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2 Ecological Engineering & Environmental Technology

2 2025

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Estimating erosion, sediment yield, and dam lifetime using revised universal soil loss equation and potential erosion model in the Chichaoua watershed and Boulaouane Dam, High Atlas, Morocco

Baiddah Abdeslam, Krimissa Samira, Namous Mustapha, Eloudi Hasna, Ismaili Maryem, Hajji Sonia, Aboutaib Fatima, Badreldin Nasem

Ecological Engineering & Environmental Technology

2025

Vol. 26, iss. 3

132--158

Soil erosion is a global challenge with significant environmental, social, and economic impacts. This study, conducted in the Chichaoua watershed, aims to quantify soil loss, investigate its causes, and evaluate its effects on the construction of the new Boulaouane dam. Two models were used to quantify potential soil losses: the Revised Universal Soil Loss Equation (RUSLE) and the potential erosion model (PEM). The results indicate an average annual loss of 10.03 t/ha/yr according to RUSLE, while the EPM provides a higher estimate of 27.53 t/ha/yr. These values, exceeding the tolerance threshold, indicate that the watershed substantially contributes to the downstream sediment load, which could impact the hydrological performance and lifespan of the Boulaouane dam. Furthermore, the spatial distribution of soil losses within the Chichaoua watershed is not homogeneous, a heterogeneity that can be explained by the physical characteristics of the study area. This observation highlights the critical need for implementing erosion control measures, especially in upstream areas. This study reveals the intense erosion impacting the Chichaoua watershed, which presents substantial challenges for the sustainable management of the region’s soil and water resources. It underscores the pressing necessity of implementing targeted erosion control strategies, particularly around key infrastructures like the Boulaouane dam.

Enhancing urban flood hazard assessment: A comparative analysis of frequency ratio and xgboost models for precision risk mapping

El Haou Mohamed, Ourribane Malika, Ismaili Maryem, Krimissa Samira, Namous Mustapha

Ecological Engineering & Environmental Technology

2025

Vol. 26, iss. 3

265--284

Urban flood hazard prediction should effectively balance accuracy and interpretability. This paper compares the performances of the Frequency Ratio method, a simple statistical technique, and XGBoost, a state-of-the-art machine learning algorithm for flood Hazard mapping in Beni Mellal (Morocco). The dataset was derived from preprocessed and standardized Sentinel-2 and Landsat 8 images, a Digital Elevation Model, and geological and soil maps. A flood inventory map was produced, it was then divided into training and testing subsets in the ratio of 70:30 for model calibration and validation, respectively. The FR method highlights key geographical variables such as slope, proximity to rivers, and vegetation indices to deliver rapid, interpretable flood risk assessments. In contrast, XGBoost captures complex, nonlinear relationships by integrating natural and anthropogenic factors for precise risk mapping. The results indicate that while FR is efficient for preliminary assessments in data-scarce environments, XGBoost significantly outperforms it in accuracy, reliability, and detailed hazard differentiation. XGBoost achieved an area under the curve (AUC) of 90.71% in testing datasets compared to 86.1% for FR. Flood distribution analysis showed that FR identified 21.3% of the study area as low-risk and 11.3% as very high-risk, suitable for broad evaluations. XGBoost, however, mapped 73.0% as very low-risk and 12.0% as very high-risk, making it valuable for resource-efficient interventions. This study highlights the complementary strengths of both approaches and advocates for integrating FR’s rapid insights with XGBoost’s precision. Together, they provide a robust framework for comprehensive flood hazard management in semi-arid regions, balancing strategic planning with localized interventions.