Comparative Evaluation of Infiltration Models

Abstract

Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main purpose of this study is to compare infiltration models which are used to evaluate infiltration rates of Davood Rashid, Kelat and Honam in Iran. Field infiltration tests were carried out at sixteen different locations comprising of 155 observations by use of double ring infiltrometer. The potential of four conventional infiltration models (Kostiakov, Modified Kostiakov, Novel and Philip’s models) were evaluated by least–square fitting to observed infiltration data. Three statistical comparison criteria including coefficient of correlation (C.C), coefficient of determination (R2) and root mean square error (RMSE) were used to determine the best performing infiltration models. The novel infiltration model suggests improved performance out of other three models. Further a Multi-linear Regression (MLR) equation has been developed using field infiltration data and compare with Support Vector Machine and Gaussian Process based regression with two kernels (Pearson VII and radial basis) modeling. Results suggest that Pearson VII based SVM works well than other modeling approaches in estimating the infiltration rate of soils. Sensitivity analysis concludes that the parameter, time, plays the most significant role in the estimation of infiltration rate. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models.