Support vector machine and neural network based model for monthly stream flow forecasting

Nuratiah Zaini, Marlinda Abdul Malek, Marina Yusoff, Siti Fatimah Che Osmi, Nurul Hani Mardi, Shuhairy Norhisham @ Masam

Research output: Contribution to journalArticle


Accurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm - backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kuantan River located in Peninsular Malaysia are investigated and compared to regular SVM and BPNN model. Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. The input parameters to the forecasting models are antecedent streamflow, historical rainfall and meteorological parameters namely evaporation, temperature, relative humidity and mean wind speed. Two performance evaluation measure, root mean square error (RMSE) and coefficient of determination (R 2 ) were employed to evaluate the performance of developed forecasting model. It is found that, RMSE and R 2 for hybrid SVM-PSO are 24.8267 m 3 /s and 0.9651 respectively while general SVM model yields RMSE of 27.5086 m 3 /s and 0.9305 of R 2 for testing phase. Besides that, hybrid BA-BPNN produces RMSE, 17.7579 m 3 /s and R 2 , 0.7740 while BPNN model produces lower RMSE and R 2 of 28.1396 m 3 /s and 0.5015 respectively. Therefore, the results indicate that hybrid model, SVM-PSO and Bat-BPNN yield better performance as compared to general SVM and BPNN, respectively in streamflow forecasting.

Original languageEnglish
Pages (from-to)683-688
Number of pages6
JournalInternational Journal of Engineering and Technology(UAE)
Issue number4
Publication statusPublished - 01 Jan 2018

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science (miscellaneous)
  • Environmental Engineering
  • Chemical Engineering(all)
  • Engineering(all)
  • Hardware and Architecture

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