{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:27:15Z","timestamp":1781108835750,"version":"3.54.1"},"reference-count":97,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,4]],"date-time":"2022-12-04T00:00:00Z","timestamp":1670112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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\u2019s 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 na\u00efve 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.<\/jats:p>","DOI":"10.3390\/ijgi11120606","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T05:31:32Z","timestamp":1670218292000},"page":"606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Novel Approach Based on Machine Learning and Public Engagement to Predict Water-Scarcity Risk in Urban Areas"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9463-5119","authenticated-orcid":false,"given":"Sadeq Khaleefah","family":"Hanoon","sequence":"first","affiliation":[{"name":"Civil Engineering Department, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Fikri","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Biological and Agricultural Engineering Department, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia"},{"name":"International Institute of Aquaculture and Aquatic Sciences (I-AQUAS), Universiti Putra Malaysia, Port Dickson 70150, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helmi Z. M.","family":"Shafri","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4650-8988","authenticated-orcid":false,"given":"Aimrun","family":"Wayayok","sequence":"additional","affiliation":[{"name":"Biological and Agricultural Engineering Department, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100192","DOI":"10.1016\/j.aosl.2022.100192","article-title":"Population increase impacts the climate, using the sensitive Arctic as an example","volume":"15","author":"Johannessen","year":"2022","journal-title":"Atmospheric Ocean. Sci. 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