Assessing the Impact of Land Use, Soil, and Slope on Sediment Production Rates Using Hydrosedimentological Modeling and Artificial Neural Networks
DOI:
https://doi.org/10.24021/raac.v23i1.8492Keywords:
Sensitivity of soil slope and land use, sediment production rate, modeling with SWATAbstract
Understanding the impacts of land use, soil type, and slope on sediment production is vital for water resource management and environmental conservation. This study uses hydrosedimentological modeling and Artificial Neural Networks (ANNs) to evaluate these relationships. The SWAT model was employed to simulate sediment production rates in Brazilian basins, considering hydrological, climatic, and topographic variables. ANN techniques were integrated to analyze the importance of land use, soil type, and slope. The results show that agriculture had the greatest impact on sedimentation rates (36.32%), followed by pastures (29.73%), non-vegetated areas (21.61%), and natural vegetation (12.34%). Soil types such as Neosol and Gleysol were the most influential (23.56% and 22.88%), while Latosol and Nitosol had the least impact (9.15% and 11.15%). Higher slopes (>45%) had a greater influence (29.32%) on sediment production. The model performance was evaluated by Kling-Gupta efficiency (KGE), percentage bias (PBIAS) and Pearson correlation coefficient (r), indicating satisfactory accuracy. The findings highlight the relevance of land use, soil and slope in sediment dynamics, providing support for sediment management and environmental conservation strategies.
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