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

Yaseen, Zaher Mundher and Ebtehaj, Isa and Kim, Sungwon and Sanikhani, Hadi and Asadi, H. and Ghareb, Mazen Ismaeel and Bonakdari, Hossein and Wan Mohtar, Wan Hanna Melini and Al-Ansari, Nadhir and Shahid, Shamsuddin (2019) Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water (Switzerland), 11 (3). pp. 1-23. ISSN 2073-4441


Official URL: http://dx.doi.org/10.3390/w11030502


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.

Item Type:Article
Uncontrolled Keywords:rainfall time series forecasting, stochasticity, uncertainty analysis
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Civil Engineering
ID Code:87953
Deposited By: Yanti Mohd Shah
Deposited On:30 Nov 2020 21:37
Last Modified:30 Nov 2020 21:37

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