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Toplam kayıt 14, listelenen: 1-10
A genetic programming approach to forecast daily electricity demand
(Springer Nature Switzerland AG, 2019)
A number of recent researches have compared machine learning techniques to find more reliable approaches to solve variety of engineering problems. In the present study, capability of canonical genetic programming (GP) ...
An improved gene expression programming model for streamflow forecasting in intermittent streams
(Elsevier, 2018)
Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology. In this study, genetic algorithm (GA) was combined with gene expression programming (GEP) as a new hybrid model ...
Pareto-optimal MPSA-MGGP: a new gene-annealing model for monthly rainfall forecasting
(Elsevier, 2019)
Rainfall is considered the hardest weather variable to forecast, and its cause-effect relationships often cannot be expressed in simple or complex mathematical forms. This study introduces a novel hybrid model to month ...
Investigating the effect of hydroclimatological variables on Urmia Lake water level using wavelet coherence measure
(IWA Publishing, 2019)
In this paper, wavelet transform coherence is implemented to examine the impacts of hydroclimatological variables on water level fluctuations in two large saline lakes in the Middle East with a similar geographical location, ...
Emotional ANN (EANN): a new generation of neural networks for hydrological modeling in IoT
(Springer Nature Switzerland AG, 2019)
Emotional artificial neural network (EANN) is a cutting-edge artificial intelligence method that has been used by researchers in the engineering and medical sciences over the recent years. First introduced in the 1999s, ...
Multigene genetic programming for sediment transport modeling in sewers for conditions of non-deposition with a bed deposit
(Elsevier, 2018)
It is known that construction of large sewers based on consideration of flow with non-deposition without a bed deposit is not economical. Sewer design based on consideration of flow with non-deposition with a bed deposit ...
Genetic programming in water resources engineering: a state-of-the-art review
(Elsevier, 2018)
The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for automatic generation of computer programs. In recent decades, GP has been frequently applied on various kind of engineering problems and ...
Energy demand forecasting using deep learning
(Springer Nature Switzerland AG, 2019)
Our cities face non-stop growth in population and infrastructures and require more energy every day. Energy management is the key success for the smart cities concept since electricity is one of the essential resources ...
Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model
(Springer-Verlag, 2019)
A precise forecast of streamflow in intermittent rivers is of major difficulties and challenges in watershed management, particularly in arid and semiarid regions. The present research study introduces an ensemble gene ...
A hybrid support vector regression-firefly model for monthly rainfall forecasting
(Springer Berlin Heidelberg, 2019)
Long-term prediction of rainfalls is one of the most challenging tasks in stochastic hydrology owing to the highly random characteristics of rainfall events. In this paper, a novel approach is adopted to develop a hybrid ...