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Contribution of Geomatics and Remote Sensing to Environmental Study in the Cretaceous Basin of Errachidia-Boudenib (Morocco)

Mehdaoui Radouan, Mili El-Mostafa, Ousmana Habiba, Said Brahim Ait, El Khamal Youssef, Aziz Hachem, El Fakir Rabia

Ecological Engineering & Environmental Technology

2023

Vol. 24, iss. 1

196--206

The present study aims at mapping areas vulnerable to water erosion based on the Priority Activity Program/Regional Activity Center (PAP/CAR) model guidelines, geomatics, remote sensing, and GIS in the Errachidia-Boudenib Cretaceous basin. This basin is located in south-eastern Morocco and covers an area of 13 000 km2, the basin is 320 km long and 75 km wide. The method of estimating water erosion is composed of three phases; a predictive phase consisting of a mapping of predisposing factors such as slope, substrate, and/or soils and vegetation cover, a descriptive phase based on the mapping of actual erosion, and an integration phase to arrive at the identification and evaluation of the erosion risk. The mapping of areas vulnerable to water erosion indicates that 70% of the studied basin has low erodibility and 22% is notable, while only 8% has high to very high erodibility. The areas most affected by degradation are located at the end of the basin and correspond to lands with steep slopes (>35%). Consequently, this study has allowed us to locate certain sectors and roads that may be affected by this type of erosion, namely the mountainous areas of the High Atlas and roads numbered R13, R601, R 703, and P7106.

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.