Rainfall-runoff forecasting utilizing genetic programming technique

Ali N. Ahmed, Gasim Hayder Ahmed Salih, Raihana Aliya Binti Abdul Rahman, Abdoulhdi A. Borhana

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)1523-1534
Number of pages12
JournalInternational Journal of Civil Engineering and Technology
Volume10
Issue number1
Publication statusPublished - 01 Jan 2019

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Genetic programming
Runoff
Rain

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction
  • Computer Networks and Communications

Cite this

Ahmed, Ali N. ; Ahmed Salih, Gasim Hayder ; Rahman, Raihana Aliya Binti Abdul ; Borhana, Abdoulhdi A. / Rainfall-runoff forecasting utilizing genetic programming technique. In: International Journal of Civil Engineering and Technology. 2019 ; Vol. 10, No. 1. pp. 1523-1534.
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Rainfall-runoff forecasting utilizing genetic programming technique. / Ahmed, Ali N.; Ahmed Salih, Gasim Hayder; Rahman, Raihana Aliya Binti Abdul; Borhana, Abdoulhdi A.

In: International Journal of Civil Engineering and Technology, Vol. 10, No. 1, 01.01.2019, p. 1523-1534.

Research output: Contribution to journalArticle

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