{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T20:23:44Z","timestamp":1773519824899,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T00:00:00Z","timestamp":1607904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA20030302"],"award-info":[{"award-number":["XDA20030302"]}]},{"name":"Science and Technology Project of Xizang Autonomous Region","award":["XZ201901-GA-07"],"award-info":[{"award-number":["XZ201901-GA-07"]}]},{"name":"National Flash Flood Investigation and Evaluation Project","award":["SHZH-IWHR-57"],"award-info":[{"award-number":["SHZH-IWHR-57"]}]},{"name":"Southwest Petroleum University of Science and Technology Innovation Team Projects","award":["2017CXTD09"],"award-info":[{"award-number":["2017CXTD09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.<\/jats:p>","DOI":"10.3390\/ijgi9120748","type":"journal-article","created":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T21:25:08Z","timestamp":1607981108000},"page":"748","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China"],"prefix":"10.3390","volume":"9","author":[{"given":"Yifan","family":"Cao","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"given":"Hongliang","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"given":"Junnan","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1580-4979","authenticated-orcid":false,"given":"Weiming","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Kun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"given":"Quan","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China"}]},{"given":"Zhiwei","family":"Yong","sequence":"additional","affiliation":[{"name":"School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1016\/j.scitotenv.2017.10.037","article-title":"Mapping flood susceptibility in mountainous areas on a national scale in China","volume":"615","author":"Zhao","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1007\/s11430-017-9238-7","article-title":"Spatiotemporal evolution and driving factors of China\u2019s flash flood disasters since 1949","volume":"61","author":"Liu","year":"2018","journal-title":"Sci. China Earth Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yuan, X., Guo, L., Huang, Y., and Zhang, X. (2017). Driving Force Analysis of the Temporal and Spatial Distribution of Flash Floods in Sichuan Province. Sustainability, 9.","DOI":"10.3390\/su9091527"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xiong, J., Pang, Q., Fan, C., Cheng, W., Ye, C., Zhao, Y., He, Y.-R., and Cao, Y. (2020). Spatiotemporal Characteristics and Driving Force Analysis of Flash Floods in Fujian Province. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020133"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.jhydrol.2008.08.023","article-title":"Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins","volume":"362","author":"Norbiato","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1002\/hyp.8117","article-title":"Multivariate analysis of flood characteristics in a climate change context of the watershed of the Baskatong reservoir, Province of Qu\u00e9bec, Canada","volume":"26","author":"Chebana","year":"2012","journal-title":"Hydrol. Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.jhydrol.2011.02.017","article-title":"Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model","volume":"401","author":"Oeurng","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1007\/s11269-006-9089-0","article-title":"WetSpa Model Application for Assessing Reforestation Impacts on Floods in Margecany\u2013Hornad Watershed, Slovakia","volume":"21","author":"Bahremand","year":"2007","journal-title":"Water Resour. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1080\/10106049.2015.1041559","article-title":"Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran","volume":"31","author":"Rahmati","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1007\/s11269-014-0817-6","article-title":"Multi-Criteria Analysis Framework for Potential Flood Prone Areas Mapping","volume":"29","author":"Papaioannou","year":"2015","journal-title":"Water Resour. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.scitotenv.2017.12.256","article-title":"Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China","volume":"625","author":"Hong","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chang, M.-J., Chang, H.-K., Chen, Y.-C., Lin, G.-F., Chen, P.-A., Lai, J.-S., and Tan, Y.-C. (2018). A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems. Water, 10.","DOI":"10.3390\/w10121734"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s12665-011-1504-z","article-title":"An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia","volume":"67","author":"Kia","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1016\/j.jhydrol.2015.06.008","article-title":"Flood hazard risk assessment model based on random forest","volume":"527","author":"Wang","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bui, D.T., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F., Melesse, A.M., Pham, B.T., Pourghasemi, H.R., Mahmoudi, M., and Bahrami, S. (2019). Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model. Remote Sens., 11.","DOI":"10.3390\/rs11131589"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.geomorph.2018.09.019","article-title":"New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China","volume":"324","author":"Yang","year":"2019","journal-title":"Geomorphology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2014.05.013","article-title":"Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale","volume":"152","author":"Jebur","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"982","article-title":"Probabilistic landslide susceptibility and factor effect analysis","volume":"47","author":"Lee","year":"2005","journal-title":"Environ. Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zeng, F., Lai, C., and Wang, Z. (2012, January 1\u20133). Flood Risk Assessment Based on Principal Component Analysis for Dongjiang River Basin. Proceedings of the 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, South China University of Technology, Guangzhou, China.","DOI":"10.1109\/RSETE.2012.6260577"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Bui, D.T., Yunus, A.P., Jia, K., Song, X., Revhaug, I., Xia, H., and Zhu, Z. (2015). Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0133262"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.catena.2012.05.005","article-title":"Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran","volume":"97","author":"Pourghasemi","year":"2012","journal-title":"Catena"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/s10346-017-0893-9","article-title":"Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods","volume":"15","author":"Luo","year":"2018","journal-title":"Landslides"},{"key":"ref_24","first-page":"116","article-title":"Geodetector: Principle and prospective","volume":"72","author":"Wang","year":"2017","journal-title":"Acta Geogr. Sin."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106545","DOI":"10.1016\/j.ecolind.2020.106545","article-title":"Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin","volume":"117","author":"Zhu","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_26","first-page":"953","article-title":"Factors influencing the incidence of bacterial dysentery in parts of southwest China, using data from the geodetector","volume":"40","author":"Wang","year":"2019","journal-title":"Chin. J. Epidemiol."},{"key":"ref_27","unstructured":"(2020, November 30). China Statistical Yearbook, Available online: http:\/\/tjj.fujian.gov.cn\/tongjinianjian\/dz2018\/index-cn.htm."},{"key":"ref_28","first-page":"5344","article-title":"Study on the Spatial Pattern of Rainfall Erosivity Based on Geostatistics and GIS of Fujian Province","volume":"27","author":"Zhang","year":"2009","journal-title":"J. Mt. Sci."},{"key":"ref_29","unstructured":"Fujian Bureau of Geology and Mineral Resources (1985). Regional Geology of Fujian Province, Geological Publishing House."},{"key":"ref_30","unstructured":"Wang, D., and Zhou, X. (1982). Volcanic Petrology, Science Press."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","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_32","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1080\/19475705.2017.1362038","article-title":"GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques","volume":"8","author":"Tehrany","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xiong, J., Ye, C., Cheng, W., Guo, L., Zhou, C., and Zhang, X. (2019). The Spatiotemporal Distribution of Flash Floods and Analysis of Partition Driving Forces in Yunnan Province. Sustainability, 11.","DOI":"10.3390\/su11102926"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11069-017-2986-0","article-title":"An approach to quality validation of large-scale data from the Chinese Flash Flood Survey and Evaluation (CFFSE)","volume":"89","author":"Yuan","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_35","first-page":"919","article-title":"Regional Landslide Susceptibility Assessment for Longnan County in Jiangxi Province","volume":"19","author":"Su","year":"2019","journal-title":"Sci. Technol. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1007\/s12665-018-7667-0","article-title":"The application of a Dempster\u2013Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods","volume":"77","author":"Tehrany","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_37","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_38","first-page":"1374","article-title":"Spatial-temporal distribution and the influencing factors of mountain flood disaster in southwest China","volume":"74","author":"Xiong","year":"2019","journal-title":"Acta Geogr. Sin."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1002\/2014GL062482","article-title":"Hydrologic versus geomorphic drivers of trends in flood hazard","volume":"42","author":"Slater","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_40","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":"Bui","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1007\/s11069-015-1605-1","article-title":"Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya","volume":"77","author":"Meraj","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_42","first-page":"848","article-title":"Assessment of the Difficulty of Warning Mountain Torrent Disasters: Case Study of the Yangtze River","volume":"32","author":"Cai","year":"2015","journal-title":"J. Yangtze River Sci. Res. Inst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.jhydrol.2019.04.072","article-title":"River basin-scale flood hazard assessment using a modified multi-criteria decision analysis approach: A case study","volume":"574","author":"Toosi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1016\/j.scitotenv.2018.10.064","article-title":"An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines","volume":"651","author":"Choubin","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1002\/jame.20026","article-title":"A China Dataset of Soil Properties for Land Surface Modeling","volume":"5","author":"Shangguan","year":"2013","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Vojtek, M., and Vojtekov\u00e1, J. (2019). Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process. Water, 11.","DOI":"10.3390\/w11020364"},{"key":"ref_47","first-page":"425","article-title":"Study on Rainfall Index Selection for Hazard Analysis of Mountain Torrents Disaster of Small Watersheds","volume":"19","author":"Li","year":"2017","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1007\/s10346-015-0578-1","article-title":"Regional vulnerability assessment for debris flows in China\u2014A CWS approach","volume":"13","author":"Ding","year":"2016","journal-title":"Landslides"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1002\/2015GL066941","article-title":"Spatial association between dissection density and environmental factors over the entire conterminous United States","volume":"43","author":"Luo","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_50","first-page":"1641","article-title":"Spatial variability and influencing factors of LST in plateau area: Exemplified by Sangzhuzi District","volume":"31","author":"Xiong","year":"2019","journal-title":"Remote Sens. Land Resour."