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

Head-cut gully erosion susceptibility mapping in semi-arid region using machine learning methods: insight from the high atlas, Morocco

Gully erosion has been identified in recent decades as a global threat to people and property. This problem also affects the socioeconomic stability of societies and therefore limits their sustainable development, as it impacts a nonrenewable resource on a human scale, namely, soil. The focus of this study is to evaluate the prediction performance of four machine learning (ML) models: Logistic Regression (LR), classification and regression tree (CART), Linear Discriminate Analysis (LDA), and the k-Nearest Neighbors (kNN), which are novel approaches in gully erosion modeling research, particularly in semi-arid regions with a mountainous character. 204 samples of erosion areas and 204 samples of non-erosion areas were collected through field surveys and high-resolution satellite images, and 17 significant factors were considered. The dataset cells of samples (70% for training and 30% for testing) were randomly prepared to assess the robustness of the different models. The functional relevance between soil erosion and effective factors was computed using the ML models. The ML models were evaluated using different metrics, including accuracy, the kappa coefficient. kNN is the ideal model for this study. The value of the AUC from ROC considering the testing datasets of KNN is 0.93; the remaining models are associated to ideal AUC and are similar to kNN in terms of values. The AUC values from ROC of GLM, LDA, and CART for testing datasets are 0.90, 0.91, and 0.84, respectively. The value of accuracy considering the validation datasets of LDA, CART, KNN, and GLM are 0.85, 0.82, 0.89, 0.84 respectively. The values of Kappa of LDA, CART, and GLM for testing datasets are 0.70, 0.65, and 0.68, respectively. ML models, in particular KNN, GLM, and LDA, have achieved outstanding results in terms of creating soil erosion susceptibility maps. The maps created with the most reliable models could be a useful tool for sustainable management, watershed conservation and prevention of soil and water losses.info:eu-repo/semantics/publishedVersio

Machine learning models for spatial prediction of groundwater potentiality in a large semi-arid mountainous region: application to the rherhaya watershed, High Atlas, Morocco

info:eu-repo/semantics/publishedVersio

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

peer reviewedWater availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with “1” indicating a high GWP and “0” indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models’ prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models

Transpressional tectonics in the Marrakech High Atlas: Insight by the geomorphic evolution of drainage basins

International audienceThe Ouzzelarh Massif extends across the Marrakech High Atlas (MHA) and forms the highest elevated mountain belt. To better understand the evolution of collision-related topography, we present the results of a geomorphological study in which elevation changes generated by reactivated pre-Alpine (Variscan and Triassic-Jurassic) faults drive a landscape evolution model. We aim to evaluate the relationship between the geometry of the drainage network and the main fault systems in this region. New insight into geomorphological changes in drainage patterns and related landforms is based on geological fieldwork combined with DEM analysis. To quantitatively measure landscape features we used several classical geomorphic indices (spacing ratio, hypsometric curves and integral, stream frequency drainage, stream length-gradient). The Ouzzelarh Massif is bounded to the north by the Tizi N'Test Fault Zone (TTFZ) and to the south by the Sour Fault Zone (SFZ). These faults delimit a pop-up structure. By using the above geomorphic parameters, we ascertained that the Ouzzelarh Massif is affected by a high spatial variability of uplift. The actual landscape of the Ouzzelarh Massif reveals remnants of an uplifted ancient erosional surface and the heterogeneity of exposed rocks in the range explaining the possibility that the topographic asymmetry between north and south flanks is due to differences in lithology-controlled resistance to erosion. Drainage, topography and fault pattern all concur to show uplifted rhomboidal-shaped blocks. It exhibits high stream frequency drainage and uplift in separate tectonically-uplifted blocks such as Jebel Toubkal which is characterized by asymmetric drainage basins. (C) 2011 Elsevier B.V. All rights reserved

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

Pleistocene fluviatile deposits in the Ourika drainage basin (Marrakech High Atlas, Morocco): indicators of climatic variations associated with base level change

