[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 …

A transdisciplinary review of deep learning research and its relevance for water resources scientists

C Shen - Water Resources Research, 2018 - Wiley Online Library
Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …

A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning

Z Xiang, J Yan, I Demir - Water resources research, 2020 - Wiley Online Library
Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still
room for improvement, researchers have been developing physical and machine learning …

Long lead-time daily and monthly streamflow forecasting using machine learning methods

M Cheng, F Fang, T Kinouchi, IM Navon, CC Pain - Journal of Hydrology, 2020 - Elsevier
Long lead-time streamflow forecasting is of great significance for water resources planning
and management in both the short and long terms. Despite of some studies using machine …

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

Y Guo, Y Zhang, L Zhang… - Wiley Interdisciplinary …, 2021 - Wiley Online Library
Runoff prediction in ungauged and scarcely gauged catchments is a key research field in
surface water hydrology. There have been numerous studies before and since the launch of …

Seasonal drought prediction: Advances, challenges, and future prospects

Z Hao, VP Singh, Y Xia - Reviews of Geophysics, 2018 - Wiley Online Library
Drought prediction is of critical importance to early warning for drought managements. This
review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid …

Forecast methods for time series data: a survey

Z Liu, Z Zhu, J Gao, C Xu - Ieee Access, 2021 - ieeexplore.ieee.org
Research on forecasting methods of time series data has become one of the hot spots. More
and more time series data are produced in various fields. It provides data for the research of …

Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management

W Niu, Z Feng - Sustainable Cities and Society, 2021 - Elsevier
Accurate runoff forecasting plays an important role in guaranteeing the sustainable
utilization and management of water resources. Artificial intelligence methods can provide …

Improving AI system awareness of geoscience knowledge: Symbiotic integration of physical approaches and deep learning

S Jiang, Y Zheng, D Solomatine - Geophysical Research …, 2020 - Wiley Online Library
Modeling dynamic geophysical phenomena is at the core of Earth and environmental
studies. The geoscientific community relying mainly on physical representations may want to …

Application of machine learning to predict CO2 trapping performance in deep saline aquifers

HV Thanh, KK Lee - Energy, 2022 - Elsevier
Deep saline formations are considered potential sites for geological carbon storage. To
better understand the CO 2 trapping mechanism in saline aquifers, it is necessary to develop …