Abstract
Soil erosion is currently one of the most discussed natural resource degradation phenomena in the world. The depletion of fertile soils and the degradation of terrestrial ecosystems are among the most fatal consequences of soil erosion. The severity of soil erosion results mainly from the dynamics of land use/land cover, hydro-climatic disturbances, and anthropogenic activities. This study aims to quantify and predict soil erosion and sediment yield in the wadi Saida watershed, NW of Algeria (642 km2). To estimate the erosion amount and the sediments yield for 2010, 2015, and 2020 with a projected perspective to 2025, we used the EPM model (Erosion Potential Model) in association with artificial neural networks based on multilayer perceptron (ANN-MLP) according to two approaches, one based on the LULC producing the EPMLULC model and the second based on the spatial distribution of normalized difference vegetation index NDVI producing the EPMNDVI model. The land use/land cover mapping of the watershed was carried out on a multi-temporal aspect using the MLH classification of Landsat products. Artificial Neural Networks by Cellular Automata (ANN-CA) was used to generate the projected map of LULC for 2025. The results show that in the learning, validation, and test phases, the model based on the NDVI presents excellent behavior (r = 0.98, RMSE = 20.17 m3/km2/year) for the three stages, while the model based on LULC shows quite a lower correlation and slightly bigger error than the previous model (r = 0.85, RMSE = 64.1 m3/km2/year). At the prediction stage, the model EPMNDVI presents less root mean square error between the calculated and estimated values than the RMSE from the model EPMLULC, with 48.45 m3/km2/year of difference between the two models.
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Data availability
Data used and generated are all presented in this study. Datasets are available only from the corresponding author on reasonable request.
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Cherif, K., Yahia, N., Bilal, B. et al. Erosion potential model-based ANN-MLP for the spatiotemporal modeling of soil erosion in wadi Saida watershed. Model. Earth Syst. Environ. 9, 3095–3117 (2023). https://doi.org/10.1007/s40808-022-01657-3
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DOI: https://doi.org/10.1007/s40808-022-01657-3