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This study breaks new ground by developing a multi-hazard vulnerability map for the Tensift watershed and the Haouz plain in the Moroccan High Atlas area. The unique juxtaposition of flat and mountainous terrain in this area increases sensitivity to natural hazards, making it an ideal location for this research. Previous extreme events in this region have underscored the urgent need for proactive mitigation strategies, especially as these hazards increasingly intersect with human activities, including agriculture and infrastructure development. In this study six advanced machine learning (ML) models were used to comprehensively assess the combined probability of three significant natural hazards: flooding, gully erosion, and landslides. These models rely on causal factors derived from reputable sources, including geology, topography, meteorology, human activities, and hydrology. The research's rigorous validation process, which includes metrics such as specificity, precision, sensitivity, and accuracy, underlines the robust performance of all six models. The validation process involved comparing the model's predictions with actual hazard occurrences over a specific period. According to the outcomes in terms of the area under curve (AUC), the XGBoost model emerged as the most predictive, with remarkable AUC values of 93.41% for landslides, 91.07% for gully erosion and 93.78% for flooding. Based on the overall findings of this study, a multi-hazard risk map was created using the relationship between flood risk, gully erosion, and landslides in a geographic information system (GIS) architecture. The innovative approach presented in this work, which combined ML algorithms with geographical data, demonstrates the power of these tools in sustainable land management and the protection of communities and their assets in the Moroccan High Atlas and regions with similar topographical, geological, and meteorological conditions that are vulnerable to the aforementioned risks.
This study breaks new ground by developing a multi-hazard vulnerability map for the Tensift watershed and the Haouz plain in the Moroccan High Atlas area. The unique juxtaposition of flat and mountainous terrain in this area increases sensitivity to natural hazards, making it an ideal location for this research. Previous extreme events in this region have underscored the urgent need for proactive mitigation strategies, especially as these hazards increasingly intersect with human activities, including agriculture and infrastructure development. In this study six advanced machine learning (ML) models were used to comprehensively assess the combined probability of three significant natural hazards: flooding, gully erosion, and landslides. These models rely on causal factors derived from reputable sources, including geology, topography, meteorology, human activities, and hydrology. The research's rigorous validation process, which includes metrics such as specificity, precision, sensitivity, and accuracy, underlines the robust performance of all six models. The validation process involved comparing the model's predictions with actual hazard occurrences over a specific period. According to the outcomes in terms of the area under curve (AUC), the XGBoost model emerged as the most predictive, with remarkable AUC values of 93.41% for landslides, 91.07% for gully erosion and 93.78% for flooding. Based on the overall findings of this study, a multi-hazard risk map was created using the relationship between flood risk, gully erosion, and landslides in a geographic information system (GIS) architecture. The innovative approach presented in this work, which combined ML algorithms with geographical data, demonstrates the power of these tools in sustainable land management and the protection of communities and their assets in the Moroccan High Atlas and regions with similar topographical, geological, and meteorological conditions that are vulnerable to the aforementioned risks.
The use of percent frequency-dependent magnetic susceptibility (χfd%) is well-established for detecting superparamagnetic (SP) components in fine-grained soils and sediments. This study employs χfd% as a direct indicator of pedogenetic processes in soils from the Moroccan Rif region. Three soil transects (T1, T2, and T3), each comprising four soil cores with depths reaching 100 to 120 cm, were sampled from distinct lithological formations within an area subject to moderate to intense erosion. A total of 272 soil samples were collected and analyzed using MS2 Bartington Instruments, providing values to calculate χfd% and identify ultrafine ferrimagnetic minerals (SP, < 0.03 μm). In Quaternary fluvial terraces (T1) soils, approximately 60% of the samples indicate a mixture of SP, multidomain (MD), and Single Stable Domain (SSD) magnetic grains, while 30% contained coarser MD grains. Only 10% of the samples exhibit predominantly superparamagnetic (SP) grains. Soils on marly substrates (T2) showed 90% of samples with a combination of SP, MD, and SSD, and just 10% had SP grains. In contrast, soils from Villafranchian sandy deposits displayed χfd% values exceeding 10% in over 50% of samples, indicating that almost all iron components consist of SP grains. Physico-chemical analyses of the soils in profiles T1, T2, and T3 reveal distinct characteristics, including variations in clay content, organic matter, nutrient levels, and proportions of free and total iron. These results are important for understanding soil evolution and pedogenesis, with profiles T1 and T3 showing advanced development marked by high mineral iron, clay, and organic matter content. In contrast, profile T2 reflects a weak stage, influencing nutrient availability and contributing to overall soil dynamics in the respective profiles. The results of this study suggest that magnetic susceptibilities in these samples primarily originate from pedogenetic sources, revealing significantly advanced pedogenesis compared to T1 and T2 soils. The findings of this study align with previous research on soil erosion and degradation in the region, demonstrating that soils developed on terraces and marly substrates are more degraded and less stable than those on sandy substrates. This study underscores the utility of magnetic susceptibility as a rapid and effective indicator for initial soil assessment and gauging the degree of pedogenesis.
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