An analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning

Aug 4, 2026

Upload by Jiven

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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

Introduction

Water security challenges and motivation

Related work and research direction

Proposed integrated IT2FVIKOR and machine learning framework

Phase 1: IT2FVIKOR strategy selection

Phase 2: unsupervised clustering of polluted areas

Cluster number estimation and comparison (ARI, silhouette, elbow)

Results and retrieval of polluted-area clusters

Conclusion

TYPE Original Research PUBLISHED 06 January 2023 DOI 10.3389/fenvs.2022.971129 OPEN ACCESS EDITED BY Shiping Wen, University of Technology Sydney, Australia REVIEWED BY Anlu Zhang, Huazhong Agricultural University, China Lei Jin, Xiamen University of Technology, China *CORRESPONDENCE Nurnadiah Zamri, [nadiahzamri@unisza.edu.my](nadiahzamri@unisza.edu.my) SPECIALTY SECTION This article was submitted to Environmental Informatics and Remote Sensing, a section of the journal Frontiers in Environmental Science RECEIVED 16 June 2022 ACCEPTED 09 November 2022 PUBLISHED 06 January 2023 CITATION Zamri 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 integrated IT2FVIKOR with machine learning. Front. Environ. Sci. 10:971129 . doi: 10.3389/fenvs.2022.971129 COPYRIGHT © 2023 Zamri, Azman, Pairan, Abas and Gao. 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. An analysis of ﬁnding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning Nurnadiah Zamri 1*, Wan Nur Amira Wan Azman 1, Mohamad Ammar Pairan 1, Siti Sabariah Abas 1 and Miaomiao Gao 2 1Faculty 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 Worldwide, 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

FAQs

What problem does the integrated IT2FVIKOR and machine learning framework address?

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.

How does Phase 1 choose water security strategies?

Phase 1 applies the IT2FVIKOR method to rank and select five strategies using five criteria and five decision makers.

How are polluted areas categorized in Phase 2?

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.

Aug 4, 2026

Upload by Jiven