首页|Reports from University of Larbi Ben MHidi Provide New Insights into Machine Lea rning (A Machine Learning Approach for Ruslebased Soil Erosion Modeling In Beni Haroun Dam Watershed, Northeast Algeria)
Reports from University of Larbi Ben MHidi Provide New Insights into Machine Lea rning (A Machine Learning Approach for Ruslebased Soil Erosion Modeling In Beni Haroun Dam Watershed, Northeast Algeria)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news originating from Oum El Bouaghi, Alge ria, by NewsRx correspondents, research stated, “The lack of soil erosion data a nd other information about watersheds continues to limit soil erosion modeling. To overcome these limitations, many researchers have turned to machine learning models to analyze and model the complex water erosion processes and integrate th em with empirical models.” Our news journalists obtained a quote from the research from the University of L arbi Ben MHidi, “The Beni Haroun dam watershed faces soil erosion due to specifi c geo-environmental settings and land practices. It poses serious threats to agr icultural and natural resource development. For these reasons, this study attemp ts to identify soil erosion susceptible zones using the Revised Universal Soil L oss Equation (RUSLE) using five key factors (rainfall erosivity, soil erodibilit y, topography, cover management and conservation practice factor) in GIS environ ment. Furthermore, we integrated the five RUSLE parameters and the model outputs into two machine learning (ML) algorithms, namely Random Forest (RF) and Random Tree (RT). The proposed models underwent training on 70% of the d ataset and were subsequently validated on the remaining 30%. Our re sults indicated that the most vulnerable to severe soil erosion was concentrated in northwest regions, in contrast to the southeastern regions, which most occup y low erosion and moderate erosion. RUSLE and RT-based RUSLE models yielded near ly identical results in classifying erosion severity,estimating the annual aver age soil erosion at 17.5 and 17.69 (t ha-1y-1), respectively. In contrast, the R andom Forest RF-based RUSLE model presented slightly divergent findings 23.89 (t ha-1y-1).”
Oum El BouaghiAlgeriaCyborgsEmergi ng TechnologiesMachine LearningUniversity of Larbi Ben Mhidi
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