{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T11:36:13Z","timestamp":1768822573160,"version":"3.49.0"},"reference-count":125,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","doi-asserted-by":"publisher","award":["CUGCJ1822"],"award-info":[{"award-number":["CUGCJ1822"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability based on susceptibility to various geohazards. The method was applied to the Pearl River Delta in China, resulting in the classification of areas into high vulnerability (5961.85 km2), medium vulnerability (19,227.93 km2), low vulnerability (14,892.02 km2), and stable areas (1616.19 km2). The findings demonstrate improved accuracy and reliability compared to using AHP alone. ROC curve analysis confirms the enhanced performance of the integrated method, highlighting its effectiveness in discerning susceptibility levels and making informed decisions in hazard preparedness and risk reduction. Additionally, this study assessed the risks posed by geohazards to critical infrastructures, roads, and artificial surfaces, while discussing prevention strategies. However, this study acknowledges certain limitations, including the subjective determination of its judgment matrix and data constraints. Future research could explore the integration of alternative methods to enhance the objectivity of factor weighting. In practical applications, this study contributes to the understanding of geo-environment vulnerability assessments, providing insight into the intricate interplay among geological processes, human activities, and disaster resilience.<\/jats:p>","DOI":"10.3390\/rs15205007","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T10:36:56Z","timestamp":1697625416000},"page":"5007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Peng","family":"Huang","sequence":"first","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430000, China"}]},{"given":"Xiaoyu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430000, China"}]},{"given":"Chuanming","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430000, China"}]},{"given":"Aiguo","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Environmental Studies, China University of Geosciences, Wuhan 430000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.15244\/pjoes\/69100","article-title":"Research on the Geological Disaster Forecast and Early Warning Model Based on the Optimal Combination Weighing Law and Extension Method: A Case Study in China","volume":"26","author":"Zhang","year":"2017","journal-title":"Polish J. Environ. Stud."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.rse.2005.08.004","article-title":"Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments","volume":"98","author":"Metternicht","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100432","DOI":"10.1016\/j.spasta.2020.100432","article-title":"Modeling big spatio-temporal geo-hazards data for forecasting by error-correction cointegration and dimension-reduction","volume":"36","author":"Wang","year":"2020","journal-title":"Spatial Stat."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yanar, T., Kocaman, S., and Gokceoglu, C. (2020). Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey). ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020114"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"138850","DOI":"10.1016\/j.scitotenv.2020.138850","article-title":"Species vulnerability under climate change: Study of two sea urchins at their distribution margin","volume":"728","author":"Detree","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1111\/j.1466-8238.2010.00558.x","article-title":"Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change","volume":"19","author":"Gonzalez","year":"2010","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1016\/j.jenvman.2017.11.059","article-title":"Ecological vulnerability assessment for ecological conservation and environmental management","volume":"206","author":"He","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1038\/s41559-020-1114-9","article-title":"Developmental cost theory predicts thermal environment and vulnerability to global warming","volume":"4","author":"Marshall","year":"2020","journal-title":"Nat. Ecol. Evol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.gloenvcha.2013.12.007","article-title":"A linked vulnerability and resilience framework for adaptation pathways in remote disadvantaged communities","volume":"28","author":"Maru","year":"2014","journal-title":"Glob. Environ. Chang."