| Popis: |
Evaluation of physical and quantitative data of soil erosion is crucial to the sustainable development of the environment. The extreme form of land degradation through different forms of erosion is one of the major problems in the sub-tropical monsoon-dominated region. In India, tackling soil erosion is one of the major geo-environmental issues for its environment. Thus, identifying soil erosion risk zones and taking preventative actions are vital for crop production management. Soil erosion is induced by climate change, topographic conditions, soil texture, agricultural systems, and land management. In this research, the soil erosion risk zones of Ratlam District was determined by employing the Geographic Information System (GIS), Revised Universal Soil Loss Equation (RUSLE), Analytic Hierarchy Process (AHP), and machine learning algorithms (Random Forest and Reduced Error Pruning (REP) tree). RUSLE measured the rainfall eosivity (R), soil erodibility (K), length of slope and steepness (LS), Land cover and management (C), and support practices (P) factors. Kappa statistic was used to configure model reliability and it was found that Random Forest and AHP have higher reliability than other models. About 14.73% (715.94 km2) of the study area has very low risk to soil erosion, with an average soil erosion rate of 0.00–7.00 × 103 kg/(hm2·a), while about 7.46% (362.52 km2) of the study area has very high risk to soil erosion, with an average soil erosion rate of 30.00 × 103–48.00 × 103 kg/(hm2·a). Slope, elevation, stream density, Stream Power Index (SPI), rainfall, and land use and land cover (LULC) all affect soil erosion. The current study could help the government and non-government agencies to employ developmental projects and policies accordingly. However, the outcomes of the present research also could be used to prevent, monitor, and control soil erosion in the study area by employing restoration measures. |
