Modelling of impact of water quality on infiltration rate of soil by random forest regression

In this paper, Infiltration rate of the soil is investigated by using predictive models of Random forest regression and their performance were compared with Artificial neural network (ANN) and M5P model tree techniques. A dataset consists of 132 field measurements were used. Out of 132 observations randomly selected 88 observations were used for training, whereas remaining 44 were used for testing the model. Input variables consist of cumulative time (Tf), type of impurities (It), concentration of impurities (Ci), and moisture content (Wc) whereas the infiltration rate was considered as output. Correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE) and root relative square error (RRSE) were considered to compare the performance the both modelling approaches. The result of evolution suggests that Random forest regression approach works well than the other two models (ANN and M5P model tree). The estimated value of infiltration rate using Random forest regression lies within ±25% error lines. Sensitivity analysis suggests that cumulative time is an important parameter for predicting the infiltration rate of the soil.