Water Resources Management

, Volume 32, Issue 1, pp 105–122 | Cite as

Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

  • Zaher Mundher Yaseen
  • Mazen Ismaeel Ghareb
  • Isa Ebtehaj
  • Hossein Bonakdari
  • Ridwan Siddique
  • Salim Heddam
  • Ali A. Yusif
  • Ravinesh Deo


In this study, a new hybrid model integrated adaptive neuro fuzzy inference system with Firefly Optimization algorithm (ANFIS-FFA), is proposed for forecasting monthly rainfall with one-month lead time. The proposed ANFIS-FFA model is compared with standard ANFIS model, achieved using predictor-predictand data from the Pahang river catchment located in the Malaysian Peninsular. To develop the predictive models, a total of fifteen years of data were selected, split into nine years for training and six years for testing the accuracy of the proposed ANFIS-FFA model. To attain optimal models, several input combinations of antecedents’ rainfall data were used as predictor variables with sixteen different model combination considered for rainfall prediction. The performances of ANFIS-FFA models were evaluated using five statistical indices: the coefficient of determination (R 2 ), Nash-Sutcliffe efficiency (NSE), Willmott’s Index (WI), root mean square error (RMSE) and mean absolute error (MAE). The results attained show that, the ANFIS-FFA model performed better than the standard ANFIS model, with high values of R 2 , NSE and WI and low values of RMSE and MAE. In test phase, the monthly rainfall predictions using ANFIS-FFA yielded R 2 , NSE and WI of about 0.999, 0.998 and 0.999, respectively, while the RMSE and MAE values were found to be about 0.272 mm and 0.133 mm, respectively. It was also evident that the performances of the ANFIS-FFA and ANFIS models were very much governed by the input data size where the ANFIS-FFA model resulted in an increase in the value of R 2 , NSE and WI from 0.463, 0.207 and 0.548, using only one antecedent month of data as an input (t-1), to almost 0.999, 0.998 and 0.999, respectively, using five antecedent months of predictor data (t-1, t-2, t-3, t-6, t-12, t-24). We ascertain that the ANFIS-FFA is a prudent modelling approach that could be adopted for the simulation of monthly rainfall in the present study region.


Rainfall forecasting Tropical environment Stochastic pattern Hybrid ANFIS-FFA model 



Authors would like the acknowledge their gratitude and appreciate for the Department of Irrigation and Drainage (DID), Malaysia, for providing the rainfall data set of the studied case study and their admirable cooperation. We thank all Reviewers and the Editor-in-Chief for their insightful comments that improved the clarity of the final paper.


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Zaher Mundher Yaseen
    • 1
    • 2
  • Mazen Ismaeel Ghareb
    • 3
    • 4
  • Isa Ebtehaj
    • 5
  • Hossein Bonakdari
    • 5
  • Ridwan Siddique
    • 6
    • 7
  • Salim Heddam
    • 8
  • Ali A. Yusif
    • 9
  • Ravinesh Deo
    • 10
  1. 1.Civil and Structural Engineering Department, Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaSelangor Darul EhsanMalaysia
  2. 2.Dams and Water Resources Department, College of EngineeringUniversity of AnbarRamadiIraq
  3. 3.Department of Computer Science, College of Science and TechnologyUniversity of Human DevelopmentSulaymaniyahIraq
  4. 4.Department of Informatic, School of Computing and EngineeringUniversity of HuddersfieldHuddersfieldUK
  5. 5.Department of Civil EngineeringRazi UniversityKermanshahIran
  6. 6.Northeast Climate Science CenterUniversity of MassachusettsAmherstUSA
  7. 7.Department of Civil and Environmental EngineeringUniversity of MassachusettsAmherstUSA
  8. 8.Faculty of Science, Agronomy Department, Hydraulics DivisionUniversity 20 Août 1955SkikdaAlgeria
  9. 9.Water Resources Engineering Department, College of EngineeringUniversity of DuhokDuhokIraq
  10. 10.School of Agricultural, Computational and Environmental Sciences, Institute of Agriculture and Environment (I Ag & E)University of Southern QueenslandSpringfieldAustralia

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