A Novel Hybrid Algorithms for Groundwater Level Prediction

Estimating groundwater levels (GWL) with accuracy and reliability, in order to maximize the use of water resources, it is crucial to reduce water consumption. To predict GWL in the Shabestar plain in the north-west of Iran, this case study developed a simulation-optimization hybrid model. For predicting GWL, the HBA (honey badger algorithm) optimizes parameters of ANNs (artificial neural networks) and SVRs (support vector regressions). Results were compared to ANN and SVR models. Datasets for periods of April 2001-March 2022 were utilized to develop and assess precision of the models. The average mutual information (AMI) is utilized to find out the combination of inputs for hybrid and standalone predictive models. In consideration of appropriate goodness-of-fit criteria, the predictive accuracy of models has been evaluated: correlation coefficient (R), root mean square error (RMSE), Nash-Sutcliffe model efficiency (NSE), mean absolute error (MAE), and Taylor diagram. Based on testing phase, the HBA-ANN model shows a very good agreement with the measured data (R = 0.999, RMSE(m) = 0.012, NSE = 0.999, MAE(m) = 0.012) followed by HBA-SVR (R = 0.999, RMSE(m) = 0.063, NSE = 0.977, MAE(m) = 0.046), SVR (R = 0.886, RMSE(m) = 0.245, NSE = 0.663, MAE(m) = 0.170) and ANN (R = 0.898, RMSE(m) = 0.272, NSE = 0.584, MAE(m) = 0.212). In conclusion, the HBA-ANN and HBA-SVR models can be used to forecast GWL based on outcomes of this study. Groundwater systems can be well estimated using such advanced AI techniques, saving resources, and labour conventionally employed.