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Rainfall-runoff forecasting utilizing genetic programming technique

This paper reports how the rainfall-runoff is forecasted utilizing Genetic Programming (GP) technique. It is a program that was inspired by biological processes such as mutation, crossover, and inversion in order to create a new generation. It is a program that will learn and improve with each analy...

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Main Authors: Ahmed, A.N., Hayder, G., Rahman, R.A.B.A., Borhana, A.A.
Format: Article
Language:English
Published: 2020
Online Access:http://dspace.uniten.edu.my/jspui/handle/123456789/13213
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spelling my.uniten.dspace-132132020-08-11T03:29:43Z Rainfall-runoff forecasting utilizing genetic programming technique Ahmed, A.N. Hayder, G. Rahman, R.A.B.A. Borhana, A.A. This paper reports how the rainfall-runoff is forecasted utilizing Genetic Programming (GP) technique. It is a program that was inspired by biological processes such as mutation, crossover, and inversion in order to create a new generation. It is a program that will learn and improve with each analysis done. It uses a trial an error method in order to forecast rainfall-runoff. GP uses Root Mean Squared Error (RMSE) as an indication of how accurate the results of the forecast. The lower and closer the RMSE to zero, the more accurate the rainfall-runoff forecasted. The study consists of running the data on the software until the lowest RMSE is obtained. This research contains three models which use a different number of input variables to see whether it will give an impact on the rainfall-runoff forecasting. The results are compared and a bar chart is plotted. © IAEME Publication. 2020-02-03T03:31:08Z 2020-02-03T03:31:08Z 2019 Article http://dspace.uniten.edu.my/jspui/handle/123456789/13213 en International Journal of Civil Engineering and Technology (IJCIET)
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/
language English
description This paper reports how the rainfall-runoff is forecasted utilizing Genetic Programming (GP) technique. It is a program that was inspired by biological processes such as mutation, crossover, and inversion in order to create a new generation. It is a program that will learn and improve with each analysis done. It uses a trial an error method in order to forecast rainfall-runoff. GP uses Root Mean Squared Error (RMSE) as an indication of how accurate the results of the forecast. The lower and closer the RMSE to zero, the more accurate the rainfall-runoff forecasted. The study consists of running the data on the software until the lowest RMSE is obtained. This research contains three models which use a different number of input variables to see whether it will give an impact on the rainfall-runoff forecasting. The results are compared and a bar chart is plotted. © IAEME Publication.
format Article
author Ahmed, A.N.
Hayder, G.
Rahman, R.A.B.A.
Borhana, A.A.
spellingShingle Ahmed, A.N.
Hayder, G.
Rahman, R.A.B.A.
Borhana, A.A.
Rainfall-runoff forecasting utilizing genetic programming technique
author_facet Ahmed, A.N.
Hayder, G.
Rahman, R.A.B.A.
Borhana, A.A.
author_sort Ahmed, A.N.
title Rainfall-runoff forecasting utilizing genetic programming technique
title_short Rainfall-runoff forecasting utilizing genetic programming technique
title_full Rainfall-runoff forecasting utilizing genetic programming technique
title_fullStr Rainfall-runoff forecasting utilizing genetic programming technique
title_full_unstemmed Rainfall-runoff forecasting utilizing genetic programming technique
title_sort rainfall-runoff forecasting utilizing genetic programming technique
publishDate 2020
url http://dspace.uniten.edu.my/jspui/handle/123456789/13213
_version_ 1678595895524851712
score 13.222552

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