[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128491-en":3,"doc-seo-128491-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128491,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility","Flash floods are a high-impact natural hazard, and mapping flash flood susceptibility supports flood risk reduction, land use planning, and emergency resource deployment. Prior work shows machine learning models can outperform traditional statistical and process-based approaches, but standard ML often ignores local geographic context. This study proposes a local Geographically Weighted Random Forest (GWRF) model and compares it with global and local statistical and ML alternatives using an empirical case from Jiangxi Province, China.","A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility  \nJing Yao \\#   \nUrban Big Data Centre, School of Social and Political Sciences, University of Glasgow, UK Ziqi Li 1 \\#   \nDepartment of Geography, Florida State University, Tallahassee, FL, USA Xiaoxiang Zhang \\#  \nDepartment of Geographic Information Science, College of Hydrology and Water Resources, Hohai University, Nanjing, China  \nChangjun Liu \\#  \nDepartment of Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing, China  \nLiliang Ren \\#  \nState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, China  \n~~ Abstract ~~  \nFlash floods, as a type of devastating natural disasters, can cause significant damage to infrastructure, agriculture, and people’s livelihoods. Mapping flash flood susceptibility has long been an effective measure to help with the development of flash flood risk reduction and management strategies. Recent studies have shown that machine learning (ML) techniques perform better than traditional statistical and process-based models in estimating flash flood susceptibility. However, a major limitation of standard ML models is that they ignore the local geographic context where flash floods occur. To address this limitation, we developed a local Geographically Weighted Random Forest (GWRF) model and compared its performance against other global and local statistical and ML alternatives using an empirical flash floods model of Jiangxi Province, China.  \n2012 ACM Subject Classification Computing methodologies → Machine learning Keywords and phrases Machine Learning, Spatial Statistics, Flash floods, Susceptibility Digital Object Identifier 10.4230/LIPIcs.GIScience.2023.86  \nCategory Short Paper  \nFunding This work was supported by the National Key Research and Development Program of China [grant numbers No. 2019YFC1510601]; the Economic and Social Research Council, UK [grant number ES/P011020/1, ES/S007105/1] .  \n 1  Introduction  \nFlash floods are one of the most devastating natural disasters, which often occurs within a short period of time and can be caused by a variety of factors such as intense rainfall, rapid snow melt, landslides, and dam failure. Given their rapid speed and strong force, flash floods can cause significant damages to properties, infrastructures, and even loss of life. As a result, flash flood risk mitigation and management are of fundamental importance if sustainable  \n1 Corresponding author  \n© Jing Yao, Ziqi Li , Xiaoxiang Zhang, Changjun Liu, and Liliang Ren;  \nlicensed under Creative Commons License CC-BY 4.0  \n12th International Conference on Geographic Information Science (GIScience 2023) .  \nEditors: Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, and Sarah Wise; Article No. 86; pp. 86:1–86:6 Leibniz International Proceedings in Informatics  \n Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany  \n86:2 Global and Local Statistical and ML in Estimating Flash Flood Susceptibility  \ndevelopment is to be achieved. Flash flood susceptibility estimation has long been an effective means adopted by practitioners and policymakers to assist with development of flood risk reduction strategies, land use planning and emergency resource deployment [6] [8] .  \nCommon approaches that have been widely adopted in the estimation of flash flood susceptibility include statistical, hydrodynamic models and geographical information system (GIS) based spatial analyses. The examples of statistical models include regression analysis, frequency ratio, weights-of-evidence, and analytical hierarchy process, among others [6] . Hydrodynamic models usually predict the propensity of an area to flash floods by simulating the water flow during a rainfall event [13] . GIS-based approaches often combine potential factors that co","cbCaiq4UQibmgoPf","https://ap.wps.com/l/cbCaiq4UQibmgoPf","pdf",1127925,3,1,6,"English","en",105,"# Introduction\n## Statistical, hydrodynamic, and GIS-based approaches\n## Machine learning for flash flood susceptibility\n## GeoAI and geographic weighting motivation","[{\"question\":\"Why is flash flood susceptibility mapping important?\",\"answer\":\"It helps support flood risk reduction strategies, land use planning, and emergency resource deployment by identifying areas prone to flash floods.\"},{\"question\":\"What limitation affects standard machine learning models in this task?\",\"answer\":\"Standard ML models often ignore local geographic context, using the same hyperparameters across observations without capturing spatial differences.\"},{\"question\":\"What model does the paper propose to address local context?\",\"answer\":\"The paper develops a local Geographically Weighted Random Forest (GWRF) model and evaluates it against global and local statistical and ML alternatives using Jiangxi Province data.\"}]","A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility | 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