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Hydrological Modeling and Impact of Climate Change on Water Resources in the Ziz Valley, Central High Atlas, Morocco

El Ouali Anas, Dichane Zouhir, Roubil Allal, El Ouardi Hmidou, El Hmaidi Abdellah, Lahrach Abderrahim

Ecological Engineering & Environmental Technology

2023

Vol. 24, iss. 6

192--210

The Upper Ziz basin located in the southeast of Morocco, has a total area of 4,351 km2. The surface water feeds El Hassan Addakhil dam, which insures water supply for the downstream cities of Errachidia, Rissani, Erfoud and others along the Ziz valley. This study aimed to evaluate the availability of water resources in this basin known by its arid climate and strong climatic changes. Several global hydrological models at different times were used to simulate the discharge at the outlet. The Statistical Downscaling Model (SDSM) method has been used to reduce the average rainfall and the temperature to predict future climate change related to various Representative Concentration Pathway (RCP) scenarios such as RCP 4.5 and RCP 8.5. The results of the hydrologic models are available, with an NSE of 0.8 for the monthly model during calibration and 0.77 at validation. Future precipitation shows an increasing trend in both scenarios. As for future mean temperature, it will recognize great seasonal variability, such as warming winter and spring and cooling summer and autumn. As a result, simulated future discharge will decrease by 26% under RCP 4.5 and by 24% under RCP 8.5 in the near future.

Weather drought index prediction using the support vector regression in the Ansegmir Watershed, Upper Moulouya, Morocco

Bekri My Hachem, El Hmaidi Abdellah, Ousmana Habiba, El Faleh El Mati, Berrada Mohamed, El Aissaoui Kamal, Essahlaoui Ali, El Ouali Abdelhadi

Journal of Water and Land Development

2021

no. 50

187--194

The purpose of this study is to develop mathematical models based on artificial intelligence: Models based on the support vectors regression (SVR) for drought forecast in the Ansegmir watershed (Upper Moulouya, Morocco). This study focuses on the prediction of the temporal aspect of the two drought indices (standardized precipitation index – SPI and standardized precipitation-evapotranspiration index – SPEI) using six hydro-climatic variables relating to the period 1979–2013. The model SVR3-SPI: RBF, ε = 0.004, C = 20 and γ = 1.7 for the index SPI, and the model SVR3-SPEI: RBF ε = 0.004, C = 40 and γ = 0.167 for the SPEI index are significantly better in comparison to other models SVR1, SVR2 and SVR4. The SVR model for the SPI index gave a correlation coefficient of R = 0.92, MSE = 0.17 and MAE = 0.329 for the learning phase and R = 0.90, MSE = 0.18 and MAE = 0.313 for the testing phase. As for the SPEI index, the overlay is slightly poorer only in the case of the SPI index between the observed values and the predicted ones by the SVR model. It shows a very small gap between the observed and predicted values. The correlation coefficients R = 0.88 for the learning, R = 0.86 for testing remain higher and corresponding to a quadratic error average MSE = 0.21 and MAE = 0.351 for the learning and MSE = 0.21 and MAE = 0.350 for the testing phase. The prediction of drought by SVR model remain useful and would be extremely important for drought risk management.

Artificial intelligence for supervised classification purposes: Case of the surface water quality in the Moulouya River, Morocco

Manssouri Imad, Talhaoui Abdelghani, El Hmaidi Abdellah, Boudad Brahim, Boudebbouz Bouchra, Sahbi Hassane

Journal of Water and Land Development

2021

no. 50

240--247

From a management perspective, water quality is determined by the desired end use. Water intended for leisure, drinking water, and the habitat of aquatic organisms requires higher levels of purity. In contrast, the quality standards of water used for hydraulic energy production are much less important. The main objective of this work is focused on the development of an evaluation system dealing with supervised classification of the physicochemical quality of the water surface in the Moulouya River through the use of artificial intelligence. A graphical interface under Matlab 2015 is presented. The latter makes it possible to create a classification model based on artificial neural networks of the multilayer perceptron type (ANN-MLP). Several configurations were tested during this study. The configuration [9 8 3] retained gives a coefficient of determination close to the unit with a minimum error value during the test phase. This study highlights the capacity of the classification model based on artificial neural networks of the multilayer perceptron type (ANN-MLP) proposed for the supervised classification of the different water quality classes, determined by the calculation of the system for assessing the quality of surface water (SEQ-water) at the level of the Moulouya River catchment area, with an overall classification rate equal to 98.5% and a classification rate during the test phase equal to 100%.