Document Type : Research Paper
Authors
1
Department of Irrigation and Reclamation Engineering, Faculty of Aqriculture, University of Tehran, Tehran, Iran
2
irrigation and reclamation Engineering, faculty of agricultural, Karaj, Iran.
Abstract
Accurate prediction of groundwater levels is of great importance in water resource management, especially in arid regions. This research, with the aim of replacing traditional models with machine learning methods, has examined two algorithms: Support Vector Machine and Random Forest for predicting groundwater levels. Modeling was conducted using 20 years of data on precipitation, air temperature, evaporation, groundwater extraction, and groundwater level as input variables. After examining the normality and correlation of the data, 70% of the data were used for training and 30% for testing. The evaluation results of the R², RMSE, MAE, and MSE metrics showed that the SVM-RBF algorithm had values of 0.57, 1.05, 0.61, and 1.11 in the training phase, and values of 0.74, 0.84, 0.61, and 0.71 in the testing phase, respectively. The RF algorithm, using all features, provided values of 0.85, 0.61, 0.44, and 0.37 in the training phase and 0.71, 0.93, 0.66, and 0.86 in the testing phase, and showed better performance due to its high accuracy and resistance to multicollinearity. Additionally, using the Permutation Feature Importance method, the number of input variables for the RF model was reduced from six to one, and its results, without a significant decrease in model accuracy, included values of 0.83, 0.66, 0.45, and 0.43 in the training phase and 0.71, 0.93, 0.66, and 0.86 in the testing phase. The research findings indicate that machine learning models, particularly the RF algorithm, can be asuitable alternative to traditional methods for predicting groundwater levels and managing water resources sustainably.
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