Assessing water quality of kazerun county in southwest Iran: Multi-analytical techniques, deterministic vs. probabilistic water quality index, geospatial analysis, fuzzy C-means clustering, and machine learning
{"title":"Assessing water quality of kazerun county in southwest Iran: Multi-analytical techniques, deterministic vs. probabilistic water quality index, geospatial analysis, fuzzy C-means clustering, and machine learning","authors":"","doi":"10.1016/j.gsd.2024.101336","DOIUrl":null,"url":null,"abstract":"<div><p>Water quality is critical to human health and the environment, especially in arid and semi-arid regions. Hence, the objectives of this study were to assess drinking water quality, identify critical parameters, investigate spatial patterns, and investigate accurate predictive models for the water quality index (WQI) in the Kazerun county in southwest Iran. To address this issue using deterministic and probabilistic WQI, correlation matrix, fuzzy C-Means (FCM) clustering, geostatistics, and adaptive network-based fuzzy inference system (ANFIS) with FIS generation by fuzzy C-Means (FCM-ANFIS) and sub-clustering (SC-ANFIS).Various software tools, including Excel, MATLAB, Python, and GIS were used to analyze groundwater data collected from 25 sampling sites. Water parameters, including pH, Cl<sup>−</sup>, SO<sub>4</sub><sup>−2</sup>, EC, NO<sub>3</sub><sup>−</sup>, NO<sub>2</sub><sup>−</sup>, Ca<sup>2+</sup>, Mg<sup>2+</sup>, and F<sup>−</sup>, were examined. The results showed that F<sup>−</sup> levels were within acceptable limits set by the US EPA, but about one-third of sites posed potential health risks based on WHO guidelines. In one-third of regions, the levels of Mg<sup>2+</sup> exceeded the recommended guidelines. In deterministic and probabilistic approaches, water quality was excellent in 68% and 81.3% of sites, respectively. Sobol sensitivity analysis identified SO<sub>4</sub><sup>−2</sup>> Mg<sup>2+</sup>>Cl<sup>−</sup> > EC > F<sup>−</sup> > NO<sub>3</sub><sup>−</sup> as significant WQI variables. Spearman correlation matrix shows substantial positive correlations between WQI and EC, F<sup>−</sup>, SO<sub>4</sub><sup>−2</sup>, Mg<sup>2+</sup>, and Cl<sup>−</sup> were shown by the Spearman correlation matrix. Based on the FCM results, the southeast and central sites (56% of sites) have similar water quality. In comparison, the northern and four central sites (28% of sites) have distinct regional features, and the southern sites (16% of sites) had unique water quality characteristics. Geostatistical analyses showed that pH had the most substantial local clustering, while SO<sub>4</sub><sup>−2</sup> had significant high-value clustering. Furthermore, hot spot research revealed specific sites with high pH, F<sup>−</sup>, NO<sub>3</sub><sup>−</sup>, and Cl<sup>−</sup> levels. The FCM-ANFIS model outperformed the SC-ANFIS model, emphasizing FCM clustering's importance in water quality forecasting accuracy.</p></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Groundwater for Sustainable Development","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352801X24002595","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Water quality is critical to human health and the environment, especially in arid and semi-arid regions. Hence, the objectives of this study were to assess drinking water quality, identify critical parameters, investigate spatial patterns, and investigate accurate predictive models for the water quality index (WQI) in the Kazerun county in southwest Iran. To address this issue using deterministic and probabilistic WQI, correlation matrix, fuzzy C-Means (FCM) clustering, geostatistics, and adaptive network-based fuzzy inference system (ANFIS) with FIS generation by fuzzy C-Means (FCM-ANFIS) and sub-clustering (SC-ANFIS).Various software tools, including Excel, MATLAB, Python, and GIS were used to analyze groundwater data collected from 25 sampling sites. Water parameters, including pH, Cl−, SO4−2, EC, NO3−, NO2−, Ca2+, Mg2+, and F−, were examined. The results showed that F− levels were within acceptable limits set by the US EPA, but about one-third of sites posed potential health risks based on WHO guidelines. In one-third of regions, the levels of Mg2+ exceeded the recommended guidelines. In deterministic and probabilistic approaches, water quality was excellent in 68% and 81.3% of sites, respectively. Sobol sensitivity analysis identified SO4−2> Mg2+>Cl− > EC > F− > NO3− as significant WQI variables. Spearman correlation matrix shows substantial positive correlations between WQI and EC, F−, SO4−2, Mg2+, and Cl− were shown by the Spearman correlation matrix. Based on the FCM results, the southeast and central sites (56% of sites) have similar water quality. In comparison, the northern and four central sites (28% of sites) have distinct regional features, and the southern sites (16% of sites) had unique water quality characteristics. Geostatistical analyses showed that pH had the most substantial local clustering, while SO4−2 had significant high-value clustering. Furthermore, hot spot research revealed specific sites with high pH, F−, NO3−, and Cl− levels. The FCM-ANFIS model outperformed the SC-ANFIS model, emphasizing FCM clustering's importance in water quality forecasting accuracy.
期刊介绍:
Groundwater for Sustainable Development is directed to different stakeholders and professionals, including government and non-governmental organizations, international funding agencies, universities, public water institutions, public health and other public/private sector professionals, and other relevant institutions. It is aimed at professionals, academics and students in the fields of disciplines such as: groundwater and its connection to surface hydrology and environment, soil sciences, engineering, ecology, microbiology, atmospheric sciences, analytical chemistry, hydro-engineering, water technology, environmental ethics, economics, public health, policy, as well as social sciences, legal disciplines, or any other area connected with water issues. The objectives of this journal are to facilitate: • The improvement of effective and sustainable management of water resources across the globe. • The improvement of human access to groundwater resources in adequate quantity and good quality. • The meeting of the increasing demand for drinking and irrigation water needed for food security to contribute to a social and economically sound human development. • The creation of a global inter- and multidisciplinary platform and forum to improve our understanding of groundwater resources and to advocate their effective and sustainable management and protection against contamination. • Interdisciplinary information exchange and to stimulate scientific research in the fields of groundwater related sciences and social and health sciences required to achieve the United Nations Millennium Development Goals for sustainable development.