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Bat algorithm and neural network for monthly streamflow prediction

Streamflow prediction has a significance influence on improving water supply management and flood prevention. The applications of artificial intelligence (AI) have been proved to have better performance as compared to conventional statistical method in streamflow prediction. Therefore, this study pr...

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Main Authors:	Zaini N., Malek M.A., Yusoff M., Osmi S.F.C., Mardi N.H., Norhisham S.
Other Authors:	56905328500
Format:	Conference Paper
Published:	American Institute of Physics Inc. 2023
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id	my.uniten.dspace-23594
record_format	dspace
spelling	my.uniten.dspace-235942023-05-29T14:50:24Z Bat algorithm and neural network for monthly streamflow prediction Zaini N. Malek M.A. Yusoff M. Osmi S.F.C. Mardi N.H. Norhisham S. 56905328500 55636320055 23391662400 54963643200 57190171141 54581400300 Streamflow prediction has a significance influence on improving water supply management and flood prevention. The applications of artificial intelligence (AI) have been proved to have better performance as compared to conventional statistical method in streamflow prediction. Therefore, this study proposed on the development of streamflow prediction model AI techniques namely Bat algorithm (BA) and backpropagation neural network (BPNN). BA is an optimization technique, which is to optimize BPNN in deciding optimum parameters and then improve the prediction accuracy. The study area chosen is Kuantan river and Kenau river, located in Kuantan, Malaysia. Two prediction models are proposed in this study which are BPNN and hybrid Bat-BPNN. Monthly historical rainfall data, antecedent river flow data and meteorology parameters data for two different rivers were used as the input to the proposed models. The performance of the proposed prediction models for Kuantan river and Kenau river are then being compared and evaluated in term of RMSE and R2. It is found that hybrid model, Bat-BPNN yields lower RMSE and provides higher R2 as compared to BPNN model at both Kuantan river and Kenau river. Therefore, it can be concluded that, proposed hybrid model yields better performances as compared to BPNN model for monthly streamflow prediction. � 2018 Author(s). Final 2023-05-29T06:50:24Z 2023-05-29T06:50:24Z 2018 Conference Paper 10.1063/1.5066901 2-s2.0-85057261770 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057261770&doi=10.1063%2f1.5066901&partnerID=40&md5=28c90dce5aa895304cc793b29d9fc93b https://irepository.uniten.edu.my/handle/123456789/23594 2030 20260 American Institute of Physics Inc. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Streamflow prediction has a significance influence on improving water supply management and flood prevention. The applications of artificial intelligence (AI) have been proved to have better performance as compared to conventional statistical method in streamflow prediction. Therefore, this study proposed on the development of streamflow prediction model AI techniques namely Bat algorithm (BA) and backpropagation neural network (BPNN). BA is an optimization technique, which is to optimize BPNN in deciding optimum parameters and then improve the prediction accuracy. The study area chosen is Kuantan river and Kenau river, located in Kuantan, Malaysia. Two prediction models are proposed in this study which are BPNN and hybrid Bat-BPNN. Monthly historical rainfall data, antecedent river flow data and meteorology parameters data for two different rivers were used as the input to the proposed models. The performance of the proposed prediction models for Kuantan river and Kenau river are then being compared and evaluated in term of RMSE and R2. It is found that hybrid model, Bat-BPNN yields lower RMSE and provides higher R2 as compared to BPNN model at both Kuantan river and Kenau river. Therefore, it can be concluded that, proposed hybrid model yields better performances as compared to BPNN model for monthly streamflow prediction. � 2018 Author(s).
author2	56905328500
author_facet	56905328500 Zaini N. Malek M.A. Yusoff M. Osmi S.F.C. Mardi N.H. Norhisham S.
format	Conference Paper
author	Zaini N. Malek M.A. Yusoff M. Osmi S.F.C. Mardi N.H. Norhisham S.
spellingShingle	Zaini N. Malek M.A. Yusoff M. Osmi S.F.C. Mardi N.H. Norhisham S. Bat algorithm and neural network for monthly streamflow prediction
author_sort	Zaini N.
title	Bat algorithm and neural network for monthly streamflow prediction
title_short	Bat algorithm and neural network for monthly streamflow prediction
title_full	Bat algorithm and neural network for monthly streamflow prediction
title_fullStr	Bat algorithm and neural network for monthly streamflow prediction
title_full_unstemmed	Bat algorithm and neural network for monthly streamflow prediction
title_sort	bat algorithm and neural network for monthly streamflow prediction
publisher	American Institute of Physics Inc.
publishDate	2023
_version_	1806423352610390016
score	13.222552

Bat algorithm and neural network for monthly streamflow prediction

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