Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia

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dc.contributor.author Azamathulla, H. M
dc.contributor.author Rathnayake, Upaka
dc.contributor.author Shatnawi, Ahmad
dc.date.accessioned 2018-09-29T23:06:24Z
dc.date.available 2018-09-29T23:06:24Z
dc.date.issued 2018-09-27
dc.identifier.citation Applied Water Science. 2018 Sep 27;8(6):184
dc.identifier.uri https://doi.org/10.1007/s13201-018-0831-6
dc.identifier.uri http://hdl.handle.net/2139/46003
dc.description.abstract Abstract Climate change is not a myth. There is enough evidence to showcase the impact of climate change. Town planners and authorities are looking for potential models to predict the climatic factors in advance. Being an agricultural area in Saudi Arabia, Tabuk region gets greater interest in developing such a model to predict the atmospheric temperature.Therefore, this paper presents two different studies based on artificial neural networks (ANNs) and gene expression programming (GEP) to predict the atmospheric temperature in Tabuk. Atmospheric pressure, rainfall, relative humidity and wind speed are used as the input variables in the developed models. Multilayer perceptron neural network model (ANN model), which is high in precession in producing results, is selected for this study. The GEP model that is based on evolutionary algorithms also produces highly accurate results in nonlinear models. However, the results show that the GEP model outperforms the ANN model in predicting atmospheric temperature in Tabuk region. The developed GEP-based model can be used by the town and country planers and agricultural personals. Graphical abstract
dc.title Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia
dc.type Journal Article
dc.date.updated 2018-09-29T23:06:25Z
dc.language.rfc3066 en
dc.rights.holder The Author(s)


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