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.jenvman.2016.10.057","article-title":"A versatile method for groundwater vulnerability projections in future scenarios","volume":"187","author":"Stevenazzi","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1080\/10106049.2018.1533594","article-title":"Wetland habitat vulnerability of lower Punarbhaba river basin of the uplifted Barind region of Indo-Bangladesh","volume":"35","author":"Talukdar","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yin, L.Z., Zhu, J., Li, W.S., and Wang, J.H. (2022). Vulnerability Analysis of Geographical Railway Network under Geological Hazard in China. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11060342"},{"key":"ref_13","unstructured":"Margat, J. (1968). Vulnerability of Groundwater to Pollution, BRGM."},{"key":"ref_14","unstructured":"Timmerman, P. (1981). Vulnerability, Resilience and the Collapse of Society: A Review of Models and Possible Climatic Application, Institute for Environmental Studies."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/A:1009652531101","article-title":"The Science of Adaptation: A Framework for Assessment","volume":"4","author":"Smit","year":"1999","journal-title":"Mitig. Adapt. Strateg. Glob. Chang."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, Y.Z. (2010). Reserch on the Vulnerability of Geological Environment and Its Countermeasures in Lijiang. [Ph.D. Thesis, Kunming University of Science and Technology]. (In Chinese).","DOI":"10.1109\/ICECENG.2011.6057646"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s12665-019-8077-7","article-title":"The vulnerability evaluation of regional geo-environment: A case study in Beihai City, China","volume":"78","author":"Ma","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s11852-010-0136-x","article-title":"GIS and remote sensing as tools for conducting geo-hazards risk assessment along Gulf of Aqaba coastal zone, Egypt","volume":"15","author":"Arnous","year":"2011","journal-title":"J. Coast. Conserv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.gsf.2019.10.008","article-title":"Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?","volume":"11","author":"Pourghasemi","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1080\/19475705.2019.1701571","article-title":"Geo-environment risk assessment in Zhengzhou City, China","volume":"11","author":"Ma","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103128","DOI":"10.1016\/j.ijdrr.2022.103128","article-title":"Risk assessment of multi-disaster in Mining Area of Guizhou, China","volume":"78","author":"Chang","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110364","DOI":"10.1016\/j.ecolind.2023.110364","article-title":"Vulnerability assessment of the eco-geo-environment of mining cities in arid and semi-arid areas: A case study from Zhungeer, China","volume":"152","author":"Li","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_23","first-page":"1003","article-title":"Comparative analysis on classification methods of geological disaster susceptibility assessment","volume":"45","author":"Jie","year":"2021","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth-Sci. Rev."},{"key":"ref_25","unstructured":"Wei, H., Pierre-Yves, H., Xu, Q., Theo, V., and Wang, G.H. (June, January 29). Experimental Study of Fluidized Landslide. Proceedings of the 4th World Landslide Forum, Ljubljana, Slovenia."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40677-020-00152-0","article-title":"Landslide susceptibility evaluation and hazard zonation techniques\u2014A review","volume":"7","author":"Shano","year":"2020","journal-title":"Geoenviron. Disasters"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1007\/s42452-019-1499-8","article-title":"Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India","volume":"1","author":"Das","year":"2019","journal-title":"SN Appl. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.enggeo.2013.02.009","article-title":"Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area","volume":"158","author":"Bathrellos","year":"2013","journal-title":"Eng. Geol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kontoes, C., Loupasakis, C., Papoutsis, I., Alatza, S., Poyiadji, E., Ganas, A., Psychogyiou, C., Kaskara, M., Antoniadi, S., and Spanou, N. (2021). Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WoE Methods, with Remote Sensing and Ground Truth Data. Land, 10.","DOI":"10.3390\/land10040402"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y.M., Deng, X.L., Ji, P.K., Yang, Y.M., Jiang, W.X., and Zhao, Z.F. (2022). Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph192114248"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15548","DOI":"10.1080\/10106049.2022.2102216","article-title":"A random forest model of karst ground collapse susceptibility based on factor and parameter coupling optimization","volume":"37","author":"Wang","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18067","DOI":"10.1080\/10106049.2022.2136265","article-title":"Land subsidence susceptibility assessment using advanced artificial intelligence models","volume":"37","author":"Yu","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2248069","DOI":"10.1080\/10106049.2023.2248069","article-title":"Evaluation of Jining mining subsidence susceptibility based on three multiple-criteria decision analysis methods","volume":"38","author":"Cui","year":"2023","journal-title":"Geocarto Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.jafrearsci.2014.05.004","article-title":"Slope stability susceptibility evaluation parameter (SSEP) rating scheme\u2014An approach for landslide hazard zonation","volume":"99","author":"Raghuvanshi","year":"2014","journal-title":"J. Afr. Earth Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104855","DOI":"10.1016\/j.cageo.2021.104855","article-title":"A comparative study of machine learning and Fuzzy-AHP technique to groundwater potential mapping in the data-scarce region","volume":"155","author":"Kumar","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13023-016-0555-3","article-title":"Multi-criteria decision analysis (MCDA): Testing a proposed MCDA framework for orphan drugs","volume":"12","author":"Schey","year":"2017","journal-title":"Orphanet J. Rare Dis."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s12205-009-0359-2","article-title":"The spatial MCDA approach for evaluating flood damage reduction alternatives","volume":"13","author":"Lim","year":"2009","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1007\/s40808-020-00881-z","article-title":"Comparing potential risk of soil erosion using RUSLE and MCDA techniques in Central Ethiopia","volume":"7","author":"Tadesse","year":"2021","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_39","first-page":"5","article-title":"Multicriteria Decision Analysis (Mcda) Methods in Life Cycle Assessment (Lca). A Comparison of Private Passenger Vehicles","volume":"28","author":"Maciol","year":"2018","journal-title":"Oper. Res. Decis."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1111\/j.1400-0952.2004.01068.x","article-title":"Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran","volume":"51","author":"Tangestani","year":"2004","journal-title":"Aust. J. Earth Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"31981","DOI":"10.1007\/s11356-019-06355-9","article-title":"A novel approach for assessing watershed susceptibility using weighted overlay and analytical hierarchy process (AHP) methodology: A case study in Eagle Creek Watershed, USA","volume":"26","author":"Jabbar","year":"2019","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Saaty, T.L. (1980). The Analytic Hierarchy Process, McGraw-Hill.","DOI":"10.21236\/ADA214804"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1287\/opre.2013.1197","article-title":"The Modern Science of Multicriteria Decision Making and Its Practical Applications: The AHP\/ANP Approach","volume":"61","author":"Saaty","year":"2013","journal-title":"Oper. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1007\/s10668-019-00406-4","article-title":"A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India","volume":"22","author":"Basu","year":"2020","journal-title":"Environ. Dev. Sustain."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, W., Han, H.X., Huang, B., Huang, Q.L., and Fu, X.D. (2017). Variable-Weighted Linear Combination Model for Landslide Susceptibility Mapping: Case Study in the Shennongjia Forestry District, China. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110347"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1080\/13658816.2017.1283505","article-title":"Modeling urban expansion by using variable weights logistic cellular automata: A case study of Nanjing, China","volume":"31","author":"Shu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1007\/s10040-017-1614-0","article-title":"Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory","volume":"25","author":"Wu","year":"2017","journal-title":"Hydrogeol. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"S97","DOI":"10.1007\/s11069-015-1931-3","article-title":"China\u2019s regional social vulnerability to geological disasters: Evaluation and spatial characteristics analysis","volume":"84","author":"Hou","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s11069-014-1585-6","article-title":"Chinese karst geology and measures to prevent geohazards during shield tunnelling in karst region with caves","volume":"77","author":"Cui","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Du, Y.N., Feng, G.C., Liu, L., Fu, H.Q., Peng, X., and Wen, D.B. (2020). Understanding Land Subsidence Along the Coastal Areas of Guangdong, China, by Analyzing Multi-Track MTInSAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12020299"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.1080\/19475705.2019.1680450","article-title":"Integrated assessment of ecological risk for multi-hazards in Guangdong province in southeastern China","volume":"10","author":"Liu","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1080\/13504500709469742","article-title":"Characteristics of geological hazards in South China coastal areas and impact on regional sustainable development","volume":"14","author":"Zhu","year":"2007","journal-title":"Int. J. Sustain. Dev. World Ecol."