Groundwater level forecasting with machine learning models: A review.

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.

Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system.

A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.