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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 ...
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 ...
Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques
(Water Science and Technology, 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, ...