[HTML][HTML] Groundwater level prediction using machine learning models: A comprehensive review
H Tao, MM Hameed, HA Marhoon… - Neurocomputing, 2022 - Elsevier
Developing accurate soft computing methods for groundwater level (GWL) forecasting is
essential for enhancing the planning and management of water resources. Over the past two …
essential for enhancing the planning and management of water resources. Over the past two …
A review of the artificial intelligence methods in groundwater level modeling
This study is a review to the special issue on artificial intelligence (AI) methods for
groundwater level (GWL) modeling and forecasting, and presents a brief overview of the …
groundwater level (GWL) modeling and forecasting, and presents a brief overview of the …
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear …
It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate
and reliable groundwater level forecasts, which are an important tool for sustainable …
and reliable groundwater level forecasts, which are an important tool for sustainable …
Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting
This article explores the suitability of a long short-term memory recurrent neural network
(LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The …
(LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The …
Machine learning in geo-and environmental sciences: From small to large scale
In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
patterns, and predicting intricate variables have been made. One efficient way of analyzing …
Machine learning for hydrologic sciences: An introductory overview
The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network
F Di Nunno, F Granata - Environmental Research, 2020 - Elsevier
In the Mediterranean area, the high water demand frequently leads to an excessive
exploitation of the water resource, which involves a qualitative degradation of the …
exploitation of the water resource, which involves a qualitative degradation of the …
Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms
Groundwater level (GWL) forecasting is crucial for irrigation scheduling, water supply and
land development. Machine learning (ML)(eg, artificial neural networks) has been …
land development. Machine learning (ML)(eg, artificial neural networks) has been …
Simulation of regional groundwater levels in arid regions using interpretable machine learning models
Regional groundwater level forecasting is critical to water resource management, especially
for arid regions which require effective management of groundwater resources to meet …
for arid regions which require effective management of groundwater resources to meet …
Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model
Large-scale reservoirs play an essential role in water resources management for agriculture
irrigation, water supply and flood controls. However, we need robust reservoir operation …
irrigation, water supply and flood controls. However, we need robust reservoir operation …