RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region

Kamel, Ammar Hatem and Afan, Haitham Abdulmohsin and Sherif, Mohsen and Ahmed, Ali Najah and El-Shafie, Ahmed (2021) RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region. Sustainable Computing-Informatics & Systems, 30. ISSN 2210-5379, DOI https://doi.org/10.1016/j.suscom.2021.100514.

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Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir' water system. In fact, the evaporation rate and more specifically from subsurface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model's input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively.

Item Type: Article
Uncontrolled Keywords: Prediction model; Sub-surface reservoir; Evaporation rate estimation; Arid region; Neural network model
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 21 Jul 2022 02:30
Last Modified: 21 Jul 2022 02:30
URI: http://eprints.um.edu.my/id/eprint/28081

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