Artificial neural network and energy budget method to predict daily evaporation of Boudaroua reservoir (northern Morocco)

• Autorzy: En-nkhili Hicham , Nizar Imane , Igouzal Mohammed , Touazit Azzeddin , Youness Nizar , Etebaai Issam

Identyfikatory

DOI

10.24425/jwld.2023.145341

• Języki publikacji: EN

Abstrakty

EN

Evaporation is one of the main essential components of the hydrologic cycle. The study of this parameter has significant consequences for knowing reservoir level forecasts and water resource management. This study aimed to test the three artificial neural networks (feed-forward, Elman and nonlinear autoregressive network with exogenous inputs (NARX) models) and multiple linear regression to predict the rate of evaporation in the Boudaroua reservoir using the calculated values obtained from the energy budget method. The various combinations of meteorological data, including solar radiation, air temperature, relative humidity, and wind speed, are used for the training and testing of the model’s studies. The architecture that was finally chosen for three types of neural networks has the 4-10-1 structure, with contents of 4 neurons in the input layer, 10 neurons in the hidden layer and 1 neuron in the output layer. The calculated evaporation rate presents a typical annual cycle, with low values in winter and high values in summer. Moreover, air temperature and solar radiation were identified as meteorological variables that mostly influenced the rate of evaporation in this reservoir, with an annual average equal to 4.67 mm∙d^-1. The performance evaluation criteria, including the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE) approved that all the networks studied were valid for the simulation of evaporation rate and gave better results than the multiple linear regression (MLR) models in the study area.

Słowa kluczowe

EN

artificial neural network; Boudaroua reservoir; energy budget; evaporation rate; meteorological data

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2023
• Tom: no. 57
• Strony: 107--115
• Opis fizyczny: Bibliogr. 33 poz., rys., tab., wykr.

Twórcy

autor

En-nkhili Hicham

• Ibn Tofail University, Faculty of Science, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, University campus, B.P. 242, 14000 Kenitra, Morocco

• https://orcid.org/0000-0002-5514-9182

autor

Nizar Imane

• University Hassan II, Higher Normal School of Technical Education (ENSET), Computer Science, Artificial Intelligence and Cybersecurity (IIACS), Mohammedia, Casablanca, Morocco

• https://orcid.org/0000-0001-7943-7157

autor

Igouzal Mohammed

• Ibn Tofail University, Faculty of Science, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, University campus, B.P. 242, 14000 Kenitra, Morocco

• https://orcid.org/0000-0002-9659-3503

autor

Touazit Azzeddin

• Ibn Tofail University, Faculty of Science, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, University campus, B.P. 242, 14000 Kenitra, Morocco

• https://orcid.org/0000-0003-3926-0187

autor

Youness Nizar

• Ibn Tofail University, Faculty of Science, Department of Physics, Laboratory of Electronic Systems, Information Processing, Mechanics and Energy, University campus, B.P. 242, 14000 Kenitra, Morocco

• https://orcid.org/0000-0002-4889-3392

autor

Etebaai Issam

• Abdelmalek Essaadi University, Faculty of Science and Technique, Department of Earth and Environmental Sciences, Team of Applied Geosciences and Geological Engineering, Al Hoceima, Morocco

• https://orcid.org/0000-0001-5941-656X

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).