Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Abstract

Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO3), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.

Assessing the Wind Power Potential in Naama, Algeria to Complement Solar Energy through Integrated Modeling of the Wind Resource and Turbine Wind Performance

“…It is home to a population of around 200,000 inhabitants. This arid region is characterized by hot and dry conditions prevailing during the summer months and mild, occasionally rainy winters typical of desert environments [18]. In terms of geographic positioning, it is strategically positioned on an elevated plateau averaging 800 m above sea level; Naama's geographical location contributes to its distinct climate and environmental conditions.…”

“…The landscape is predominantly flat and interspersed with sparse grasslands; it features desert regions, occasional low hills, and rocky outcrops. Due to the harsh climatic conditions, vegetation in the area is limited and primarily comprises shrubs and hardy plant species adapted to withstand drought [18]. Climatically speaking, in terms of climate classification, Naama falls under the BWh category of the Köppen system, which denotes a desert climate.…”

“…By fully leveraging these complementary natural resources, Naama could play a pivotal role in spearheading Algeria's transition to sustainable, renewable energy and diversifying its energy mix while cutting fossil fuel dependence. Thereby, Naama would meaningfully contribute to Algeria's energy matrix and sustainability objectives [18]. Situated in the strategic Naama region Figure 2, the Salhi Ahmed Naama University Center stands out for its exceptional experimental capabilities in renewable energy research.…”

Integrated management of groundwater quantity, physicochemical properties, and microbial quality in West Nile delta using a new MATLAB code and geographic information system mapping

“…The WQI has been calculated considering the Algerian drinking water quality standards to know the suitability of water for human consumption 30 . Derdour et al 31 also assessed water quality in arid regions. They monitored the status of groundwater quality based on hydrochemical parameters using artificial intelligence approaches, such as irrigation water quality index (IWQI), and k-nearest neighbours (KNN) classifiers in Matlab's classification learner toolbox.…”

Hydrochemical assessment of groundwater using multivariate statistical methods and water quality indices (WQIs)

“…Numerous techniques have been developed and utilized by researchers to study and characterize the spatiotemporal variation of groundwater physicochemical parameters, including multivariate statistics, geochemical modeling and water quality index (Barkat et al 2021(Barkat et al , 2022(Barkat et al , 2023Bouteraa et al 2019;Chabour et al 2021;Chen and Feng 2013;Foued et al 2017;Hamed et al 2022;Lekrine et al 2023;Mohamadi et al 2021;Derdour et al 2023).…”