MANAGEMENT OF AGRICULTURAL GROUNDWATER IN SUDAN: THE USE OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN KHARTOUM STATE

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Authors

  • Musaab Adam Abbakar MOHAMMED University of Miskolc
  • Sani I. ABBA King Fahd University of Petroleum and Minerals
  • Norbert Péter SZABÓ University of Miskolc
  • Péter SZŰCS University of Miskolc

Keywords:

Groundwater quality, Irrigation indices, machine learning, Nubian aquifer, Sudan

Abstract

This research aims to predict the irrigation indices of sodium adsorption ratio (SAR) and sodium percentage (Na%) using innovative machine learning (ML) techniques, including support vector regression (SVR) and Gaussian process regression (GPR). Thirty-seven groundwater samples were collected, and the primary investigation indicated that Ca-Mg-HCO3 and Na-HCO3 water types dominate the samples. The data is divided into two sets for training (70%) and validation (30%), and the models are tested with three statistical criteria, including mean square error (MSE), root mean square error (RMSE), and determination coefficient (R2). The GPR algorithm showed better performance in predicting SAR and Na% than SVR since it provided the lowest errors. The implemented approach proved efficient for the sustainable management of agricultural water.

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Published

2023-09-25

Issue

Vol. 11 No. 1 (2023)

Section

Articles