Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco

• Autorzy: El Azhari Kaoutar , Abdallaoui Badreddine , Dehbi Ali , Abdalloui Abdelaziz , Zineddine Hamid

Identyfikatory

DOI

10.24425/jwld.2022.141550

• Języki publikacji: EN

Abstrakty

EN

This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014. It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error (MSE) and a high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.

Słowa kluczowe

EN

artificial neural network; ANN; learning algorithm; multi-layer perceptron; MLP; modelling; Rabat-Kenitra; relative humidity

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2022
• Tom: no. 54
• Strony: 13--20
• Opis fizyczny: Bibliogr. 30 poz., mapa, tab., wykr.

Twórcy

autor

El Azhari Kaoutar

• Moulay Ismail University, Faculty of Sciences, Zitoune, 50000, Meknes, Morocco

• https://orcid.org/0000-0001-9309-0073

autor

Abdallaoui Badreddine

• University of Oxford, Mathematical Institute, Oxford, United Kingdom

autor

Dehbi Ali

• Moulay Ismail University, Faculty of Sciences, Zitoune, 50000, Meknes, Morocco

• https://orcid.org/0000-0003-1283-4179

autor

Abdalloui Abdelaziz

• Moulay Ismail University, Faculty of Sciences, Zitoune, 50000, Meknes, Morocco

• https://orcid.org/0000-0002-3778-7973

autor

Zineddine Hamid

• Moulay Ismail University, Faculty of Sciences, Zitoune, 50000, Meknes, Morocco

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).