Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis

Zaher Mundher Yaseen, Isa Ebtehaj, Sungwon Kim, Hadi Sanikhani, H. Asadi, Mazen Ismaeel Ghareb, Hossein Bonakdari, Wan Hanna Melini Wan Mohtar, Nadhir Al-Ansari, Shamsuddin Shahid

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the effect of monthly, seasonal, and annual history were considered. The performances of the forecasting models were assessed using various forecasting skill indicators. Moreover, an uncertainty analysis of the developed forecasting models was performed to evaluate the ability of the hybrid ANFIS models. The bound width of 95% confidence interval (d-factor) and the percentage of observed samples which was enveloped by 95% forecasted uncertainties (95PPU) were used for this purpose. The results indicated that all the hybrid ANFIS models performed better than the conventional ANFIS and for all input combinations. The obtained results showed that the models with best input combinations had the (95PPU and d-factor) values of (91.67 and 1.41), (91.03 and 1.41), (89.74 and 1.42), and (88.46 and 1.43) for ANFIS-PSO, ANFIS-GA, ANFIS-DE, and the conventional ANFIS, respectively. Based on the 95PPU and d-factor, it is concluded that all hybrid ANFIS models have an acceptable degree of uncertainty in forecasting monthly rainfall. The results of this study proved that the hybrid ANFIS with an evolutionary algorithm is a reliable modeling technique for forecasting monthly rainfall.

Original languageEnglish
Article number502
JournalWater (Switzerland)
Volume11
Issue number3
DOIs
Publication statusPublished - 1 Mar 2019

Fingerprint

uncertainty analysis
Uncertainty analysis
Fuzzy inference
Intelligence
Uncertainty
Rain
intelligence
uncertainty
rain
rainfall
system model
swarms
genetic algorithm
Aptitude
Malaysia
Evolutionary algorithms
Particle swarm optimization (PSO)
Genetic algorithms
History
Confidence Intervals

Keywords

  • Hybrid ANFIS model
  • Rainfall time series forecasting
  • Stochasticity
  • Uncertainty analysis

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

Cite this

Yaseen, Z. M., Ebtehaj, I., Kim, S., Sanikhani, H., Asadi, H., Ghareb, M. I., ... Shahid, S. (2019). Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water (Switzerland), 11(3), [502]. https://doi.org/10.3390/w11030502

Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. / Yaseen, Zaher Mundher; Ebtehaj, Isa; Kim, Sungwon; Sanikhani, Hadi; Asadi, H.; Ghareb, Mazen Ismaeel; Bonakdari, Hossein; Wan Mohtar, Wan Hanna Melini; Al-Ansari, Nadhir; Shahid, Shamsuddin.

In: Water (Switzerland), Vol. 11, No. 3, 502, 01.03.2019.

Research output: Contribution to journalArticle

Yaseen, ZM, Ebtehaj, I, Kim, S, Sanikhani, H, Asadi, H, Ghareb, MI, Bonakdari, H, Wan Mohtar, WHM, Al-Ansari, N & Shahid, S 2019, 'Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis', Water (Switzerland), vol. 11, no. 3, 502. https://doi.org/10.3390/w11030502
Yaseen, Zaher Mundher ; Ebtehaj, Isa ; Kim, Sungwon ; Sanikhani, Hadi ; Asadi, H. ; Ghareb, Mazen Ismaeel ; Bonakdari, Hossein ; Wan Mohtar, Wan Hanna Melini ; Al-Ansari, Nadhir ; Shahid, Shamsuddin. / Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. In: Water (Switzerland). 2019 ; Vol. 11, No. 3.
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