Modelling and mapping of soil erosion risk based on GIS and PAP/RAC guidelines in the watershed of Tassaoute (Central High-Atlas, Morocco)

Morocco watersheds, which provided many ecosystem services necessary for the socio-economic life of rural communities, are experiencing significant change and environmental problems. Therefore, examining potential soil erosion considered a major problem in the Moroccan highlands is very important to prioritize high erosion severity areas. Keeping in view of the above aspects, the present study aimed to evaluate and map areas at risk of water erosion in the upstream Tassaoute watershed (central High Atlas, Morocco), using the Priority Action Program/Regional Activity Center (PAP/RAC) method associated with Geographic Information Systems (GIS) and remote sensing. The PAP/RAC approach consisted of integrating the natural factors that influence water erosion, namely slope, lithology, vegetation cover and land use. This method provided an accurate cartographic product that reflects the reality of the state of soil degradation and the qualitative assessment of erosion. The generated erosion risk map of the study area showed that the phenomenon of erosion threatens this basin, especially in the middle and downstream, such that 40% of the basin surface has significant erosion and the high and very high degree of erosion represented 27% of the total surface of the study area. These results therefore demonstrated the PAP/CAR model reliability in assessing and mapping of water erosion risks in the upstream Tassaoute basin

Robustness of Optimized Decision Tree-Based Machine Learning Models to Map Gully Erosion Vulnerability

Gully erosion is a worldwide threat with numerous environmental, social, and economic impacts. The purpose of this research is to evaluate the performance and robustness of six machine learning ensemble models based on the decision tree principle: Random Forest (RF), C5.0, XGBoost, treebag, Gradient Boosting Machines (GBMs) and Adaboost, in order to map and predict gully erosion-prone areas in a semi-arid mountain context. The first step was to prepare the inventory data, which consisted of 217 gully points. This database was then randomly subdivided into five percentages of Train/Test (50/50, 60/40, 70/30, 80/20, and 90/10) to assess the stability and robustness of the models. Furthermore, 17 geo-environmental variables were used as potential controlling factors, and several metrics were examined to evaluate the performance of the six models. The results revealed that all of the models used performed well in terms of predicting vulnerability to gully erosion. The C5.0 and RF models had the best prediction performance (AUC = 90.8 and AUC = 90.1, respectively). However, according to the random subdivisions of the database, these models exhibit small but noticeable instability, with high performance for the 80/20% and 70/30% subdivisions. This demonstrates the significance of database refining and the need to test various splitting data in order to ensure efficient and reliable output results

Assessing the performance of MCDM, statistical and machine learning ensemble models for gully sensitivity mapping in a semi-arid context

Gully erosion is a complex socio-environmental issue that has a negative influence on natural resources and has significant economic costs. This study examined the performance of two ensemble models based on multicriteria decision making (MCDM) analysis, analytic hierarchy process (AHP), weight of evidence (WoE) and random forest (RF) for spatiotemporal monitoring of gully erosion sensitivity (GES) from 1988 to 2019 as well as a projection for 2040 in a semi-arid area. The findings revealed that the vulnerable areas significantly raise between 1988 and 2040 (> 27% of the study area since 2019), in perfect alignment with a rapid deterioration of the vegetation cover (−16%), a general decrease in rainfall (−25% since 2019), and an increase in land surface temperature (LST) average (30°–37° approximatively). Finally, the area under curve (AUC) value revealed a high prediction performance for both developed models (AUC = 0.888 for WoE-RF and 0.886 for MCDM-WoE-AHP)