Estimation and inter-comparison of infiltration models

Abstract

Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main objective of the present work is estimation and inter-comparison of infiltration models which are used to evaluate the infiltration rates of National Institute of Technology (NIT)-campus in district of Kurukshetra, Haryana (India) and for this study, field infiltration tests were carried out at ten different locations comprising of 109 observations by use of double ring infiltrometer. The potential of three infiltration models (Kostiakov, Modified Kostiakov and US-Soil Conservation Service (SCS)) were evaluated by least-square fitting to observed infiltration data. Three statistical comparison criteria including maximum absolute error (MAE), Bias and root mean square error (RMSE) were used to determine the best performing infiltration models. In addition, a novel infiltration model was developed from field tests data using nonlinear regression modeling which suggests improved performance out of other three models. In case of nonexistence of observed infiltration data, this novel model can be used to artificially generate infiltration data for NIT campus.

Assessment of Infiltration Rate of Soil Using Empirical and Machine Learning‐Based Models

“…The Novel model represents the infiltration rate of soil with the highest accuracy, followed by the RF model. The Novel model proposed by Sihag et al (2017) was observed to work reasonably well in comparison to other aforesaid empirical models, while the RF model is better relative to ANFIS-based soft computing models. As can be inferred from Figure 11, the points simulated by the Novel and RF models precisely follow the path of the actual infiltration rate and ensure their suitability in approximating the infiltration rate of soil in the field.…”

“…As can be inferred from Figure 11, the points simulated by the Novel and RF models precisely follow the path of the actual infiltration rate and ensure their suitability in approximating the infiltration rate of soil in the field. The Novel model proposed by Sihag et al (2017) was observed to work reasonably well in comparison to other aforesaid empirical models, while the RF model is better relative to ANFIS-based soft computing models. Despite the better performance of the Novel model, knowledge of equation parameters respective to the location is necessary for their application, very similar to other empirical models (KM and MKM).…”

“…Parameter estimation is the main criterion for selection of any model over others. Sihag et al (2017) proposed a Novel model using the least square fitting method which gives a better estimation of the infiltration rate of soil compared to the other models. Dashtaki et al (2009), in their study, compared the performance of four infiltration models for different sites and observed that the Mezencev model is a more reasonable predictor for all the studied sites.…”

“…However, the modified Kostiakov model was observed to be fitted best for silty loam soils. Sihag et al (2017) proposed a Novel model using the least square fitting method which gives a better estimation of the infiltration rate of soil compared to the other models. Vand et al (2018) and Sihag et al (2018a) applied support vector machines, Gaussian process, gene expression programming and neural network approaches to field infiltration data of some locations of Iran and reported successful application of these modelling approaches.…”

Análise de Desempenho de Modelos de Infiltração Unidimensional de Água no Solo

“…Segundo Sihag et al (2017), os erros podem ser minimizados a partir do incremento temporal do experimento, uma vez que os modelos KO, KL e SCS têm melhor desempenho para simulações com tempos mais longos.…”