| dc.description.abstract | Soil erosion is a significant environmental
concern that can have adverse effects on agricultural
productivity and natural resource sustainability. This
research focuses on assessing soil erosion in the Kalu
River catchment of Sri Lanka using the Revised Universal
Soil Loss Equation (RUSLE) and Artificial Neural
Network (ANN) models. The study aims to quantify yearly
soil loss between 2000 and 2020 and identify the spatial
pattern of soil erosion risk. The results of the study
indicate that the K factor, LS factor, P factor, C factor,
and R factor have varying levels of influence on soil
erosion. An ANN model is used to accurately predict soil
erosion, but the RUSLE model is found to be more
effective in evaluating soil erosion susceptibility in the
specific study area. The research also examines the
variation in soil erosion among sub-catchments within the
Kalu River catchment. Sub-catchment A10 exhibits the
highest soil erosion value, while A4 has the lowest. The
Landslide Frequency Ratio (LFR) is employed to establish
a correlation between soil erosion hazard classes and
landslide frequency. High-priority areas for soil
conservation measures are identified based on LFR values,
soil erosion rates, and land-use change. The findings
underscore the importance of estimating soil erosion rates,
creating soil erosion hazard zonation maps, and
prioritizing areas for soil conservation practices and
sustainable land management. Policymakers, land-use
planners, and farmers can utilize this research to make
informed decisions and promote sustainable land-use
practices. The study contributes to the understanding of
soil erosion factors and provides valuable insights for
future research in other regions. | en_US |
