| dc.description.abstract | Soil erosion is a critical environmental concern with profound implications for agricultural
productivity and natural resource sustainability. This research endeavors to evaluate
soil erosion within the Kalu River catchment in Sri Lanka, spanning the period from
2000 to 2020, using the Revised Universal Soil Loss Equation (RUSLE) and Artificial
Neural Network (ANN) models. The primary objectives are to quantify annual soil loss
and delineate the spatial distribution of soil erosion risk. The study reveals that the
K factor, LS factor, P factor, C factor, and R factor exert varying degrees of influence
on soil erosion. Through the application of an ANN model, accurate predictions of soil
erosion are achieved. However, for assessing soil erosion susceptibility in the specific
study area, the RUSLE model emerges as more effective. Additionally, the research inves tigates disparities in soil erosion across sub-catchments within the Kalu River catchment.
Results indicate that sub-catchment A10 experiences the highest soil erosion, while A4
exhibits the lowest erosion rates. Furthermore, the Landslide Frequency Ratio (LFR)
method is employed to establish a correlation between soil erosion hazard classes and
landslide frequency. By integrating LFR values, soil erosion rates, and land-use changes,
high-priority regions requiring soil conservation measures are identified. This study
underscores the significance of estimating soil erosion rates, creating hazard zonation
maps, and prioritizing areas for sustainable land management and soil conservation
practices. Additionally, it enhances soil erosion factor comprehension, offers valuable
insights for further research, and empowers policymakers, land-use planners, and farmers
in implementing Eco-friendly land-use practices. | en_US |