(IF 0.77; Q2)International audienceAggradational packages of alluvial sediment are preserved in the Ourika river basin. Field mapping and DEM analysis allowed us to identify a model sequence of river terraces. We focus on sites where the study of sedimentary sections allow us to decipher the sedimentary evolution of the Ourika watershed. Quaternary evolution of the drainage basin is characterized by major phases of sediment accumulation and erosion, forming alluvial fans and cut-fill terraces. More intense rainfall events during the Middle Pleistocene Ourika drainage basin resulted in increased erosion and transport of sediment from the hillslopes into the trunk river. First, the cut-fill terraces near the sub-basins outlets are formed by a large-scale aggradation, followed by a main vertical incision and lateral erosion. Then, the second sedimentation period was probably a result of increased precipitation that caused landsliding in steep sub-basins. Finally, a last stage of incision in the Ourika Valley is linked to a base level lowering due to climatic fluctuations. We suggest that cyclic climatic fluctuations superimposed on a continuous uplift of the High Atlas are responsible for the generation of stepped terraces along the Ourika River. Sub-basins steep affected by erosion processes dominated by landslides rocky shallow were accompanied by debris flows along convex profiles at their downstream end and associated with steep knickpoints. We interpreted erosion of the Pleistocene deposits as the result of an autocyclic negative feedback such as exhaustion of the hillslope sediment stocks and the resulting increase of the relative capacity of the trunk stream to bring and transfer sediment towards the Ourika Valley

Landslide susceptibility mapping using GIS-based bivariate models in the Rif chain (northernmost Morocco)

The coastline between Tetouan and Bou Ahmed in the northernmost Rif of Morocco and its hinterland has become immensely hazardous due to frequent triggering of diversified landslides from last two decades. This paper describes the potential application of a set of multisource data and the GIS platform for zoning and identifying anomalous areas prone to landsliding and its associated landslide hazards. For this purpose, Information value (IV), Statistical index SI (Wi), Weighting factors (WF) and Evidential belief function (EBF) models have been used in this study. Eleven conditioning factors such as elevation, slope, aspect, curvature, shaded/relief, proximity to streams, proximity to faults, proximity to roads, land use, lithology, annual rainfall and an inventory of 905 unstable spots were used to develop the spatial database for landslides susceptibility mapping (LSM). The factors have been used after a test of multi-collinearity. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) methods were used for validation of the LSM. The AUC results showed good prediction accuracy for all models with a prediction rate of 78% (IV), 77% (SI), 73% (WF) and 70% (EBF) respectively. However, the results indicated that comparatively, the IV model followed by WI model is more precise and accurate for landslides susceptibility mapping than other models. According to the presented models, about 64% of the study area is located in high to very high landslide susceptible zone. The findings presented in this study are imperatively valuable especially wherein large development projects and land use planning activities are going on

Delineation of landslide susceptible zones using Frequency Ratio (FR) and Shannon Entropy (SE) models in northern Rif, Morocco

This study describes the findings of landslide susceptibility modelling and hazard analysis in the coastline Mediterranean between Tetouan-Bou Ahmed and its hinterlands North Morocco. The study was carried out using Frequency Ratio (FR) and Shannon Entropy (SE) models with the aid of GIS tools and remote sensing data sources supported by extensive field surveys. A methodology was developed for modelling and identifying the landslide susceptible zones and for generating an updated landslide inventory map to delineate the most sensitive landslide prone areas as well as to predict and reduce their impacts. For building these models, a total of 905 landslide incidences and eleven main landslide causative factors were used based on multi-collinearity diagnosis test. The validation of the model results showed good prediction ability (>76%) for both the models. However, the accuracy prediction indicated that the FR is about 3% more precise than SE model in landslide susceptibility delineation. Furthermore, more than 60% of the area was found as high risk zone that is predicted highly susceptible to the landsliding hazards under suitable triggering factors. The findings of this study constitutes a major and suitable database for local and national authorities for providing stratigies for landslide hazard mitigation and making better policies for sustainable development in the region. Sustainable adaptive solutions and measures are required to prevent the stability of this mountainous region which is under the impact of wide anthropogenic activities for developmental purposes