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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 ...
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) ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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