[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122603-en":3,"doc-seo-122603-105":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":20,"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},122603,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","An analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning","Worldwide water security is strained by population growth, rural–urban migration, climate variability, hydrological conditions, overabstraction of groundwater, and increased per-capita water use. Water security modeling supports safer water management and policy design, making intelligent decision methods increasingly important for areas requiring clean supplies. This study proposes an integrated interval type-2 fuzzy VIseKriterijumska Optimizcija I Kompromisno Resenje (IT2FVIKOR) combined with unsupervised machine learning to rank strategies and cluster polluted regions using Terengganu River datasets.","TYPE Original Research PUBLISHED 06 January 2023 DOI 10.3389/fenvs.2022.971129  \nOPEN ACCESS  \nEDITED BY  \nShiping Wen,  \nUniversity of Technology Sydney, Australia  \nREVIEWED BY  \nAnlu Zhang,  \nHuazhong Agricultural University, China Lei Jin,  \nXiamen University of Technology, China  \n*CORRESPONDENCE  \nNurnadiah Zamri,  \n [nadiahzamri@unisza.edu.my](nadiahzamri@unisza.edu.my)  \nSPECIALTY SECTION  \nThis article was submitted to Environmental Informatics and Remote Sensing,  \na section of the journal  \nFrontiers in Environmental Science  \nRECEIVED 16 June 2022  \nACCEPTED 09 November 2022  \nPUBLISHED 06 January 2023  \nCITATION  \nZamri N, Azman WNAW, Pairan MA, Abas SS and Gao M (2023), An analysis of finding the best strategies of water security for water source areas using an  \nintegrated IT2FVIKOR with machine learning.  \nFront. Environ. Sci. 10:971129 .  \ndoi: 10.3389/fenvs.2022.971129  \nCOPYRIGHT  \n© 2023 Zamri, Azman, Pairan, Abas and Gao. This is an open-access article distributed under the terms of the  \nCreative 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.  \nAn analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning  \nNurnadiah Zamri 1*, Wan Nur Amira Wan Azman 1, Mohamad Ammar Pairan 1, Siti Sabariah Abas 1 and Miaomiao Gao 2  \n1Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Besut Terengganu, Malaysia, 2Faculty of Biology Medicine and Health, School of Medical Sciences, University of Manchester, Manchester, United Kingdom  \nWorldwide, water security is adversely affected by factors such as population growth, rural–urban migration, climate, hydrological conditions, overabstraction of groundwater, and increased per-capita water use. Water security modeling is one of the key strategies to better manage water safety and develop appropriate policies to improve security. In view of the growing global demand for safe water, intelligent methods and algorithms must be developed. Therefore, this paper proposes an integrated interval type-2 Fuzzy VIseKriterijumska Optimizcija I Kompromisno Resenje (IT2FVIKOR) with unsupervised machine learning (ML) . This includes IT2FVIKOR for ranking and selecting a set of alternatives. Unsupervised machine learning includes hierarchical clustering, self-organizing map, and autoencoder for clustering, silhouette analysis and elbow method to find the most optimal cluster count, and finally Adjusted Rank Index (ARI) to find the best comparison within two clusters. This proposed integrated method can be divided into a two-phase fuzzy-machine learning-based framework to select the best water security strategies and categorize the polluted area using the water datasets from the Terengganu River, one of Malaysia ’s rivers. Phase 1 focuses on the IT2FVIKOR method to select five different strategies with five different criteria using five decision makers for finding the best water security strategies. Phase 2 continues the unsupervised machine learning where three different clustering algorithms, namely, hierarchical clustering, self-organizing map, and autoencoder, are used to cluster the polluted area in the Terengganu River. Silhouette analysis is applied along with the clustering algorithms to estimate the number of optimal clusters in a dataset. Then, the ARI is applied to find the best comparison within the original data with hierarchical clustering, self-organizing map, and autoencoder. Next, the elbow method is applied to double-confirm the best clusters for each clustering algorithm. Last, lists of polluted areas in each cluster are retrieved. Finally, this 2-phase fuz","cbCaioLr8WDISkfF","https://ap.wps.com/l/cbCaioLr8WDISkfF","pdf",3417631,1,25,"English","en",105,"# Introduction\n## Water security challenges and motivation\n## Related work and research direction\n# Proposed integrated IT2FVIKOR and machine learning framework\n## Phase 1: IT2FVIKOR strategy selection\n## Phase 2: unsupervised clustering of polluted areas\n## Cluster number estimation and comparison (ARI, silhouette, elbow)\n# Results and retrieval of polluted-area clusters\n# Conclusion","[{\"question\":\"What problem does the integrated IT2FVIKOR and machine learning framework address?\",\"answer\":\"It selects the best water security strategies and categorizes polluted areas by ranking alternatives with IT2FVIKOR and then clustering pollution patterns using unsupervised machine learning on Terengganu River datasets.\"},{\"question\":\"How does Phase 1 choose water security strategies?\",\"answer\":\"Phase 1 applies the IT2FVIKOR method to rank and select five strategies using five criteria and five decision makers.\"},{\"question\":\"How are polluted areas categorized in Phase 2?\",\"answer\":\"Phase 2 uses hierarchical clustering, self-organizing map, and autoencoder to cluster polluted areas, then applies silhouette analysis to estimate the optimal cluster count, ARI for comparison within two clusters, and the elbow method to confirm clusters before retrieving each cluster’s polluted-area list.\"}]","An analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning | 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