A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches

Due to the complex nature of river stage-discharge process, the present study tried to develop a unique strategy to predict it precisely. The proposed conceptual strategy has some advantages to cover the shortcomings. First, it uses one model instead of several models to predict multiple points inst...

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Autores principales:	Farhad Alizadeh, Alireza Faregh Gharamaleki, Rasoul Jalilzadeh
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ensemble empirical decomposition mode (eemd) machine learning multi-station modeling mutual information (mi) river stage–discharge process wavelet transform (wt) Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/fad1cc943d1f4219ba404cbb03d787f8
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id	oai:doaj.org-article:fad1cc943d1f4219ba404cbb03d787f8
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spelling	oai:doaj.org-article:fad1cc943d1f4219ba404cbb03d787f82021-11-05T18:41:12ZA two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches2040-22442408-935410.2166/wcc.2020.006https://doaj.org/article/fad1cc943d1f4219ba404cbb03d787f82021-02-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/1/278https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354Due to the complex nature of river stage-discharge process, the present study tried to develop a unique strategy to predict it precisely. The proposed conceptual strategy has some advantages to cover the shortcomings. First, it uses one model instead of several models to predict multiple points instead of one point. On the one hand, the constructed model was inspired by physical-based model (to include time-space attributes of the catchment). On the other hand, ensemble empirical mode decomposition algorithm (EEMD), wavelet transform (WT), and mutual information (MI) were employed as a hybrid pre-processing approach conjugated to support vector machine. For this end, a conceptual strategy (multi-station model) was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. First, a classic model along with WT was performed to predict the 1-day-ahead river discharge for each single station. Therefore DWT-EEMD and feature selection were used for decomposed subseries using MI to be employed in conceptual models. In the proposed feature selection method, some useless subseries were deleted to achieve better performance. The results approved efficiency of the proposed WT-EEMD-MI approach to improve accuracy of different modeling strategies.Farhad AlizadehAlireza Faregh GharamalekiRasoul JalilzadehIWA Publishingarticleensemble empirical decomposition mode (eemd)machine learningmulti-station modelingmutual information (mi)river stage–discharge processwavelet transform (wt)Environmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 1, Pp 278-295 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ensemble empirical decomposition mode (eemd) machine learning multi-station modeling mutual information (mi) river stage–discharge process wavelet transform (wt) Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	ensemble empirical decomposition mode (eemd) machine learning multi-station modeling mutual information (mi) river stage–discharge process wavelet transform (wt) Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Farhad Alizadeh Alireza Faregh Gharamaleki Rasoul Jalilzadeh A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches
description	Due to the complex nature of river stage-discharge process, the present study tried to develop a unique strategy to predict it precisely. The proposed conceptual strategy has some advantages to cover the shortcomings. First, it uses one model instead of several models to predict multiple points instead of one point. On the one hand, the constructed model was inspired by physical-based model (to include time-space attributes of the catchment). On the other hand, ensemble empirical mode decomposition algorithm (EEMD), wavelet transform (WT), and mutual information (MI) were employed as a hybrid pre-processing approach conjugated to support vector machine. For this end, a conceptual strategy (multi-station model) was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. First, a classic model along with WT was performed to predict the 1-day-ahead river discharge for each single station. Therefore DWT-EEMD and feature selection were used for decomposed subseries using MI to be employed in conceptual models. In the proposed feature selection method, some useless subseries were deleted to achieve better performance. The results approved efficiency of the proposed WT-EEMD-MI approach to improve accuracy of different modeling strategies.
format	article
author	Farhad Alizadeh Alireza Faregh Gharamaleki Rasoul Jalilzadeh
author_facet	Farhad Alizadeh Alireza Faregh Gharamaleki Rasoul Jalilzadeh
author_sort	Farhad Alizadeh
title	A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches
title_short	A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches
title_full	A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches
title_fullStr	A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches
title_full_unstemmed	A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches
title_sort	two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/fad1cc943d1f4219ba404cbb03d787f8
work_keys_str_mv	AT farhadalizadeh atwostagemultiplepointconceptualmodeltopredictriverstagedischargeprocessusingmachinelearningapproaches AT alirezafareghgharamaleki atwostagemultiplepointconceptualmodeltopredictriverstagedischargeprocessusingmachinelearningapproaches AT rasouljalilzadeh atwostagemultiplepointconceptualmodeltopredictriverstagedischargeprocessusingmachinelearningapproaches AT farhadalizadeh twostagemultiplepointconceptualmodeltopredictriverstagedischargeprocessusingmachinelearningapproaches AT alirezafareghgharamaleki twostagemultiplepointconceptualmodeltopredictriverstagedischargeprocessusingmachinelearningapproaches AT rasouljalilzadeh twostagemultiplepointconceptualmodeltopredictriverstagedischargeprocessusingmachinelearningapproaches
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A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches

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