Comparative analysis of artificial intelligence techniques for the prediction of infiltration process

Abstract

Knowledge of the infiltration process is beneficial in designing and planning of irrigation networks, soil erosion, hydrologic design, and watershed management. In this study, the infiltration process was analyzed using predictive models of artificial neural network (ANN), multi-linear regression (MLR), Random Forest regression (RF), M5P tree, and their performances were compared with the empirical model: Kostiakov model. Field experimental data was implemented for training and testing the above models, and their outcomes were assessed with the help of suitable performance assessment parameters. These models were assessed using a field dataset containing 340 observations, out of which 70% were used for the training purpose and the remaining for the testing. The RF-based models perform better than other models with Nash-Sutcliffe model efficiency (NSE) equal to 0.9963 and 0.9904 for the training and testing stages, correspondingly. ANN, MLR, and M5P model also give a good prediction performance, but the Kostiakov model’s performance is inferior. Sensitivity investigation suggests that the parameters, cumulative time, and moisture content in the soil are the most influential parameters for assessing the cumulative infiltration of soil.

Keywords

• infiltration process
• artificial intelligence techniques
• kostiakov model
• nash-sutcliff efficiency
• multi-linear regression