[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118674-en":3,"doc-seo-118674-105":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},118674,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Intelligent Forecasting of Flooding Intensity Using Machine Learning","Innovative flood forecasting research for Bor County, South Sudan develops an intelligent, machine-learning based model to predict flooding intensity from complex hydrological drivers. The study integrates land-use analysis and rainfall calculations using a decade of weather records, then applies classifiers including Support Vector Machines, Decision Trees, Naive Bayes, and Neural Networks. Results show Linear SVM accuracy of 87.5% for raw data and 100% for high-velocity events, with Naive Bayes comparable performance and ANN advantages in runoff estimation.","Intelligent Forecasting of Flooding Intensity Using Machine  \nLearning  \nAbraham Ayuen Ngong Deng 1, Nursetiawan 1, 2, Jazaul Ikhsan 1, 2*,  \nSlamet Riyadi 2, 3 , Ahmad Zaki 2, 3  \n1 Master Program of Civil Engineering, Universitas Muhammadiyah Yogyakarta, 55183 Yogyakarta, Indonesia.  \n2 Department of Civil Engineering, Universitas Muhammadiyah Yogyakarta, 55183 Yogyakarta, Indonesia.  \n3 Department of Informatic Engineering, Universitas Muhammadiyah Yogyakarta, 55183 Yogyakarta, Indonesia.  \nReceived 28 May 2024; Revised 23 September 2024; Accepted 29 September 2024; Published 01 October 2024  \nAbstract  \nThis innovative study addresses critical flood prediction needs in Bor County, South Sudan, utilizing machine learning to develop an intelligent forecasting model. The research integrates diverse analytical techniques, including land use analysis and rainfall calculations, with a decade of weather data to understand complex hydrological dynamics. This research employs machine learning classifiers such as Support Vector Machines, Decision Trees, and Neural Networks. Findings reveal promising results, with the Linear SVM classifier achieving 87.5% prediction accuracy for raw data and 100% accuracy for high-velocity flooding events. The Naive Bayes classifier matched this performance, while Artificial Neural Networks showed a slight advantage in runoff estimation. The study's novelty lies in its holistic approach, combining machine learning with advanced visualization tools and geographic information systems. This creates a dynamic, real-time forecasting system bridging sophisticated analysis and practical flood management strategies. Focusing on model interpretability and multi-scale forecasting enhances its value to policymakers and disaster management authorities. This research significantly advances the application of AI to flood prediction and disaster management in offering future studies on humanitarian challenges. By enhancing early warning capabilities, this system substantially reduces flood-related losses and transforms disaster preparedness in vulnerable regions worldwide, potentially saving lives and mitigating economic impacts.  \nKeywords: Support Vector Machines; Flood Intensity; Rainfall Data; Classification Learners; Confusion Matrix.  \n1. Introduction  \nThe more significant part of Bor County in the Jonglei State of South Sudan is lowland and tropical, with a tiny area of raised ground in the north.  \nBecause of the region's shallow topography, especially in the southern parts, a sustainable urban drainage system must be implemented to lessen the likelihood of flooding, brought about by the lack of staged evaluation and monitoring systems to address catastrophic situations like river overflowing, which highlights this necessity [1] . The county's susceptibility to floods was brought to light on July 12, 2020, when a flash flood destroyed the surrounding countryside. The dyke along the Nile River burst because of a combination of high rainfall and following conditions. Such events highlight the critical importance of developing environmentally conscious urban waterways and water control systems  \n* Corresponding author: [jazaul.ikhsan@umy.ac.id](jazaul.ikhsan@umy.ac.id)  \n [http://dx.doi.org/10.28991/CEJ-2024-010-10-010](http://dx.doi.org/10.28991/CEJ-2024-010-10-010)  \n© 2024 by the authors. Licensee C.E.J, Tehran, Iran. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nthat can provide ecological services to both metropolitan and natural environments while counteracting the effects of gentrification and ensuring sustainable water flow management. Flooding in Bor County is primarily attributed to subfactorial etiologic factors, encompassing both artificial and natural causes. These include extensive deforestation and prolonged perio","cbCaioKw3g8aQ7qH","https://ap.wps.com/l/cbCaioKw3g8aQ7qH","pdf",1997181,1,23,"English","en",105,"# Introduction\n# Methods\n## Machine Learning Classifiers\n# Results and Discussion\n## Prediction Accuracy\n# Conclusion and Implications","[{\"question\":\"What problem does the study address in Bor County?\",\"answer\":\"The study targets the need for more reliable flood intensity forecasts in Bor County, where recurring flash floods and a lack of adequate early warning systems lead to severe harm.\"},{\"question\":\"Which machine learning methods are used for forecasting?\",\"answer\":\"The research uses classifiers including Support Vector Machines, Decision Trees, Naive Bayes, and Artificial Neural Networks, combining them with hydrological and rainfall-related inputs.\"},{\"question\":\"How accurate is the proposed forecasting approach?\",\"answer\":\"Linear SVM achieves 87.5% prediction accuracy for raw data and 100% accuracy for high-velocity flooding events; Naive Bayes matches this performance, while ANN shows a slight edge in runoff estimation.\"}]","Intelligent Forecasting of Flooding Intensity Using Machine Learning | PDF",1785684829,58,{"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},"intelligent-forecasting-of-flooding-intensity-using-machine-learning","",{"@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/intelligent-forecasting-of-flooding-intensity-using-machine-learning/118674/",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","2026-08-02",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},"What problem does the study address in Bor County?","Question",{"text":75,"@type":76},"The study targets the need for more reliable flood intensity forecasts in Bor County, where recurring flash floods and a lack of adequate early warning systems lead to severe harm.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used for forecasting?",{"text":80,"@type":76},"The research uses classifiers including Support Vector Machines, Decision Trees, Naive Bayes, and Artificial Neural Networks, combining them with hydrological and rainfall-related inputs.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the proposed forecasting approach?",{"text":84,"@type":76},"Linear SVM achieves 87.5% prediction accuracy for raw data and 100% accuracy for high-velocity flooding events; 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