},{"key":"ref_53","unstructured":"GPDPRYEC (2010). Guangdong Province Disaster Prevention and Reduction Yearbook, South China University of Technology Press. (In Chinese)."},{"key":"ref_54","unstructured":"Zeng, M., and Liu, F.M. (2016). AER\u2014Advances in Engineering Research, 4th International Conference on Sustainable Energy and Environmental Engineering (ICSEEE), Shenzhen, China, 20\u201321 December 2015, Atlantis Press."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4239","DOI":"10.1109\/JSTARS.2019.2938554","article-title":"Integration of Analytical Hierarchy Process and Landslide Susceptibility Index Based Landslide Susceptibility Assessment of the Pearl River Delta Area, China","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","unstructured":"Dou, J., Zheng, X.Z., Qian, J.P., Liu, R.H., and Wu, Q.T. (2008). Geoinformatics 2008 and Joint Conference on GIS and Built Environment\u2014Advanced Spatial Data Models and Analyses, SPIE."},{"key":"ref_57","first-page":"103228","article-title":"Land subsidence modeling and assessment in the West Pearl River Delta from combined InSAR time series, land use and geological data","volume":"118","author":"Liu","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.2166\/hydro.2019.073","article-title":"Modeling saltwater intrusion using an integrated Bayesian model averaging method in the Pearl River Delta","volume":"21","author":"Lin","year":"2019","journal-title":"J. Hydroinf."},{"key":"ref_59","unstructured":"(2023, July 04). Geospatial Data Cloud. (In Chinese)."},{"key":"ref_60","unstructured":"(2023, July 04). Soil Science Database. (In Chinese)."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1080\/19475705.2020.1734101","article-title":"Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks","volume":"11","author":"Li","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Psomiadis, E., Papazachariou, A., Soulis, K.X., Alexiou, D.S., and Charalampopoulos, I. (2020). Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data. Land, 9.","DOI":"10.3390\/land9050133"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2248082","DOI":"10.1080\/10106049.2023.2248082","article-title":"Landslide susceptibility mapping in Badakhshan province, Afghanistan: A comparative study of machine learning algorithms","volume":"38","author":"Qasimi","year":"2023","journal-title":"Geocarto Int."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2227324","DOI":"10.1080\/19475705.2023.2227324","article-title":"Multivariate statistical algorithms for landslide susceptibility assessment in Kailash Sacred landscape, Western Himalaya","volume":"14","author":"Pandey","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2210255","DOI":"10.1080\/19475705.2023.2210255","article-title":"An integrated approach based landslide susceptibility mapping: Case of Muzaffarabad region, Pakistan","volume":"14","author":"Basharat","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/20964471.2018.1472392","article-title":"Mapping landslide susceptibility and types using Random Forest","volume":"2","author":"Taalab","year":"2018","journal-title":"Big Earth Data"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s40808-019-00683-y","article-title":"Collapse dolines susceptibility mapping using frequency ratio method and GIS in Sahel-Doukkala, Morocco","volume":"6","author":"Bouzerda","year":"2020","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s10346-014-0493-x","article-title":"Debris-flow susceptibility assessment at regional scale: Validation on an alpine environment","volume":"12","author":"Bregoli","year":"2015","journal-title":"Landslides"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.geomorph.2017.03.025","article-title":"Improving transferability strategies for debris flow susceptibility assessment: Application to the Saponara and Itala catchments (Messina, Italy)","volume":"288","author":"Cama","year":"2017","journal-title":"Geomorphology"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geomorph.2018.01.025","article-title":"Debris flow susceptibility assessment based on an empirical approach in the central region of South Korea","volume":"308","author":"Kang","year":"2018","journal-title":"Geomorphology"},{"key":"ref_71","first-page":"743","article-title":"Evaluating Susceptibility of Debris Flow Hazard using Multivariate Statistical Analysis in Hualien County","volume":"5","author":"Shen","year":"2012","journal-title":"Disaster Adv."