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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IAEME Publication
2023
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| author | Ahmed A.N. Hayder G. Rahman R.A.B.A. Borhana A.A. |
| author2 | 57214837520 |
| author_facet | 57214837520 Ahmed A.N. Hayder G. Rahman R.A.B.A. Borhana A.A. |
| author_sort | Ahmed A.N. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uniten.dspace-25035 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | IAEME Publication |
| record_format | dspace |
| spelling | my.uniten.dspace-250352023-05-29T15:30:46Z Rainfall-runoff forecasting utilizing genetic programming technique Ahmed A.N. Hayder G. Rahman R.A.B.A. Borhana A.A. 57214837520 56239664100 57205651379 55212152300 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. Final 2023-05-29T07:30:46Z 2023-05-29T07:30:46Z 2019 Article 2-s2.0-85060928940 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060928940&partnerID=40&md5=fc57e4d103c43b26a882d93d5d621a62 https://irepository.uniten.edu.my/handle/123456789/25035 10 1 1523 1534 IAEME Publication Scopus |
| spellingShingle | Ahmed A.N. Hayder G. Rahman R.A.B.A. Borhana A.A. Rainfall-runoff forecasting utilizing genetic programming technique |
| title | 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_short | Rainfall-runoff forecasting utilizing genetic programming technique |
| title_sort | rainfall-runoff forecasting utilizing genetic programming technique |
| url_provider | http://dspace.uniten.edu.my/ |