Abstract
The aim of this paper was to investigate the impact of the fly ash concentration on the infiltration process and to assess the potential of five soft computing techniques such as artificial neural network, Gaussian process, support vector machine (SVM), random forest, and M5P model tree and compare with two popular conventional models, SCS and Kostiakov mode, to estimate the cumulative infiltration of fly-ash-mixed soils. Laboratory experiment was carried out with the different combinations of the sand, clay, and fly ash by using mini disk infiltrometer. The combination consists of the different concentrations of sand (25–45%), clay (25–45%), and fly ash (10–50%). The total observation consists of the 138 field measurement. The cumulative infiltration increase with an increment in the concentration of the fly ash, but it decreases when fly ash concentration increases 40–50% in the soil. On the other hand, the cumulative infiltration increases with the decrease in the concentration of clay in samples. The predictive modeling technique, SVM with RBF kernel, is the best technique to predict the cumulative infiltration with minimum error. Results suggest that SVM with RBF kernel is the best-fit modeling technique among other soft computing techniques as well as conventional models to find the impact of fly ash on infiltration characteristics for the given combination of the sand, clay and fly ash.
Highlights
Infiltration is the vital property of the water
The estimation of the infiltration characteristics is useful to evaluate the performance of the hydrogeological investigations (Pedretti et al 2012)
Three most frequent performance evaluation parameters were used such as rootmean-squared error (RMSE), coefficient of correlation (R), Nash–Sutcliffe efficiency coefficient (NSE) in this study
Summary
Infiltration is the vital property of the water. It is the process in which surface water such as precipitation, flood, and snowfall percolate into the soil. National Institute of Technology, Hamirpur, Hamirpur, India system (Al-Azawi 1985; Bhave and Sreeja 2013). It separates the water into two parts: groundwater flow and surface flow (Singh et al 2018a). There are many parameters such as density, texture, and type of soil and moisture content that affect the infiltration process (Angelaki et al 2013). The estimation of the infiltration characteristics is useful to evaluate the performance of the hydrogeological investigations (Pedretti et al 2012)
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