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ecolind.2016.02.052","article-title":"A measure of spatial stratified heterogeneity","volume":"67","author":"Wang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1080\/10106049.2017.1404143","article-title":"Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China","volume":"34","author":"Chen","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.jenvman.2018.11.110","article-title":"Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS","volume":"232","author":"Arabameri","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_54","first-page":"1","article-title":"GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China","volume":"75","author":"Chen","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_55","first-page":"1","article-title":"Weights-of-evidence method based on GIS for assessing susceptibility to debris flows in Kangding County, Sichuan Province, China","volume":"75","author":"Chen","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lim, J., and Lee, K.-S. (2018). Flood Mapping Using Multi-Source Remotely Sensed Data and Logistic Regression in the Heterogeneous Mountainous Regions in North Korea. Remote Sens., 10.","DOI":"10.3390\/rs10071036"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.geomorph.2015.06.001","article-title":"Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)","volume":"249","author":"Trigila","year":"2015","journal-title":"Geomorphology"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s10346-010-0202-3","article-title":"Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model","volume":"7","author":"Chauhan","year":"2010","journal-title":"Landslides"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.jhydrol.2019.03.073","article-title":"A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods","volume":"573","author":"Khosravi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s12517-018-4095-0","article-title":"Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors","volume":"11","author":"Nassar","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, W., Shahabi, H., Zhang, S., Khosravi, K., Shirzadi, A., Chapi, K., Pham, B.T., Han, L., Chai, H., and Ma, J. (2018). Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression. Appl. Sci., 8.","DOI":"10.3390\/app8122540"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1007\/s12665-017-6981-2","article-title":"A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)","volume":"76","author":"Hong","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.1007\/s11269-019-02293-w","article-title":"Comparison between Different Distributed Methods for Flood Susceptibility Mapping","volume":"33","author":"Liuzzo","year":"2019","journal-title":"Water Resour. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jhydrol.2012.03.028","article-title":"Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool\u2013A case of predicting potential zones of sustainable groundwater resources","volume":"440","author":"Adiat","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"101211","DOI":"10.1016\/j.ijdrr.2019.101211","article-title":"Flood susceptibility modeling and hazard perception in Rwanda","volume":"38","author":"Li","year":"2019","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/19475705.2018.1506509","article-title":"Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods","volume":"10","author":"Tehrany","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_67","first-page":"2","article-title":"A Study of the Impact of Terrain on the Precipitation of \u201cKROSA\u201d","volume":"9","author":"Huang","year":"2009","journal-title":"Meteorol. Mon."},{"key":"ref_68","first-page":"370","article-title":"Influence of The Regional Scale Topography on the Climatalogical Distribution of Precipitatio Over Southeastern China","volume":"9","author":"Pang","year":"1993","journal-title":"J. Trop. Meteorol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"11904","DOI":"10.1029\/2018GL078998","article-title":"Response of the Hydrological Cycle in Asian Monsoon Systems to Global Warming Through the Lens of Water Vapor Wave Activity Analysis","volume":"45","author":"Xue","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"7082606","DOI":"10.1155\/2017\/7082606","article-title":"Spatial-Temporal Patterns and Controls of Evapotranspiration across the Tibetan Plateau (2000\u20132012)","volume":"2017","author":"Zhang","year":"2017","journal-title":"Adv. Meteorol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"094015","DOI":"10.1088\/1748-9326\/10\/9\/094015","article-title":"The timing of anthropogenic emergence in simulated climate extremes","volume":"10","author":"King","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_72","first-page":"2932","article-title":"GIS-based Risk Zoning of Flood Disasters in Upstream of the Minjiang River","volume":"5","author":"Yue","year":"2015","journal-title":"J. Environ. Eng. Technol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1016\/j.scitotenv.2018.06.342","article-title":"The way forward: Can connectivity be useful to design better measuring and modelling schemes for water and sediment dynamics?","volume":"644","author":"Keesstra","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Ramesh, V., and Iqbal, S.S. (2020). Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: A case study of Greater Mumbai, Maharashtra, India. Geocarto Int., 1\u201326.","DOI":"10.1080\/10106049.2020.1730448"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s12665-018-8041-y","article-title":"Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan","volume":"78","author":"Mahmood","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.advwatres.2019.05.020","article-title":"Flood risk and its reduction in China","volume":"130","author":"Kundzewicz","year":"2019","journal-title":"Adv. Water Resour."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/12\/748\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:44:43Z","timestamp":1760179483000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/12\/748"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,14]]},"references-count":76,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["ijgi9120748"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9120748","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,14]]}}}