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Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts
(SPRINGER, 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 ...
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 ...
Sediment transport modeling in rigid boundary open channels using generalize structure of group method of data handling
(ELSEVIER, 2019)
Sediment transport in open channels has complicated nature and finding the analytical models applicable for channel design in practice is a quite difficult task. To this end, behind theoretical consideration of the open ...
Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models
(ELSEVIER, 2019)
Prediction of river flow as a fundamental source of hydrological information plays a crucial role in various fields of water projects. In this study, at first, the capabilities of two time series analysis approaches, namely ...
Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques
(IWA PUBLISHING, 2019)
Deposition of sediment is a vital economical and technical problem for design of sewers, urban drainage, irrigation channels and, in general, rigid boundary channels. In order to confine continuous sediment deposition, ...
Self-cleansing design of sewers: Definition of the optimum deposited bed thickness
(WILEY, 2019)
Sediment deposits may influence the performance of the sewer systems. Sediments are the main store of pollutants which causes sewer systems overflows. In order to prevent the deposition of sediment in sewer systems, ...
Decision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipes
(IWA PUBLISHING, 2019)
Sediment deposition in sewers and urban drainage systems has great effect on the hydraulic capacity of the channel. In this respect, the self-cleansing concept has been widely used for sewers and urban drainage systems ...
Regression models for sediment transport in tropical rivers
(Springer, 2021-05)
The investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to
predict the changes in rivers. The characteristics of rivers and sediment in the tropical ...
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.
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