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Neurocomputing in surface water hydrology and hydraulics: A review of two decades retrospective, current status and future prospects
Neurocomputing methods have contributed significantly to the advancement of modelling
techniques in surface water hydrology and hydraulics in the last couple of decades, primarily …
techniques in surface water hydrology and hydraulics in the last couple of decades, primarily …
A review on the application of machine learning methods in tropical cyclone forecasting
Z Wang, J Zhao, H Huang, X Wang - Frontiers in Earth Science, 2022 - frontiersin.org
At present, there is still a bottleneck in tropical cyclone (TC) forecasting due to its complex
dynamical mechanisms and various impact factors. Machine learning (ML) methods have …
dynamical mechanisms and various impact factors. Machine learning (ML) methods have …
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 …
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 …
Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation
River suspended sediment load (SSL) estimation is of importance in water resources
engineering and hydrological modeling. In this study, a novel hybrid approach is …
engineering and hydrological modeling. In this study, a novel hybrid approach is …
Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation
Among several components of the hydrology cycle, streamflow is one of the essential
process necessarily needed to be studied. The establishment of an accurate and reliable …
process necessarily needed to be studied. The establishment of an accurate and reliable …
River suspended sediment load prediction based on river discharge information: application of newly developed data mining models
Suspended sediment load (SSL) is one of the essential hydrological processes that affects
river engineering sustainability. Sediment has a major influence on the operation of dams …
river engineering sustainability. Sediment has a major influence on the operation of dams …
Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis
Ensemble machine learning models have been widely used in hydro-systems modeling as
robust prediction tools that combine multiple decision trees. In this study, three newly …
robust prediction tools that combine multiple decision trees. In this study, three newly …
Prediction of evaporation in arid and semi-arid regions: A comparative study using different machine learning models
Evaporation, one of the fundamental components of the hydrology cycle, is differently
influenced by various meteorological variables in different climatic regions. The accurate …
influenced by various meteorological variables in different climatic regions. The accurate …
[HTML][HTML] A comparison of performance of SWAT and machine learning models for predicting sediment load in a forested Basin, Northern Spain
In water bodies, sediment transport is a potential source of numerous negative effects on
water resource projects and can damage environmental services. Two machine learning …
water resource projects and can damage environmental services. Two machine learning …