"Fan","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.asoc.2023.110591_b149","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13040-021-00244-z","article-title":"The matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation","volume":"14","author":"Chicco","year":"2021","journal-title":"BioData Min."},{"key":"10.1016\/j.asoc.2023.110591_b150","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1017\/jog.2021.18","article-title":"Future glacial lakes in High Mountain Asia: An inventory and assessment of hazard potential from surrounding slopes","volume":"67","author":"Furian","year":"2021","journal-title":"J. Glaciol."},{"key":"10.1016\/j.asoc.2023.110591_b151","doi-asserted-by":"crossref","DOI":"10.1016\/j.earscirev.2022.104125","article-title":"Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory","author":"Loche","year":"2022","journal-title":"Earth Sci. Rev."},{"key":"10.1016\/j.asoc.2023.110591_b152","doi-asserted-by":"crossref","DOI":"10.1016\/j.soildyn.2023.108088","article-title":"Seismic fragility and demand hazard analyses for earth slopes incorporating soil property variability","volume":"173","author":"Wang","year":"2023","journal-title":"Soil Dyn. Earthq. Eng."},{"year":"2019","series-title":"Distribution of Landslides Reconstructed from Inventory Data and Estimation of Landslide Susceptibility in Hungary","author":"J\u00f3zsa","key":"10.1016\/j.asoc.2023.110591_b153"},{"key":"10.1016\/j.asoc.2023.110591_b154","doi-asserted-by":"crossref","DOI":"10.1016\/j.gsf.2021.101248","article-title":"National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data","volume":"12","author":"Lin","year":"2021","journal-title":"Geosci. Front."},{"key":"10.1016\/j.asoc.2023.110591_b155","doi-asserted-by":"crossref","DOI":"10.33494\/nzjfs522022x226x","article-title":"Effectiveness of vegetative mitigation strategies in the restoration of fluvial and fluvio-mass movement gully complexes over 60 years, East Coast region, North island, New Zealand","volume":"52","author":"Marden","year":"2022","journal-title":"New Zealand J. For. Sci."},{"key":"10.1016\/j.asoc.2023.110591_b156","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43247-022-00568-6","article-title":"Permafrost degradation increases risk and large future costs of infrastructure on the Third Pole","volume":"3","author":"Ran","year":"2022","journal-title":"Commun. Earth Environ."},{"issue":"2895","key":"10.1016\/j.asoc.2023.110591_b157","article-title":"Inventory of historical and recent earthquake-triggered landslides and assessment of related susceptibility by GIS-based analytic hierarchy process: The case of Cephalonia (Ionian Islands, Western Greece)","volume":"12","author":"Mavroulis","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.asoc.2023.110591_b158","doi-asserted-by":"crossref","first-page":"2899","DOI":"10.5194\/essd-12-2899-2020","article-title":"Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis","volume":"12","author":"Hao","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"10.1016\/j.asoc.2023.110591_b159","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-26964-8","article-title":"30-Year record of Himalaya mass-wasting reveals landscape perturbations by extreme events","volume":"12","author":"Jones","year":"2021","journal-title":"Nature Commun."},{"issue":"2301","key":"10.1016\/j.asoc.2023.110591_b160","article-title":"Multi-temporal satellite image composites in Google Earth engine for improved landslide visibility: A case study of a glacial landscape","volume":"14","author":"Lindsay","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.asoc.2023.110591_b161","doi-asserted-by":"crossref","first-page":"6166","DOI":"10.1109\/JSTARS.2020.3028855","article-title":"A new deep-learning-based approach for earthquake-triggered landslide detection from single-temporal RapidEye satellite imagery","volume":"13","author":"Yi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"10.1016\/j.asoc.2023.110591_b162","series-title":"MATEC Web of Conferences","first-page":"02042","article-title":"Weights of evidence method for landslide susceptibility mapping in Tangier, Morocco","author":"Bousta","year":"2018"},{"key":"10.1016\/j.asoc.2023.110591_b163","first-page":"1","article-title":"Landslide susceptibility mapping using GIS-based bivariate models in the Rif chain (northernmost Morocco)","author":"Es-Smairi","year":"2022","journal-title":"Geocarto Int."},{"key":"10.1016\/j.asoc.2023.110591_b164","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.geomorph.2019.02.014","article-title":"Hydro-geomorphological consequences of the abandonment of agricultural terraces in the Mediterranean region: Key controlling factors and landscape stability patterns","volume":"333","author":"Moreno-de-las Heras","year":"2019","journal-title":"Geomorphology"},{"key":"10.1016\/j.asoc.2023.110591_b165","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-019-4892-0","article-title":"Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN)","volume":"12","author":"Harmouzi","year":"2019","journal-title":"Arab. J. Geosci."},{"key":"10.1016\/j.asoc.2023.110591_b166","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10064-019-01548-5","article-title":"On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: The central part of the Rif Mountains (Morocco)","volume":"79","author":"Ozer","year":"2020","journal-title":"Bull. Eng. Geol. Environ."},{"key":"10.1016\/j.asoc.2023.110591_b167","doi-asserted-by":"crossref","DOI":"10.1029\/2020JE006475","article-title":"Global patterns of recent mass movement on asteroid (101955) Bennu","volume":"125","author":"Jawin","year":"2020","journal-title":"J. Geophys. Res. Planets"},{"key":"10.1016\/j.asoc.2023.110591_b168","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.enggeo.2018.11.010","article-title":"Analysing post-earthquake mass movement volume dynamics with multi-source DEMs","volume":"248","author":"Tang","year":"2019","journal-title":"Eng. Geol."},{"key":"10.1016\/j.asoc.2023.110591_b169","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s11069-021-05199-2","article-title":"Could road constructions be more hazardous than an earthquake in terms of mass movement?","volume":"112","author":"Tanya\u015f","year":"2022","journal-title":"Nat. Hazards"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494623006099?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494623006099?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:24:53Z","timestamp":1761596693000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494623006099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":169,"alternative-id":["S1568494623006099"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2023.110591","relation":{},"ISSN":["1568-4946"],"issn-type":[{"type":"print","value":"1568-4946"}],"subject":[],"published":{"date-parts":[[2023,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Mass movement susceptibility prediction and infrastructural risk assessment (IRA) using GIS-based Meta classification algorithms","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2023.110591","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"110591"}}