[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120057-en":3,"doc-seo-120057-105":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},120057,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Assessment of Flood Risk Induced by Land Subsidence Using Machine Learning","Semarang City faces escalating flood impacts driven by land subsidence, which increases inundation extent and worsens damage in vulnerable lowland districts. With urban expansion and climate change intensifying hazard exposure, reliable flood risk assessment becomes essential for sustainable planning and disaster management. This study evaluates flood risk induced by land subsidence using machine learning, comparing five models (DT, KNN, LR, SVM, RF) trained and tested on 2884 sample points with 14 indices and hyperparameter optimization.","ISSN 2354-9114 (online), ISSN 0024-9521 (print)  \nIndonesian Journal of Geography Vol 56, No 3 (2024): 376-386  \nDOI: [10.22146/ijg.94726 website: htps://jurnal.ugm.ac.id/ijg](10.22146/ijg.94726 website: htps://jurnal.ugm.ac.id/ijg)  \n©2024 Faculty of Geography UGM and The Indonesian Geographers Associaton  \nRESEARCH ARTICLE  \nAssessment of Flood Risk Induced by Land Subsidence Using Machine Learning  \nB. D. Yuwono* 1, L.M. Sabri1. A.P. Wijaya1, and M. Awaluddin1  \n1Department of Geodetic Engineering, Faculty of Engineering, Diponegoro University, Indonesia  \nSubmit: 2024-03-10.  \nReceived: 2024-04-02  \nAccepted: 2024-07-25  \nPublished: 2024-10-03  \nKey words: flood risk, machine learning, dataset, hyperparameter.  \nCorrespondent email:  \n[bdyuwono92@gmail.com](bdyuwono92@gmail.com)  \nAbstract Semarang City is facing significant environmental challenges, with land subsidence being a critical issue that intensifies flood inundation and worsening flood damage. As urban areas expand and climate change impacts become more pronounced, understanding and mitigating flood risks are crucial for sustainable urban development and disaster management. Therefore, this study aimed to assess flood risk induced by land subsidence using machine learning to improve flood management. Five different machine learning models (MLMs) were used to assess flood risk, which included Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) . Additionally, fourteen different indices and 2884 sample points were used to train and test the models, with hyperparameter optimization ensuring fairness in comparisons. To address uncertainty in the sample dataset, flood hot spots were used to validate the rationality of flood risk zoning maps. The study investigated driving factors of different flood risk levels, focusing on flood areas to determine flood risk mechanisms in the highest-risk areas. The results showed that KNN performed the best and provided the most reasonable flood risk value among the models. Meanwhile, curve number (CN), distance to the river (DTRiver), and Building Density (BD) were identified as the top three significant factors of flood risk, ranked using the average score decrease in KNN model. Finally, this study expanded the application of machine learning for flood risk assessment and also deepened understanding of the potential mechanisms of flood risk, and provided perceptions about better flood risk management.  \n©2024 by the authors. Licensee Indonesian Journal of Geography, Indonesia.  \nThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY NC) license[https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) .  \n1. Introduction  \nSemarang is a city located on the north of Java Island and faces significant challenges due to its vulnerability to flooding. The population of this city is 1.7 million, with several individuals living in lowland areas that are prone to flooding. The impacts of flooding are very severe, including property damage, loss of life, and disruption of essential services. Asa major economic center in Central Java, Semarang plays a significant role in commercial, industrial, and agricultural sectors. However, this economic importance is threatened by recurring flooding leading to infrastructure damage, loss of crops, and transportation disruption. The city is currently experiencing significant urbanization, with the occurrence of new residential and commercial developments in floodprone areas. Therefore, to ensure new development is planned and constructed to be flood-resilient, there is a need for a comprehensive understanding of flood risk (Yuwono et al., 2021) .  \nLand subsidence refers to the sinking of land, which can be caused by various factors such as groundwater pumping, natural geological processes, and human activities such as urba","cbCaik090ZePUd8R","https://ap.wps.com/l/cbCaik090ZePUd8R","pdf",1918952,1,11,"English","en",105,"# Introduction\n## Land subsidence and flooding risk in Semarang\n## Methods for flood risk assessment: HDMS, SSA, MCDA, and machine learning\n# Study design (models, indices, and data)\n## Machine learning models compared\n## Indices and sample dataset\n## Hyperparameter optimization and uncertainty handling\n# Results and discussion\n## Best-performing model\n## Key driving factors and flood risk mechanisms\n# Conclusion","[{\"question\":\"What problem does this research focus on in Semarang City?\",\"answer\":\"It focuses on how land subsidence intensifies flood inundation and increases flood damage in Semarang’s vulnerable areas.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"The study compares Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF).\"},{\"question\":\"What factors are identified as most significant for flood risk?\",\"answer\":\"Curve number (CN), distance to the river (DTRiver), and building density (BD) are identified as the top three factors using an average score decrease in the KNN model.\"}]","Assessment of Flood Risk Induced by Land Subsidence Using Machine Learning | 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problem does this research focus on in Semarang City?","Question",{"text":75,"@type":76},"It focuses on how land subsidence intensifies flood inundation and increases flood damage in Semarang’s vulnerable areas.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study?",{"text":80,"@type":76},"The study compares Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF).",{"name":82,"@type":73,"acceptedAnswer":83},"What factors are identified as most significant for flood risk?",{"text":84,"@type":76},"Curve number (CN), distance to the river (DTRiver), and building density (BD) are identified as the top three factors using an average score decrease in the KNN 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