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Now showing items 1-20 of 23
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An ensemble genetic programming approach to develop incipient sediment motion models in rectangular channels
(ELSEVIER, 2020)Assimilating unique features of genetic programming (GP) and gene expression programming (GEP), this study introduces a hybrid algorithm which results in promising incipient non-cohesive sediment motion models. The new ... -
Application of Soft Computing Techniques for Particle Froude Number Estimation in Sewer Pipes
(ASCE-AMER SOC CIVIL ENGINEERS, 2020)Sedimentation in sewer networks is a major problem in urban hydrology. In comparison to the well-known classic sediment transport models, this study investigates the capabilities of soft computing methods, including multigene ... -
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 ... -
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. -
Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes
(IRTCES, 2020)Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice. Hence, the limiting velocity should be determined to keep the channel bottom clean from sediment deposits. ... -
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 ... -
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 ... -
Developing novel hybrid models for estimation of daily soil temperature at various depths
(ELSEVIER, 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 ... -
Drought modeling using classic time series and hybrid wavelet-gene expression programming models
(ELSEVIER, 2020)The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e., SPEI-3, SPEI-6, and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end, two ... -
Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree
(GAZI UNIV, 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 ... -
Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020)Wind speed data are of particular importance in the design and management of wind power projects. In the current study, three types of linear time series models including autoregressive (AR), moving average (MA), and ... -
Experimental analysis for self-cleansing open channel design
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm
(ELSEVIER, 2020)In this study, Regression in the Reproducing Kernel Hilbert Space (RRKHS) technique which is a non-linear regression approach formulated in the reproducing kernel Hilbert space (RRKHS) is applied for rainfall-runoff (R-R) ... -
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 ... -
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, ... -
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 ...
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