Novel approach for predicting groundwater storage loss using machine learning.

Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major groundwater aquifers are in a critical state. For this purpose, we introduce a novel approach using Artificial Intelligence (AI) and machine learning (ML). The methodology involves water budget variables that are easily accessible such as aquifer area, storage coefficient, groundwater use, return flow, discharge, and recharge. The GSL was calculated for 178 major aquifers of Iran using different combinations of input data. Out of 11 investigated variables, agricultural water consumption, aquifer area, river infiltration, and artificial drainage were highly associated to GSL with a correlation of 0.84, 0.79, 0.70, and 0.69, respectively. For the final model, 9 out of the totally 11 investigated variables were chosen for prediction of GSL. Results showed that ML methods are efficient in discriminating between different input variables for reliable GSL estimation. The Harris Hawks Optimization Adaptive Neuro-Fuzzy Inference System (HHO-ANFIS) and the Least-Squares Support Vector Machine (LS-SVM) gave best results. Overall, however, the HHO-ANFIS was most efficient to predict GSL. AI and ML methods can thus, save time and costs for these complex calculations and point at the most efficient data inputs. The suggested methodology is especially suited for data-scarce regions with a great deal of uncertainty and a lack of reliable observations of groundwater levels and pumping.

The prediction of aquifer groundwater level based on spatial clustering approach using machine learning.

Water resources management requires a proper understanding of the status of available and exploitable water. One of the useful management tools is the use of simulation models that are highly efficient in spite of the complex problems in the groundwater sector. In the present study, three data-based models, namely, group method of data handling (GMDH), Bayesian network (BN), and artificial neural network (ANN), have been investigated to simulate the groundwater levels and assess the quantitative status of aquifers. Five observation wells were selected in Birjand aquifer using spatial clustering to analyze and evaluate the aquifer. To determine the effective variables in predicting groundwater level, 10 scenarios were developed by combining several variables, including groundwater level in the previous month, aquifer exploitation, surface recharge, precipitation, temperature, and evaporation. Results showed that the GMDH model with three input variables, i.e., the groundwater level in the previous month, aquifer exploitation, and precipitation, had the highest prediction performance, RMSE, NASH, MAPE, and R2 of which were obtained equal to 0.074, 0.97, 0.0037, and 0.97, respectively. Furthermore, Taylor's diagram showed that the predicted values using the GMDH model had the highest correlation with the observational data. Hydrograph simulation was performed for 6 years to analyze the condition of the aquifer. The results showed that the groundwater level is in critical condition in this aquifer, and a 1.2-m groundwater loss was predicted for this aquifer. The findings of this study show that the management of the studied aquifer is necessary to improve its current situation.