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Clear-water scour depth prediction in long channel contractions: Application of new hybrid machine learning algorithms
(2021)
Scour depth prediction and its prevention is one of the most important issues in channel and waterway design. However the potential for advanced machine learning (ML) algorithms to provide models of scour depth has yet to ...
Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods
(Springer, 2021)
Owing to the nonlinear and non-stationary nature of the suspended sediment transport in rivers, suspended sediment concentration (SSC) modeling is a challenging task in environmental engineering. Investigation of SSC is ...
Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms
(Public Library of Science, 2021)
To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning ...
Electrical Energy Demand Prediction: A Comparison Between Genetic Programming And Decision Tree
(2020)
Several recent studies have used various data mining techniques to obtain accurate electrical energy demand forecasts in power supply systems. This paper, for the first time, compares the efficiency of the decision tree ...
Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach
(ELSEVIER, 2019)
River flow rates are important for water resources projects. Given this, the current study explored the use of autoregressive (AR) and moving average (MA) techniques as individual time series models and compared them to ...
Closure to the discussion of Ebtehaj et al. on "Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach"
(Elsevier, 2021)
In this closure, we respond to the comments of Ebtehaj et al. (2020), and also provide additional details regarding several features of our study.
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