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1080\/19475705.2019.1604572","article-title":"Mapping debris flow susceptibility based on watershed unit and grid cell unit: A comparison study","volume":"10","author":"Qin","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"953","DOI":"10.28991\/cej-2021-03091702","article-title":"Nasrullah Susceptibility Assessment of Single Gully Debris Flow Based on AHP and Extension Method","volume":"7","author":"Mehmood","year":"2021","journal-title":"Civil Eng. J. Tehran"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1007\/s11069-023-06099-3","article-title":"Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization","volume":"118","author":"Li","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"104669","DOI":"10.1016\/j.tust.2022.104669","article-title":"Risk assessment of ground collapse along tunnels in karst terrain by using an improved extension evaluation method","volume":"129","author":"Zhang","year":"2022","journal-title":"Tunnell. Underground Space Technol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Xie, Y.H., Zhang, B.H., Liu, Y.X., Liu, B.C., Zhang, C.F., and Lin, Y.S. (2022). Evaluation of the Karst Collapse Susceptibility of Subgrade Based on the AHP Method of ArcGIS and Prevention Measures: A Case Study of the Quannan Expressway, Section K1379+300-K1471+920. Water, 14.","DOI":"10.3390\/w14091432"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"04020035","DOI":"10.1061\/(ASCE)NH.1527-6996.0000404","article-title":"Development of Sinkhole Susceptibility Map of East Central Florida","volume":"21","author":"Kim","year":"2020","journal-title":"Nat. Hazards Rev."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s12665-013-2422-z","article-title":"Radar interferometry techniques for the study of ground subsidence phenomena: A review of practical issues through cases in Spain","volume":"71","author":"Tomas","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1109\/JSTARS.2015.2428615","article-title":"Integration of InSAR Analysis and Numerical Modeling for the Assessment of Ground Subsidence in the City of Lisbon, Portugal","volume":"9","author":"Catalao","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Bianchini, S., Solari, L., Del Soldato, M., Raspini, F., Montalti, R., Ciampalini, A., and Casagli, N. (2019). Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic. Remote Sens., 11.","DOI":"10.3390\/rs11172015"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s00267-011-9766-5","article-title":"Spatial Prediction of Ground Subsidence Susceptibility Using an Artificial Neural Network","volume":"49","author":"Lee","year":"2012","journal-title":"Environ. Manage."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1007\/s11069-023-06058-y","article-title":"Investigation of land-subsidence phenomenon and aquifer vulnerability using machine models and GIS technique","volume":"118","author":"Ghasemi","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"26580","DOI":"10.1007\/s11356-022-24065-7","article-title":"Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence","volume":"30","author":"Mohammadifar","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1007\/s12665-012-1634-y","article-title":"Assessing the susceptibility to water-induced soil erosion using a geomorphological, bivariate statistics-based approach","volume":"67","author":"Magliulo","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.catena.2016.01.011","article-title":"Effects of climate, land cover and topography on soil erosion risk in a semiarid basin of the Andes","volume":"140","author":"Ochoa","year":"2016","journal-title":"Catena"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"116901","DOI":"10.1016\/j.envres.2023.116901","article-title":"Assessment of badland susceptibility and its governing factors using a random forest approach. Application to the Upper Llobregat River Basin and Catalonia (Spain)","volume":"237","author":"Torra","year":"2023","journal-title":"Environ. Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.ijsrc.2023.06.002","article-title":"Rapid magnetic susceptibility measurement as a tracer to assess the erosion-deposition process using tillage homogenization and simple proportional models: A case study in northern of Morocco","volume":"38","author":"Ouallali","year":"2023","journal-title":"Int. J. Sediment Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1207027","DOI":"10.3389\/fenvs.2023.1207027","article-title":"Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion","volume":"11","author":"Aboutaib","year":"2023","journal-title":"Front. Environ. Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.jhydrol.2017.02.044","article-title":"Assessing the risk of saltwater intrusion in coastal aquifers","volume":"551","author":"Klassen","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.jenvman.2019.01.069","article-title":"GALDIT-SUSI a modified method to account for surface water bodies in the assessment of aquifer vulnerability to seawater intrusion","volume":"235","author":"Kazakis","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s12594-020-1469-1","article-title":"Assessment of Aquifer Vulnerability Using GALDIT Model\u2013A Case Study","volume":"95","author":"Sujitha","year":"2020","journal-title":"J. Geol. Soc. India"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1007\/s12665-022-10534-2","article-title":"The use of hybrid machine learning models for improving the GALDIT model for coastal aquifer vulnerability mapping","volume":"81","author":"Bordbar","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Pham, N.Q., Ta, T.T., Tran, L., and Nguyen, T.T. (2023). Assessment of seawater intrusion vulnerability of coastal aquifers in context of climate change and sea level rise in the central coastal plains, Vietnam. Environ. Dev. Sustain.","DOI":"10.1007\/s10668-023-03498-1"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s11069-015-2075-1","article-title":"Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)","volume":"81","author":"Myronidis","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s11069-020-04317-w","article-title":"A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model","volume":"105","author":"Wei","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"220104","DOI":"10.5614\/j.eng.technol.sci.2022.54.1.4","article-title":"Land Subsidence Susceptibility Projection for Palembang Slum Area by Complex MCDM-AHP Technique","volume":"54","author":"Deros","year":"2022","journal-title":"J. Eng. Technol. Sci."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s40677-019-0124-x","article-title":"Modelling terrain erosion susceptibility of logged and regenerated forested region in northern Borneo through the Analytical Hierarchy Process (AHP) and GIS techniques","volume":"6","author":"Vijith","year":"2019","journal-title":"Geoenviron. Disasters"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s10661-022-10601-y","article-title":"Comparative assessment of groundwater vulnerability using GIS-based DRASTIC and DRASTIC-AHP for Thoothukudi District, Tamil Nadu India","volume":"195","author":"Saravanan","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_99","unstructured":"(2023, October 06). OpenStreetMap. Available online: https:\/\/www.openstreetmap.org\/."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s12665-022-10508-4","article-title":"Rockfall hazard assessment of the slope of Mogao Grottoes, China based on AHP, F-AHP and AHP-TOPSIS","volume":"81","author":"Zhang","year":"2022","journal-title":"Environ. Earth Sci."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Sinha, A., Nikhil, S., Ajin, R.S., Danumah, J.H., Saha, S., Costache, R., Rajaneesh, A., Sajinkumar, K.S., Amrutha, K., and Johny, A. (2023). Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models. Fire, 6.","DOI":"10.3390\/fire6020044"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"102103","DOI":"10.1016\/j.scs.2020.102103","article-title":"Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen","volume":"56","author":"Lyu","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s12665-023-10766-w","article-title":"Spring water suitable and vulnerable watershed demarcation using AHP-TOPSIS and AHP-VIKOR models: Study on Aizawl district of North-Eastern hilly state of Mizoram, India","volume":"82","author":"Biswas","year":"2023","journal-title":"Environ. Earth Sci."},{"key":"ref_104","unstructured":"Feizi, Z. (2022). Sustainable Energy-Water-Environment Nexus in Desert Climates, Proceedings of the First International Conference on Sustainable Energy-Water-Environment Nexus in Desert Climate, Doha, Qatar, 2\u20135 December 2019, Springer."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1016\/j.aej.2020.11.012","article-title":"Safety risk identification and prioritize of forest logging activities using analytic hierarchy process (AHP)","volume":"60","author":"Unver","year":"2021","journal-title":"Alex. Eng. J."},{"key":"ref_106","unstructured":"Wang, P.Z. (1985). Fuzzy Set and Random Set Shadow, Beijing Normal University Press. (In Chinese)."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"66","DOI":"10.5004\/dwt.2019.23844","article-title":"Evaluation of management level of water conservancy construction supervision unit based on variable weight fuzzy theory","volume":"152","author":"Wang","year":"2019","journal-title":"Desalin. Water Treat."