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Toplam kayıt 13, listelenen: 1-10
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 ...
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 ...
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 ...
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) ...
Streamflow and sediment load prediction using linear genetic programming
(Uludağ University Journal of The Faculty of Engineering, 2018)
Daily flow and suspended sediment discharge are two major hydrological variables that affect rivers’ morphology and ecosystem, particularly during flood events. Artificial neural networks (ANNs) have been successfully used ...
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 ...
A novel fuzzy random forest model for meteorological drought classification and prediction in ungauged catchments
(Pure and Applied Geophysics, 2020)
This paper presents a new tree-based model, namely Fuzzy Random Forest (FRF), for one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) classification and prediction with a noteworthy application in ...
Chaos-based multigene genetic programming: a new hybrid strategy for river flow forecasting
(Elsevier, 2018)
Chaos theory is integrated with Multi-Gene Genetic Programming (MGGP) engine as a new hybrid model for river flow forecasting. This is to be referred to as Chaos-MGGP and its performance is tested using daily historic flow ...
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 ...