Abstract
Quantification of infiltration rate is a time-consuming process because of its variability and challenges in the accurate estimation of infiltration model parameters. In this study predictive equations for parameters of Horton, Kostiakov, Modified Kostiakov and Philip infiltration models were developed using basic soil-properties. The model-parameters were initially determined applying non-linear Levenberg Marquardt algorithm (LMA) on field-observed infiltration data and were subsequently determined by predictive equations developed after applying regression analysis to investigated soil-properties. Regression analysis was carried-out using stepwise-regression (SR) where all the measured soil-properties were used, and by applying principal component analysis (PCA) prior to multiple linear-regression for reducing number of predictors. The results revealed that developed equations using stepwise regression and the ones developed after applying PCA were able to explain 40- 78% and 10- 50% of variation respectively. The performance evaluation of developed regression equations at two information levels along with LMA for prediction of infiltration model-parameters was carried out by computing an overall performance index (OPI), which combines relative weight of different statistical indicators, namely, Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (ENS), Willmott’s Index of Agreement (W), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Performance evaluation revealed, LMA with highest OPI-value is most suitable to ascertain parameters of studied infiltration models. However, for selected models using parameters determined at two information levels, it was observed that there exists no significant difference in OPI-value of computed infiltration rates suggesting that equations developed after PCA can be used successfully for determination of infiltration model-parameters.
Performance evaluation of EPM and MPSIAC Models for determination of Erosion Status of Shahriari Watershed
