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Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction
(Springer, 2021-03)
Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the
service life of the downstream dam. To this end, estimation models are needed to compute ...
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.
Experimental analysis for self-cleansing open channel design
(Journal of Hydraulic Research, 2020)
Self-cleansing is a hydraulic design concept for drainage systems for mitigation of sediment deposition. Experimental studies in the literature have mostly been performed in circular channels. In this study, experiments ...
Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts
(Environmental Monitoring and Assessment, 2020)
It is well documented that standalone machine learning methods are not suitable for rainfall forecasting in long lead-time horizons. The task is more difficult in arid and semiarid regions. Addressing these issues, the ...
Developing novel hybrid models for estimation of daily soil temperature at various depths
(Soil and Tillage Research, 2020)
Estimation of soil temperature (ST) as one of the vital parameters of soil, which has an impact on many chemical and physical characteristics of soil, is of great importance in soil science. This study applies a time ...
Electrical energy demand prediction: A comparison between genetic programming and decision tree
(Gazi University Journal of Science, 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 ...
Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm
(Springer, 2021)
Lake water level changes are relatively sensitive to the climate-born events that rely on numerous phenomena, e.g., surface soil type, adjacent groundwater discharge, and hydrogeological situations. By incorporating the ...
Weighted instances handler wrapper and rotation forest-based hybrid algorithms for sediment transport modeling
(Elsevier, 2021)
Sediment transport modeling has been known as an essential issue and challenging task in water resources and environmental engineering. In order to minimize the adverse impacts of the continues sediment deposition that is ...
Discharge coefficient for vertical sluice gate under submerged condition using contraction and energy loss coefficients
(Elsevier, 2021)
A novel method is suggested for the determination of flow discharge in vertical sluice gates with considerably small bias. First, in order to derive an equation for the discharge coefficient, energy-momentum equations are ...
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 ...