Predicting the infiltration characteristics for semi-arid regions using regression trees

The study of the infiltration process is considered essential and necessary for all hydrology studies. Therefore, accurate predictions of infiltration characteristics are required to understand the behavior of the subsurface flow of water through the soil surface. The aim of the current study is to...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Parveen Sihag, Munish Kumar, Saad Sh. Sammen
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	cumulative infiltration infiltration rate m5 tree random forest random tree Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/38672fd834ca462b8e54bd2fcfa2333d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:38672fd834ca462b8e54bd2fcfa2333d
record_format	dspace
spelling	oai:doaj.org-article:38672fd834ca462b8e54bd2fcfa2333d2021-11-06T10:07:34ZPredicting the infiltration characteristics for semi-arid regions using regression trees1606-97491607-079810.2166/ws.2021.047https://doaj.org/article/38672fd834ca462b8e54bd2fcfa2333d2021-09-01T00:00:00Zhttp://ws.iwaponline.com/content/21/6/2583https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798The study of the infiltration process is considered essential and necessary for all hydrology studies. Therefore, accurate predictions of infiltration characteristics are required to understand the behavior of the subsurface flow of water through the soil surface. The aim of the current study is to simulate and improve the prediction accuracy of the infiltration rate and cumulative infiltration of soil using regression tree methods. Experimental data recorded with a double ring infiltrometer for 17 different sites are used in this study. Three regression tree methods: random tree, random forest (RF) and M5 tree, are employed to model the infiltration characteristics using basic soil characteristics. The performance of the modelling approaches is compared in predicting the infiltration rate as well as cumulative infiltration, and the obtained results suggest that the performance of the RF model is better than the other applied models with coefficient of determination (R2) = 0.97 and 0.97, root mean square error (RMSE) = 8.10 and 6.96 and mean absolute error (MAE) = 5.74 and 4.44 for infiltration rate and cumulative infiltration respectively. The RF model is used to represent the infiltration characteristics of the study area. Moreover, parametric sensitivity is adopted to study the significance of each input parameter in estimating the infiltration process. The results suggest that time (t) is the most influencing parameter in predicting the infiltration process using this data set. HIGHLIGHTS Infiltration rate and cumulative infiltration characteristics in semi-arid region were modelled using regression tree.; Random forest outperforms random tree as well as M5 tree to predict the infiltration characteristics.; Time is observed as the most important parameter in affecting the prediction performance of both the infiltration rate and the cumulative infiltration of soil.;Parveen SihagMunish KumarSaad Sh. SammenIWA Publishingarticlecumulative infiltrationinfiltration ratem5 treerandom forestrandom treeWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 6, Pp 2583-2595 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cumulative infiltration infiltration rate m5 tree random forest random tree Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	cumulative infiltration infiltration rate m5 tree random forest random tree Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Parveen Sihag Munish Kumar Saad Sh. Sammen Predicting the infiltration characteristics for semi-arid regions using regression trees
description	The study of the infiltration process is considered essential and necessary for all hydrology studies. Therefore, accurate predictions of infiltration characteristics are required to understand the behavior of the subsurface flow of water through the soil surface. The aim of the current study is to simulate and improve the prediction accuracy of the infiltration rate and cumulative infiltration of soil using regression tree methods. Experimental data recorded with a double ring infiltrometer for 17 different sites are used in this study. Three regression tree methods: random tree, random forest (RF) and M5 tree, are employed to model the infiltration characteristics using basic soil characteristics. The performance of the modelling approaches is compared in predicting the infiltration rate as well as cumulative infiltration, and the obtained results suggest that the performance of the RF model is better than the other applied models with coefficient of determination (R2) = 0.97 and 0.97, root mean square error (RMSE) = 8.10 and 6.96 and mean absolute error (MAE) = 5.74 and 4.44 for infiltration rate and cumulative infiltration respectively. The RF model is used to represent the infiltration characteristics of the study area. Moreover, parametric sensitivity is adopted to study the significance of each input parameter in estimating the infiltration process. The results suggest that time (t) is the most influencing parameter in predicting the infiltration process using this data set. HIGHLIGHTS Infiltration rate and cumulative infiltration characteristics in semi-arid region were modelled using regression tree.; Random forest outperforms random tree as well as M5 tree to predict the infiltration characteristics.; Time is observed as the most important parameter in affecting the prediction performance of both the infiltration rate and the cumulative infiltration of soil.;
format	article
author	Parveen Sihag Munish Kumar Saad Sh. Sammen
author_facet	Parveen Sihag Munish Kumar Saad Sh. Sammen
author_sort	Parveen Sihag
title	Predicting the infiltration characteristics for semi-arid regions using regression trees
title_short	Predicting the infiltration characteristics for semi-arid regions using regression trees
title_full	Predicting the infiltration characteristics for semi-arid regions using regression trees
title_fullStr	Predicting the infiltration characteristics for semi-arid regions using regression trees
title_full_unstemmed	Predicting the infiltration characteristics for semi-arid regions using regression trees
title_sort	predicting the infiltration characteristics for semi-arid regions using regression trees
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/38672fd834ca462b8e54bd2fcfa2333d
work_keys_str_mv	AT parveensihag predictingtheinfiltrationcharacteristicsforsemiaridregionsusingregressiontrees AT munishkumar predictingtheinfiltrationcharacteristicsforsemiaridregionsusingregressiontrees AT saadshsammen predictingtheinfiltrationcharacteristicsforsemiaridregionsusingregressiontrees
_version_	1718443787911430144

Predicting the infiltration characteristics for semi-arid regions using regression trees

Ejemplares similares