Assessment of infiltration models developed using soft computing techniques

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 addit...

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Autores principales:	Parveen Sihag, Munish Kumar, Balraj Singh
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	infiltration rate support vector machines gaussian process based regression artificial neural network random forest regression Ecology QH540-549.5 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/9eae689a5e1c49bb956bd87e72c55468
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spelling	oai:doaj.org-article:9eae689a5e1c49bb956bd87e72c554682021-11-26T11:19:50ZAssessment of infiltration models developed using soft computing techniques2474-950810.1080/24749508.2020.1720475https://doaj.org/article/9eae689a5e1c49bb956bd87e72c554682021-10-01T00:00:00Zhttp://dx.doi.org/10.1080/24749508.2020.1720475https://doaj.org/toc/2474-9508In 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.Parveen SihagMunish KumarBalraj SinghTaylor & Francis Grouparticleinfiltration ratesupport vector machinesgaussian process based regressionartificial neural networkrandom forest regressionEcologyQH540-549.5GeologyQE1-996.5ENGeology, Ecology, and Landscapes, Vol 5, Iss 4, Pp 241-251 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	infiltration rate support vector machines gaussian process based regression artificial neural network random forest regression Ecology QH540-549.5 Geology QE1-996.5
spellingShingle	infiltration rate support vector machines gaussian process based regression artificial neural network random forest regression Ecology QH540-549.5 Geology QE1-996.5 Parveen Sihag Munish Kumar Balraj Singh Assessment of infiltration models developed using soft computing techniques
description	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.
format	article
author	Parveen Sihag Munish Kumar Balraj Singh
author_facet	Parveen Sihag Munish Kumar Balraj Singh
author_sort	Parveen Sihag
title	Assessment of infiltration models developed using soft computing techniques
title_short	Assessment of infiltration models developed using soft computing techniques
title_full	Assessment of infiltration models developed using soft computing techniques
title_fullStr	Assessment of infiltration models developed using soft computing techniques
title_full_unstemmed	Assessment of infiltration models developed using soft computing techniques
title_sort	assessment of infiltration models developed using soft computing techniques
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/9eae689a5e1c49bb956bd87e72c55468
work_keys_str_mv	AT parveensihag assessmentofinfiltrationmodelsdevelopedusingsoftcomputingtechniques AT munishkumar assessmentofinfiltrationmodelsdevelopedusingsoftcomputingtechniques AT balrajsingh assessmentofinfiltrationmodelsdevelopedusingsoftcomputingtechniques
_version_	1718409489874419712

Assessment of infiltration models developed using soft computing techniques

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