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Han, F., Liu, Z.L., and Wang, C.X. (2023). Research on a Comfort Evaluation Model for High-Speed Trains Based on Variable Weight Theory. Appl. Sci., 13.","DOI":"10.3390\/app13053144"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"108562","DOI":"10.1016\/j.ecolind.2022.108562","article-title":"The pollution scale weighting model in water quality evaluation based on the improved fuzzy variable theory","volume":"135","author":"Zeng","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.1007\/s10706-021-01709-y","article-title":"Model on Improved Variable Weight-Matter Element Theory for Risk Assessment of Water Inrush in Karst Tunnels","volume":"39","author":"Wang","year":"2021","journal-title":"Geotech. Geol. Eng."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.1007\/s11356-019-07311-3","article-title":"Evaluation of groundwater sustainable development considering seawater intrusion in Beihai City, China","volume":"27","author":"Ma","year":"2020","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"2754","DOI":"10.1002\/joc.7389","article-title":"An assessment of statistical interpolation methods suited for gridded rainfall datasets","volume":"42","author":"Liu","year":"2022","journal-title":"Int. J. Climatol."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Febrianto, H., Fariza, A., and Hasim, J.A.N. (2016, January 15\u201317). Urban Flood Risk Mapping Using Analytic Hierarchy Process and Natural Break Classification. Proceedings of the 5th International Conference on Knowledge Creation and Intelligent Computing (KCIC), Manado, Indonesia.","DOI":"10.1109\/KCIC.2016.7883639"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Khamis, N., Sin, T.C., and Hock, G.C. (2018, January 3\u20134). Segmentation of Residential Customer Load Profile in Peninsular Malaysia using Jenks Natural Breaks. Proceedings of the 7th IEEE International Conference on Power and Energy (PECon), Kuala Lumpur, Malaysia.","DOI":"10.1109\/PECON.2018.8684113"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Li, B.H., Liu, K., Wang, M., He, Q., Jiang, Z.Y., Zhu, W.H., and Qiao, N.N. (2022). Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14225795"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1007\/s12665-021-09763-8","article-title":"Enhancing spatial prediction of sinkhole susceptibility by mixed waters geochemistry evaluation: Application of ROC and GIS","volume":"80","author":"Taheri","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Liu, G.X., Zhang, Y.C., Zhang, J.Q., Lang, Q.L., Chen, Y.A., Wan, Z.Y., and Liu, H.A. (2023). Geographic-Information-System-Based Risk Assessment of Flooding in Changchun Urban Rail Transit System. Remote Sens., 15.","DOI":"10.3390\/rs15143533"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Shawky, M., and Hassan, Q.K. (2023). Geospatial Modeling Based-Multi-Criteria Decision-Making for Flash Flood Susceptibility Zonation in an Arid Area. Remote Sens., 15.","DOI":"10.3390\/rs15102561"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"18010","DOI":"10.1007\/s11356-021-16924-6","article-title":"Assessment of groundwater sustainable development considering geo-environment stability and ecological environment: A case study in the Pearl River Delta, China","volume":"29","author":"Huang","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"311","DOI":"10.2166\/ws.2016.129","article-title":"Investigation and control of seawater intrusion in the Eastern Nile Delta aquifer considering climate change","volume":"17","year":"2017","journal-title":"Water Sci. Technol. Water Supply"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Intui, S., Inazumi, S., and Soralump, S. (2022). Sustainability of Soil\/Ground Environment under Changes in Groundwater Level in Bangkok Plain, Thailand. Sustainability, 14.","DOI":"10.3390\/su141710908"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s11069-015-1960-y","article-title":"Comparing landslide susceptibility models in the Rio El Estado watershed on the SW flank of Pico de Orizaba volcano, Mexico","volume":"80","author":"Paulin","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jhydrol.2013.09.034","article-title":"Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS","volume":"504","author":"Tehrany","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s00366-018-0644-0","article-title":"Modification of landslide susceptibility mapping using optimized PSO-ANN technique","volume":"35","author":"Moayedi","year":"2019","journal-title":"Eng. Comput."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/5007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:09:02Z","timestamp":1760130542000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/5007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,18]]},"references-count":125,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15205007"],"URL":"https:\/\/doi.org\/10.3390\/rs15205007","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,18]]}}}