Variation of USLE-K Soil Erodibility Factor and Its Estimation with Artificial Neural Network Approach in Semi-humid Environmental Condition

Abstract

Abstract Soil erosion is the most important soil degradation process threatening arid, semi-arid and semi-humid areas. In this current study, in order to determine the susceptibility of micro basin soils in Çorum province with semi-humid ecological conditions to erosion, some physico-chemical soil properties such as organic matter, sand, silt, clay, bulk density and hydraulic conductivity factors that closely affect soil erosion (USLE-K factor) were determined. For that aim, soil erodibility values were determined for soil samples taken from surface depth (0-20 cm) of the micro basin. In addition, ANN approach was used to estimate the availability of this parameter in similar ecological conditions and spatial distribution of erosion susceptibility maps for the current micro basin were produced with the results obtained. The neural network's input parameters included organic matter, bulk density, hydraulic conductivity, sand, silt, and clay. The output parameter chosen was erodibility. R2 values of 0.81509 for the test, 0.99 for the training, 0.95 for the validation value, and 0.99 for all values were achieved when taking into account the results of the artificial neural network study.

Keywords

• Artificial Neural Network
• Erodibility
• Soil
• K-Factor