A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability

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.

Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system

Accurate prediction of river discharge is critical for a wide range of sectors, from human activities to environmental hazard management, especially in the face of increasing demand for water resources and climate change. To address this need, a multivariate model that incorporates both local and global data sources, including river and piezometer gauges, sea level, and climate parameters. By employing phase shift analysis, the model optimizes correlations between the target discharge and 12 parameters related to hydrologic and climatic systems, all sampled daily. In addition, a stacked LSTM - a more complex neural network architecture - is used to improve information extraction ability. Exploring river dynamics in the Loire-Bretagne basin and its surroundings, the investigation delves into predictions in daily time steps for one, three, and six months ahead. The resulting forecast features high accuracy and efficiency in predicting river discharge fluctuations, showcasing superior performance in forecasting drought periods over flood peaks. A detailed examination on data used highlights the significance of both local and global datasets in predicting river discharge, where the former dictates short-term predictions, while the latter drives long-range forecasts. Seasonally extended forecasting confirms a strong connection between the forecast leading time and the shift in data correlation, with lower correlation at a lag of 3 months due to seasonal changes affecting forecast quality, compensated by a higher correlation at a longer lag of 6 months. Such mutual effect in this multi-time-step forecasting improves the predictive quality of a six-month horizon, thus encourages progress in long-term prediction to a seasonal scale. The research establishes a practical foundation for effectively utilizing big data to leverage long-term forecasting of environmental dynamics.

Long-run forecasting surface and groundwater dynamics from intermittent observation data: An evaluation for 50 years

The accurate prediction of water dynamics is critical for operational water resource management. In this study, we propose a novel approach to perform long-term forecasts of daily water dynamics, including river levels, river discharges, and groundwater levels, with a lead time of 7-30 days. The approach is based on the state-of-the-art neural network, bidirectional long short-term memory (BiLSTM), to enhance the accuracy and consistency of dynamic predictions. The operation of this forecasting system relies on an in-situ database observed for over 50 years with records gauging in 19 rivers, the karst aquifer, the English Channel, and the meteorological network in Normandy, France. To address the problem of missing measurements and gauge installations over time, we developed an adaptive scheme in which the neural network is regularly adjusted and re-trained in response to changing inputs during a long operation. Advances in BiLSTM with extensive learning past-to-future and future-to-past further help to avoid time-lag calibration that simplifies data processing. The proposed approach provides high accuracy and consistent prediction for the three water dynamics within a similar accuracy range as an on-site observation, with approximately 3 % error in the measurement range for the 7 day-ahead predictions and 6 % error for the 30 d-ahead predictions. The system also effectively fills the gap in actual measurements and detects anomalies at gauges that can last for years. Working with multiple dynamics not only proves that the data-driven model is a unified approach but also reveals the impact of the physical background of the dynamics on the performance of their predictions. Groundwater undergoes a slow filtration process following a low-frequency fluctuation, favoring long-term prediction, which differs from other higher-frequency river dynamics. The physical nature drives the predictive performance even when using a data-driven model.