{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:58:58Z","timestamp":1779382738509,"version":"3.53.1"},"reference-count":90,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T00:00:00Z","timestamp":1540944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education and Training of Vietnam, and Hanoi University of Mining and Geology","award":["B2018-MDA-18\u0110T"],"award-info":[{"award-number":["B2018-MDA-18\u0110T"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg\u2013Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.<\/jats:p>","DOI":"10.3390\/s18113704","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T11:55:41Z","timestamp":1540986941000},"page":"3704","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6574-5762","authenticated-orcid":false,"given":"Phuong-Thao Thi","family":"Ngo","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Hanoi University of Mining and Geology, No. 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi 10000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nhat-Duc","family":"Hoang","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, Da Nang 550000 Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia"},{"name":"Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8214-9236","authenticated-orcid":false,"given":"Quang Khanh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Hanoi University of Mining and Geology, No. 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi 10000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan Truong","family":"Tran","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, No. 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi 10000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quang Minh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, No. 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi 10000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7010-8613","authenticated-orcid":false,"given":"Viet Nghia","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, No. 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi 10000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pijush","family":"Samui","sequence":"additional","affiliation":[{"name":"Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"},{"name":"Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-6479","authenticated-orcid":false,"given":"Dieu","family":"Tien Bui","sequence":"additional","affiliation":[{"name":"Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, N-3800 B\u00f8 i Telemark, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1080\/10106049.2017.1316780","article-title":"Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models","volume":"33","author":"Siahkamari","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Woodruff, S.C., and Regan, P. (2018). Quality of national adaptation plans and opportunities for improvement. Mitig. Adapt. Strat. Glob. Chang., 1\u201319.","DOI":"10.1007\/s11027-018-9794-z"},{"key":"ref_3","unstructured":"National Weather Service (NWS) (2018, July 06). What Is Flash Flooding, Available online: https:\/\/www.weather.gov\/phi\/FlashFloodingDefinition."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"S121","DOI":"10.1111\/jfr3.12187","article-title":"Characterising flash flood response to intense rainfall and impacts using historical information and gauged data in Britain","volume":"11","author":"Archer","year":"2018","journal-title":"J. Flood Risk Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1175\/BAMS-D-15-00247.1","article-title":"The FLASH Project: Improving the Tools for Flash Flood Monitoring and Prediction across the United States","volume":"98","author":"Gourley","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Papagiannaki, K., Kotroni, V., Lagouvardos, K., and Bezes, A. (2017). Flash Flood Risk and Vulnerability Analysis in Urban Areas: The Case of October 22, 2015, in Attica, Greece. Perspectives on Atmospheric Sciences, Springer International Publishing.","DOI":"10.1007\/978-3-319-35095-0_31"},{"key":"ref_7","first-page":"315","article-title":"Planform changes and large wood dynamics in two torrents during a severe flash flood in Braunsbach, Germany 2016","volume":"640\u2013641","author":"Schwientek","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s11069-017-3052-7","article-title":"Analysis of flash flood disaster characteristics in china from 2011 to 2015","volume":"90","author":"He","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/978-3-319-09048-1_155","article-title":"Flash flood events and urban development in Genoa (Italy): Lost in translation","volume":"Volume 5","author":"Faccini","year":"2015","journal-title":"Engineering Geology for Society and Territory"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"242","DOI":"10.3390\/hydrology2040242","article-title":"Flash Flood Prediction by Coupling KINEROS2 and HEC-RAS Models for Tropical Regions of Northern Vietnam","volume":"2","author":"Nguyen","year":"2015","journal-title":"Hydrology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1175\/1520-0450(2000)039<0815:POAFFI>2.0.CO;2","article-title":"Prediction of a Flash Flood in Complex Terrain. Part II: A Comparison of Flood Discharge Simulations Using Rainfall Input from Radar, a Dynamic Model, and an Automated Algorithmic System","volume":"39","author":"Yates","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Volkmann, T.H.M., Lyon, S.W., Gupta, H.V., and Troch, P.A. (2010). Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain. Water Resour. Res., 46.","DOI":"10.1029\/2010WR009145"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s11069-008-9300-0","article-title":"Modelling flash flood propagation in urban areas using a two-dimensional numerical model","volume":"50","author":"Paquier","year":"2009","journal-title":"Nat. Hazards"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s11069-010-9711-6","article-title":"Flash flood routing modeling for levee-breaks and overbank flows due to typhoon events in a complicated river system","volume":"58","author":"Liu","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jhydrol.2016.06.027","article-title":"Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS","volume":"540","author":"Pradhan","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.scitotenv.2018.01.266","article-title":"A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran","volume":"627","author":"Khosravi","year":"2018","journal-title":"Sci. Total. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ahmadlou, M., Karimi, M., Alizadeh, S., Shirzadi, A., Parvinnejhad, D., Shahabi, H., and Panahi, M. (2018). Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto Int., 1\u201321.","DOI":"10.1080\/10106049.2018.1474276"},{"key":"ref_18","first-page":"29","article-title":"Opportunities provided by geographic information systems and volunteered geographic information for a timely emergency response during flood events in Cologne, Germany","volume":"91","author":"Tzavella","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Atta-ur-Rahman Dash, S., and Mahmud, M. (2018). Flood-Prediction Techniques Based on Geographical Information System Using Wireless Sensor Networks. Advances in Data and Information Sciences, Springer.","DOI":"10.1007\/978-981-13-0277-0_30"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.catena.2014.10.017","article-title":"Flood susceptibility assessment using GIS-based support vector machine model with different kernel types","volume":"125","author":"Tehrany","year":"2015","journal-title":"CATENA"},{"key":"ref_21","first-page":"123","article-title":"An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data","volume":"73","author":"Li","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1109\/TGRS.2018.2797536","article-title":"Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images","volume":"56","author":"Amitrano","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/s12517-018-3584-5","article-title":"Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: A comparative study","volume":"11","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, H., Zhang, Z., and Liu, Z. (2017). Application of artificial neural networks for catalysis: A review. Catalysts, 7.","DOI":"10.3390\/catal7100306"},{"key":"ref_25","first-page":"8","article-title":"A Review of User Interface Design for Interactive Machine Learning","volume":"8","author":"Dudley","year":"2018","journal-title":"ACM Trans. Interact. Intell. Syst. (TiiS)"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s12665-016-5323-0","article-title":"Flood hazard mapping in Jamaica using principal component analysis and logistic regression","volume":"75","author":"Nandi","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1007\/s11069-016-2357-2","article-title":"A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique","volume":"83","author":"Khosravi","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.scitotenv.2017.09.262","article-title":"Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms","volume":"615","author":"Kornejady","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1080\/19475705.2017.1308971","article-title":"Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea","volume":"8","author":"Lee","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.5194\/gmd-10-3391-2017","article-title":"A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods","volume":"10","author":"Hoang","year":"2017","journal-title":"Geosci. Model. Dev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.envsoft.2017.06.012","article-title":"A novel hybrid artificial intelligence approach for flood susceptibility assessment","volume":"95","author":"Chapi","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sachdeva, S., Bhatia, T., and Verma, A.K. (2017, January 3\u20135). Flood susceptibility mapping using GIS-based support vector machine and particle swarm optimization: A case study in Uttarakhand (India). Proceedings of the 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India.","DOI":"10.1109\/ICCCNT.2017.8204182"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1007\/s11269-017-1589-6","article-title":"Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models","volume":"31","author":"Rahmati","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s12665-010-0551-1","article-title":"Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery","volume":"62","author":"Youssef","year":"2011","journal-title":"Environ. Earth Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.jhydrol.2005.11.059","article-title":"Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii","volume":"327","author":"Sahoo","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00704-015-1702-9","article-title":"Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of na\u00efve bayes, multilayer perceptron neural networks, and functional trees methods","volume":"128","author":"Pham","year":"2017","journal-title":"Theor. Appl. Clim."