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Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques
(TAYLOR & FRANCIS LTD, 2019)
This paper presents the results of a comparison between multivariate adaptive regression splines (MARS) and random forest (RF) techniques in pipe failure prediction in two water distribution networks. In this regard, pipe ...
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 ...
Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach
(Journal of Hydrology, 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 ...
Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques
(Urban Water Journal, 2019)
This paper presents the results of a comparison between multivariate adaptive regression splines (MARS) and random forest (RF) techniques in pipe failure prediction in two water distribution networks. In this regard, pipe ...
Sediment transport modeling in rigid boundary open channels using generalize structure of group method of data handling
(Journal of Hydrology, 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
(Journal of Hydrology, 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 ...
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