Estimation and inter-comparison of infiltration models
By Parveen Sihag, N. K. Tiwari, S. Ranjan. Water Science, 2017
95 citations recorded by the source corpus.
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.
Subfields: Civil and Structural Engineering, Soil Science, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/597123b4c62dfadefe2391dcc4ddd9a74321b484
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In Civil and Structural Engineering
In Soil Science
In Water Science and Technology
Citations in the corpus
- Support vector regression-based modeling of cumulative infiltration of sandy soil (2018, background): “…(GP) regression techniques have been emerged as powerful tools in water and environment engineering applications (Minasny and Perfect 2004; Lamorski et al. 2008; Pal et al. 2011; Elbisy 2015; Karandish and Šimůnek 2016; Singh et al. 2017; Tiwari et al. 2017; Sihag et al. 2017c, Sihag et al. 2018).”
- Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS) (2019, methodology): “Thus, indirect methods using the predictive approach have been developed for estimation of hydraulic properties of soil from soil properties (Schuh and Bauder 1986; Vereecken 1995; Singh et al. 2017; Sihag et al. 2017b).”