Abstract
ABSTRACT In this study, predicting ability of support vector machines (SVM), Gaussian process (GP), artificial neural network (ANN), and Random forests (RF) based regression approaches was tested on the infiltration data of soil samples having different compositions of sand, silt, clay, and fly ash. In addition to this, their performances were compared with the Kostiakov model (KM) and Philip’s model (PM). Dataset containing a total of 392 observations was collected from the experimental measurements of soil infiltration rate on different soil samples. Out of the total dataset, 272 recordings were randomly selected for training and the residual 120 observations were selected for validation of the developed models. Standard statistical parameters were used to measure the predicting ability of various developed models. The result suggests that the best performance could be achieved by Polynomial kernel function-based GP regression (GP_Poly) with coefficient of correlation values as 0.9824, 0.9863, Bias values as 0.0006, −2.3542, root-mean-square error values as 47.7336, 40.3026, and Nash Sutcliffe model efficiency values as 0.9655, 0.9727 using training and testing dataset, respectively. Furthermore, time is found as the most influencing input variable for predicting the infiltration rate when GP_Poly-based model is used to predict the infiltration rate.
Highlights
Infiltration plays a very important part in hydrology related to above and under the surface of the earth as well as irrigation; it has earned an enormous consideration from hydrologists
It indicates that the performances of Kostiakov model and Philip’s model are unsatisfactory
A lot of research has been conducted in the field as well as in the laboratory pertaining to the estimation of infiltration characteristics, viz., hydraulic conductivity, cumulative infiltration and infiltration rate of soil, recharging rate, and permeability of soil
Summary
Infiltration plays a very important part in hydrology related to above and under the surface of the earth as well as irrigation; it has earned an enormous consideration from hydrologists. A noticeable number of infiltration models have been developed for the estimation of infiltration rate and they can be further classified into subcategories (Mishra, Tyagi, & Singh, 2003): (i) physical model, (ii) semi-empirical model, and (iii) empirical model. Physical and semi-empirical models depend on the derived laws and the equations. Smith (1972), Green and Ampt (1911), Horton (1938), Holtan (1961) are their examples. Empirical model is dependent on the field and laboratory experimental data. Modified Kostiakov (Smith, 1972), Soil conservation service (1972) are the examples of empirical models. There are numerous infiltration models but still, their fitness is not clear for the actual world conditions
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