Groundwater Level Prediction Using a Hybrid Neural Network and Genetic Algorithm

This study suggests a hybrid model for groundwater level prediction that combines genetic algorithms (GAs) and artificial neural networks (ANNs). The model makes use of historical data on groundwater as well as meteorological variables including temperature, precipitation, and soil moisture content. To improve the neural network's parameters and increase prediction accuracy, genetic algorithms are used. With regard to groundwater level predictions, the suggested method performs admirably, offering insightful information for the sustainable use of water resources. The study emphasizes how machine learning and optimization methods can be combined to effectively address challenging environmental issues.