LANDSLIDE SUSCEPTIBILITY MAPPING IN THE MUNICIPALITY OF OUDKA, NORTHERN MOROCCO: A COMPARISON BETWEEN LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORKS MODELS

The Rif is among the areas of Morocco most susceptible to landslides, because of the existence of relatively young reliefs marked by a very important dynamics compared to other regions. These landslides are one of the most serious problems on many levels: social, economic and environmental. The increase in the frequency and impact of landslides over the past decade has demonstrated the need for an in-depth study of these phenomena, allowing the identification of areas susceptible to landslides. The main objective of this study is to identify the optimal method for the mapping of the area susceptible to landslides in municipality of Oudka. This area has been marked by the largest landslide in the region, caused by heavy rainfall in 2013. Two Statistical Methods i) Regression Logistics (LR) ii) Artificial Neural Networks (ANN), were used to create a landslide susceptibility map. The realization of this susceptibility map required, first, the mapping of old landslides by the aerial photography, the data of the geological map and by the data obtained using field surveys using GPS. A total of 105 landslides were mapped from these various sources. 50% of this database was used for model building and 50% for validation. Eight independent landslide factors are exploited to detect the most sensitive areas: altitude, slope, aspect, distance of faults, distance streams, distance from roads, lithology and vegetation index (NDVI). The results of the landslide susceptibility analysis were verified using success and prediction rates. The success rate (AUC&thinsp;=&thinsp;0.918) and the prediction rate (AUC&thinsp;=&thinsp;0.901) of the LR model is higher than that of the ANN model (success rate (AUC&thinsp;=&thinsp;0.886) and prediction rate (AUC&thinsp;=&thinsp;0.877). These results indicate that the Regression Logistic (LR) model is the best model for determining landslide susceptibility in the study area.</p

COMPARING SOIL MOISTURE FROM THE SMOS MISSION AND GLDAS OVER TWO MOROCCAN STATIONS

Soil moisture has been used in several environmental studies. Recent technologies have shown that soil moisture can be measured by a variety of remote sensing techniques, each method has its advantages and disadvantages. In this article we compared the soil moisture data from SMOS and GLDAS in two stations with different climate (SIDI SLIMANE station in northern Morocco and SMARA station in the south). At these two stations we analyzed the effect of precipitation on soil moisture. During year 2012, the results of this study show a correlation between GLDAS (0-10 cm), GLDAS (10-40 cm) and SMOS data at the SIDI SILIMANE station. The results show that at the SIDI SLIMANE station the correlation exists between the precipitation data of the month preceding the soil moisture measurements of GLDAS (0-10cm), GLDAS (10-40cm) and SMOS. At SMARA station we find a correlation only between GLDAS (10-40cm) and the previous month of precipitation measurement