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hoang, N.D., and Tien Bui, D. (2017). GIS-Based Landslide Spatial Modeling Using Batch-Training Back-propagation Artificial Neural Network: A Study of Model Parameters. Advances and Applications in Geospatial Technology and Earth Resources, Springer International Publishing.","DOI":"10.1007\/978-3-319-68240-2_15"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/19475705.2017.1407368","article-title":"Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)","volume":"9","author":"Kalantar","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2018.06.006","article-title":"Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia","volume":"318","author":"Aditian","year":"2018","journal-title":"Geomorphology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.engappai.2012.01.023","article-title":"A hybrid algorithm for artificial neural network training","volume":"26","author":"Yaghini","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/01969722.2017.1285162","article-title":"A Hybrid Artificial Neural Network with Metaheuristic Algorithms for Predicting Stock Price","volume":"48","author":"Ghasemiyeh","year":"2017","journal-title":"Cybern. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kuok, K.K., Kueh, S.M., and Chiu, P.C. (2018). Bat optimisation neural networks for rainfall forecasting: Case study for Kuching city. J. Water Clim. Chang.","DOI":"10.2166\/wcc.2018.136"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s10489-017-0967-3","article-title":"Improved monarch butterfly optimization for unconstrained global search and neural network training","volume":"48","author":"Faris","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6381610","DOI":"10.1155\/2018\/6381610","article-title":"STATCOM Estimation Using Back-Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural Networks","volume":"2018","author":"Soodi","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.engappai.2017.09.012","article-title":"Optimization of neural network using kidney-inspired algorithm with control of filtration rate and chaotic map for real-world rainfall forecasting","volume":"67","author":"Jaddi","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_46","first-page":"1435810","article-title":"A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training","volume":"2018","author":"Hacibeyoglu","year":"2018","journal-title":"Sci. Program."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.engappai.2017.01.013","article-title":"Metaheuristic design of feedforward neural networks: A review of two decades of research","volume":"60","author":"Ojha","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_48","unstructured":"Vnexpress (2018, July 06). Flash Floods Kill 18, Isolate Towns in Northern Vietnam. Available online: VnExpress.net."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1016\/j.envsci.2011.05.017","article-title":"Flash flood forecasting, warning and risk management: The HYDRATE project","volume":"14","author":"Borga","year":"2011","journal-title":"Environ. Sci. Policy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.rse.2016.09.009","article-title":"Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry","volume":"186","author":"Dai","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1111\/jfr3.12303","article-title":"Multi-temporal synthetic aperture radar flood mapping using change detection","volume":"11","author":"Clement","year":"2018","journal-title":"J. Flood Risk Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1080\/01431161.2016.1192304","article-title":"Sentinel-1-based flood mapping: A fully automated processing chain","volume":"37","author":"Twele","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lee, J.-S. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell., 165\u2013168.","DOI":"10.1109\/TPAMI.1980.4766994"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","article-title":"Artificial neural networks: Fundamentals, computing, design, and application","volume":"43","author":"Basheer","year":"2000","journal-title":"J. Microbiol. Methods"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"04016030","DOI":"10.1061\/(ASCE)CP.1943-5487.0000599","article-title":"Predicting Colonization Growth of Algae on Mortar Surface with Artificial Neural Network","volume":"30","author":"Tran","year":"2016","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1109\/72.329697","article-title":"Training feedforward networks with the Marquardt algorithm","volume":"5","author":"Hagan","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1016\/j.asoc.2012.10.014","article-title":"Neural networks to predict earthquakes in Chile","volume":"13","author":"Reyes","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_59","unstructured":"Beale, M.H., Hagan, M.T., and Demuth, H.B. (2018). Neural Network Toolbox User\u2019s Guide, The MathWorks, Inc.. Available online: https:\/\/www.mathworks.com\/help\/pdf_doc\/nnet\/nnet_ug.pdf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","article-title":"Firefly algorithm, stochastic test functions and design optimisation","volume":"2","author":"Yang","year":"2010","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.swevo.2013.06.001","article-title":"A comprehensive review of firefly algorithms","volume":"13","author":"Fister","year":"2013","journal-title":"Swarm Evol. Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.conbuildmat.2018.05.201","article-title":"A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete","volume":"180","author":"Bui","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.apenergy.2016.12.134","article-title":"Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm","volume":"190","author":"Wang","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Cheng, M.Y., and Hoang, N.D. (2017). Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine. Neural Comput. Appl.","DOI":"10.1007\/s00521-017-2840-z"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Tilahun, S.L., Ngnotchouye, J.M.T., and Hamadneh, N.N. (2017). Continuous versions of firefly algorithm: A review. Artif. Intell. Rev.","DOI":"10.1007\/s10462-017-9568-0"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"04018031","DOI":"10.1061\/(ASCE)CP.1943-5487.0000779","article-title":"Back-Analysis Method for Stope Displacements Using Gradient-Boosted Regression Tree and Firefly Algorithm","volume":"32","author":"Qi","year":"2018","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"04016064","DOI":"10.1061\/(ASCE)CP.1943-5487.0000639","article-title":"Discrete Firefly Algorithm for Scaffolding Construction Scheduling","volume":"31","author":"Hou","year":"2017","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2009). Firefly algorithms for multimodal optimization. Stochastic Algorithms: Foundations and Applications, Proceedings of the International Symposium on Stochastic Algorithms, Sapporo, Japan, 26\u201328 October 2009, Springer.","DOI":"10.1007\/978-3-642-04944-6_14"},{"key":"ref_69","unstructured":"GSO (2017). Lao Cai Statistical Year Book 2016."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.jhydrol.2006.10.020","article-title":"The changing flow regime and sediment load of the Red River, Viet Nam","volume":"334","author":"Le","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jenvman.2018.03.089","article-title":"Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping","volume":"217","author":"Valavi","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_72","unstructured":"USGS (2018, February 15). The United States Geological Survey Earth Resources Observation and Science Cente, Available online: http:\/\/earthexplorer.usgs.gov."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.enggeo.2016.10.011","article-title":"Development of a landslide susceptibility assessment for a rail network","volume":"215","author":"Gavin","year":"2016","journal-title":"Eng. Geol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2009.09.025","article-title":"GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China","volume":"115","author":"Bai","year":"2010","journal-title":"Geomorphology"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geomorph.2004.06.010","article-title":"The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan","volume":"65","author":"Ayalew","year":"2005","journal-title":"Geomorphology"},{"key":"ref_76","unstructured":"Heaton, J. (2008). Introduction to Neural Networks for C#, Heaton Research, Inc."},{"key":"ref_77","unstructured":"Matwork (2017). Statistics and Machine Learning Toolbox User\u2019s Guide, Matwork Inc.. Available online: https:\/\/www.mathworks.com\/help\/pdf_doc\/stats\/stats.pdf."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.jhydrol.2014.03.008","article-title":"Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS","volume":"512","author":"Tehrany","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1016\/j.scitotenv.2017.10.114","article-title":"Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution","volume":"621","author":"Hong","year":"2018","journal-title":"Sci. Total. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Tran","year":"2016","journal-title":"Landslides"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Montavon, G., Orr, G., and M\u00fcller, K.R. (2012). Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s00521-015-2121-7","article-title":"Medium\u2013large earthquake magnitude prediction in Tokyo with artificial neural networks","volume":"28","author":"Troncoso","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.knosys.2013.06.011","article-title":"Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula","volume":"50","author":"Reyes","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/S0720-048X(97)00157-5","article-title":"Receiver operating characteristic (ROC) analysis: Basic principles and applications in radiology","volume":"27","author":"Pattynama","year":"1998","journal-title":"Eur. J. Radiol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Jaafari, A., Prakash, I., and Bui, D.T. (2018). A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bull. Eng. Geol. Environ.","DOI":"10.1007\/s10064-018-1281-y"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.catena.2018.01.005","article-title":"Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)","volume":"163","author":"Hong","year":"2018","journal-title":"CATENA"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"CATENA"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1080\/19475705.2015.1084541","article-title":"Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem","volume":"7","author":"Satir","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s10064-016-0924-0","article-title":"Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: A multi-dataset study","volume":"77","author":"Hoang","year":"2018","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.pce.2009.12.002","article-title":"Landslide susceptibility assessment of the Kra\u013eovany\u2013Liptovsk\u00fd Mikul\u00e1\u0161 railway case study","volume":"35","author":"Bednarik","year":"2010","journal-title":"Phys. Chem. Earth Parts A B C"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3704\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:27:09Z","timestamp":1760196429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3704"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,31]]},"references-count":90,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113704"],"URL":"https:\/\/doi.org\/10.3390\/s18113704","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,31]]}}}