AUTHOR=Zamri Nurnadiah , Azman Wan Nur Amira Wan , Pairan Mohamad Ammar , Abas Siti Sabariah , Gao Miaomiao 

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

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.971129

DOI=10.3389/fenvs.2022.971129

ISSN=2296-665X

ABSTRACT=Worldwide, water security is adversely affected by factors such as population growth, rural-urban migration, climate, and hydrological conditions, over abstraction of groundwater, and increased per-capita water use. Water security modelling 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 (IT2 FVIKOR) with unsupervised Machine Learning (ML). This include IT2FVIKOR for ranking and selecting a set of alternatives, unsupervised ML include Hierarchical Clustering, SOM 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 2-phase fuzzy-ML 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 river parts. Phase 1 focuses on the IT2FVIKOR method to select five different strategies with five different criteria using five DMs for finding the best water security strategies. Phase 2 continues the unsupervised ML where three different clustering algorithms Hierarchical Clustering, SOM 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, ARI is applied to find best comparing within original data with Hierarchical Clustering, SOM and Autoencoder. Next, Elbow method is applied to double confirm the best clusters for each clustering algorithm. Lastly, lists of polluted area in each cluster are retrieved. Finally, these 2-phase fuzzy-ML based offers an alternative intelligent model to solve the water security problems and find the most polluted area.