Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques.

Journal: Marine pollution bulletin
PMID: 38925024

Abstract

Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl, Fe, Ca, Mg, Na, SO, Mn, HCO, K, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.

Authors

  • Mustafa El-Rawy
    Civil Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt; Civil Engineering Department, College of Engineering, Shaqra University, Dawadmi 11911, Saudi Arabia. Electronic address: mustafa.elrawy@mu.edu.eg.
  • Mohamed Wahba
    Civil Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt. Electronic address: m_wahba@mans.edu.eg.
  • Heba Fathi
    College of Design and Architecture, Jazan University, Jazan, Saudi Arabia. Electronic address: hamansour@jazanu.edu.sa.
  • Fahad Alshehri
    Abdullah Alrushaid Chair for Earth Science Remote Sensing Research, Geology and Geophysics Department, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia. Electronic address: Falshehria@ksu.edu.sa.
  • Fathy Abdalla
    Geology Department, Faculty of Science, South Valley University, 83523 Qena, Egypt; Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia. Electronic address: fabdalla@ksu.edu.sa.
  • Raafat M El Attar
    Geology Department, Faculty of Science, South Valley University, 83523 Qena, Egypt.

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