A Novel Approach Based on Machine Learning and Public Engagement to Predict Water-Scarcity Risk in Urban Areas

Bibliographic Data

ID22033353
AuthorsSadeq Khaleefah Hanoon (0000-0001-9463-5119, Universiti Putra Malaysia, corresponding author), Ahmad Fikri Abdullah (0000-0003-3589-1459, Universiti Putra Malaysia), Helmi Zulhaidi Mohd Shafri (0000-0002-8669-874X, Universiti Putra Malaysia), Aimrun Wayayok (0000-0003-4650-8988, Universiti Putra Malaysia)
Year2022
Volume11
Issue12
Pages606
Publication date2022-12-04
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi11120606
OpenAlexW4311533050
LanguageEN
Citations received1
References cited94

Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world’s population now reside. Due to the complex urban fabric, conventional techniques should be developed to diagnose water shortage risk (WSR) by engaging crowdsourcing. This study aims to develop a novel approach based on public participation (PP) with a geographic information system coupled with machine learning (ML) in the urban water domain. The approach was used to detect (WSR) in two ways, namely, prediction using ML models directly and using the weighted linear combination (WLC) function in GIS. Five types of ML algorithm, namely, support vector machine (SVM), multilayer perceptron, K-nearest neighbour, random forest and naïve Bayes, were incorporated for this purpose. The Shapley additive explanation model was added to analyse the results. The Water Evolution and Planning system was also used to predict unmet water demand as a relevant criterion, which was aggregated with other criteria. The five algorithms that were used in this work indicated that diagnosing WSR using PP achieved good-to-perfect accuracy. In addition, the findings of the prediction process achieved high accuracy in the two proposed techniques. However, the weights of relevant criteria that were extracted by SVM achieved higher accuracy than the weights of the other four models. Furthermore, the average weights of the five models that were applied in the WLC technique increased the prediction accuracy of WSR. Although the uncertainty ratio was associated with the results, the novel approach interpreted the results clearly, supporting decision makers in the proactive exploration processes of urban WSR, to choose the appropriate alternatives at the right time

Agriculture · Artificial neural network · Data mining · Geography · Machine learning · Multilayer perceptron · Perceptron · Population · Support vector machine · Water scarcity · Computer Science · Flood Risk Assessment and Management · Land Use and Ecosystem Services · Water resources management and optimization · Artificial Intelligence

Tools

Open DOIOpen Access