[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 …
Flood prediction using machine learning models: Literature review
Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …
The research on the advancement of flood prediction models contributed to risk reduction …
Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
Monthly streamflow forecasting is required for short-and long-term water resources
management especially in extreme events such as flood and drought. Therefore, there is …
management especially in extreme events such as flood and drought. Therefore, there is …
Prediction of droughts over Pakistan using machine learning algorithms
Climate change has increased frequency, severity and areal extent of droughts across the
world in the last few decades magnifying their adverse impacts. Prediction of droughts is …
world in the last few decades magnifying their adverse impacts. Prediction of droughts is …
Employing machine learning algorithms for streamflow prediction: a case study of four river basins with different climatic zones in the United States
Streamflow estimation plays a significant role in water resources management, especially for
flood mitigation, drought warning, and reservoir operation. Hence, the current study …
flood mitigation, drought warning, and reservoir operation. Hence, the current study …
Simulation and forecasting of streamflows using machine learning models coupled with base flow separation
Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …
to the high number of interrelated hydrological processes. It is well-known that machine …
Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods
M Shafapour Tehrany, L Kumar… - … , Natural Hazards and …, 2019 - Taylor & Francis
Statistical methods are the most popular techniques to model and map flood-prone areas.
Although a wide range of statistical methods have been used, application of the statistical …
Although a wide range of statistical methods have been used, application of the statistical …
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for
prediction and forecasting in water resources and environmental engineering. However …
prediction and forecasting in water resources and environmental engineering. However …
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
H Yoon, SC Jun, Y Hyun, GO Bae, KK Lee - Journal of hydrology, 2011 - Elsevier
We have developed two nonlinear time-series models for predicting groundwater level
(GWL) fluctuations using artificial neural networks (ANNs) and support vector machines …
(GWL) fluctuations using artificial neural networks (ANNs) and support vector machines …
Machine learning prediction of corrosion rate of steel in carbonated cementitious mortars
Corrosion rate (ie, corrosion current density), a crucial kinetic parameter for predicting and
modeling service-life performance of reinforced concrete structures, can be estimated using …
modeling service-life performance of reinforced concrete structures, can be estimated using …