[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118725-en":3,"doc-seo-118725-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118725,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Modeling rules of regional flash flood susceptibility prediction using different machine learning models - Research summary","Regional flash flood susceptibility varies by both space and modeling approach, motivating an evaluation of different machine learning models and their modeling rules. The study uses 14 environmental factors (e.g., elevation, slope, aspect, gully density, and highway density) to build MLP, logistic regression, support vector machine, and random forest models, mapping the flash flood susceptibility index for Longnan County, Jiangxi, China. Model performance is compared with ROC analysis and susceptibility index distribution characteristics, and key environmental drivers are identified.","TYPE Original Research PUBLISHED 17 January 2023 DOI 10.3389/feart.2023.1117004  \nOPEN ACCESS  \nEDITED BY  \nGuang-Liang Feng,  \nInstitute of Rock and Soil Mechanics (CAS), China  \nREVIEWED BY  \nJiawei Xie,  \nThe University of Newcastle, Australia Luqi Wang,  \nChongqing University, China Paraskevas Tsangaratos,  \nNational Technical University of Athens, Greece  \n*CORRESPONDENCE  \nAnyu Hong,  \n [honganyu@ncu.edu.cn](honganyu@ncu.edu.cn)  \nSPECIALTY SECTION  \nThis article was submitted to Geohazardsand Georisks,  \na section of the journal Frontiers in Earth Science  \nRECEIVED 06 December 2022  \nACCEPTED 05 January 2023  \nPUBLISHED 17 January 2023  \nCITATION  \nChen Y, Zhang X, Yang K, Zeng S and Hong A (2023), Modeling rules of regional ﬂash ﬂood susceptibility prediction using different machine learning models.  \nFront. Earth Sci. 11:1117004 .  \ndoi: 10.3389/feart.2023.1117004  \nCOPYRIGHT  \n© 2023 Chen, Zhang, Yang, Zeng and Hong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nModeling rules of regional ﬂash ﬂood susceptibility prediction using different machine learning models  \nYuguo Chen, Xinyi Zhang, Kejun Yang, Shiyi Zeng and Anyu Hong* School of Civil Engineering and Architecture, Nanchang University, Nanchang, China  \nThe prediction performance of several machine learning models for regional ﬂash ﬂood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF), are proposed to carry out ﬂash ﬂood susceptibility modeling in order to investigate the modeling rules of different machine learning models in predicting ﬂash ﬂood susceptibility. The original data of 14 environmental factors, such as elevation, slope, aspect, gully density, and highway density, are chosen as input variables for the MLP, LR, SVM, and RF models in order to estimate and map the distribution of the ﬂash ﬂood susceptibility index in Longnan County, Jiangxi Province, China. Finally, the prediction performance of various models and modeling rules is evaluated using the ROC curve and the susceptibility index distribution features. The ﬁndings show that:  \n1) Machine learning models can accurately assess the region ’s vulnerability to ﬂash ﬂoods. The MLP, LR, SVM, and RF models all predict susceptibility very well. 2) The MLP (AUC=0.973, MV=0.1017, SD=0.2627) model has the best prediction performance for ﬂash ﬂood susceptibility, followed by the SVM (AUC=0 . 964, MV=0.1090, SD=0.2561) and RF (AUC=0.975, MV=0.2041, SD=0.1943) models, and the LR (AUC=0 . 882, MV=0 . 2613, SD=0 . 2913) model. 3) To a large extent, environmental factors such as elevation, gully density, and population density inﬂuence ﬂash ﬂood susceptibility.  \nKEYWORDS  \nﬂash ﬂood susceptibility prediction, uncertainty analysis, machine learning, multilayer perceptron, support vector machine, random forest  \n1 Introduction  \nA ﬂash ﬂood is deﬁned as rapid ﬂooding within the distribution of drainage basins in hilly areas (Bobrowsky, 2013), and it is characterized by rapid disaster generation, strong ringbreaking, and unpredictability, as well as the potential for a large number of casualties (Marchiet al., 2010) . China has paid close attention in recent years to the predictive study of geological hazard susceptibility. Because China has many hilly areas, ﬂash ﬂoods affect a wide range of areas, and regional ﬂash ﬂoods are easy to produce under short-term heavy rainfall (Bobrowsky, 2013) . With China’s elevated level of climate risk and an","cbCaijXcyH8baWsv","https://ap.wps.com/l/cbCaijXcyH8baWsv","pdf",3999802,1,17,"English","en",105,"# Introduction\n## Flash flood susceptibility and regional variability\n## GIS and machine learning-based susceptibility modeling\n# Methods\n## Environmental factors and input variables\n## Machine learning models for susceptibility prediction\n# Results\n## Performance evaluation with ROC and index distribution\n## Influence of environmental factors\n# Conclusion","[{\"question\":\"Which machine learning models are compared for flash flood susceptibility prediction?\",\"answer\":\"The study compares multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF) to model regional flash flood susceptibility.\"},{\"question\":\"What input data are used to estimate the susceptibility index?\",\"answer\":\"Four model types use 14 environmental factors, including elevation, slope, aspect, gully density, and highway density, as input variables to estimate and map the flash flood susceptibility index.\"},{\"question\":\"How is model performance evaluated and what factors influence susceptibility?\",\"answer\":\"Performance is evaluated using ROC curves and susceptibility index distribution features. Results indicate that environmental factors such as elevation, gully density, and population density substantially influence flash flood susceptibility.\"}]","Modeling rules of regional flash flood susceptibility prediction using different machine learning models - Research summary | PDF",1785719937,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"modeling-rules-of-regional-flash-flood-susceptibility-prediction-using-different-machine-learning-models-research-summary","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/modeling-rules-of-regional-flash-flood-susceptibility-prediction-using-different-machine-learning-models-research-summary/118725/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are compared for flash flood susceptibility prediction?","Question",{"text":75,"@type":76},"The study compares multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF) to model regional flash flood susceptibility.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What input data are used to estimate the susceptibility index?",{"text":80,"@type":76},"Four model types use 14 environmental factors, including elevation, slope, aspect, gully density, and highway density, as input variables to estimate and map the flash flood susceptibility index.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated and what factors influence susceptibility?",{"text":84,"@type":76},"Performance is evaluated using ROC curves and susceptibility index distribution features. Results indicate that environmental factors such as elevation, gully density, and population density substantially influence flash flood susceptibility.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]