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Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis

School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Malaysia
Department of Civil Engineering, Razi University, 97146 Kermanshah, Iran
Department of Railroad Construction and Safety Engineering, Dongyang University, 36040 Yeongju, Korea
Water Engineering Department, Agriculture Faculty, University of Kurdistan, 66177-15175 Sanandaj, Iran
Water Engineering Department, Faculty of Agriculture, University of Tabriz, 51666-16471 Tabriz, Iran
Department of Computer Science, College of Science and Technology, University of Human Development, 46001 Sulaymaniyah, Iraq
Sustainable and Smart Township Research Centre (SUTRA), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden
Authors to whom correspondence should be addressed.
Water 2019, 11(3), 502;
Received: 23 January 2019 / Revised: 23 February 2019 / Accepted: 6 March 2019 / Published: 10 March 2019
(This article belongs to the Special Issue Water Resources Management Strategy Under Global Change)
PDF [12359 KB, uploaded 11 March 2019]


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. View Full-Text
Keywords: hybrid ANFIS model; rainfall time series forecasting; stochasticity; uncertainty analysis hybrid ANFIS model; rainfall time series forecasting; stochasticity; uncertainty analysis

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Yaseen, Z.M.; Ebtehaj, I.; Kim, S.; Sanikhani, H.; Asadi, H.; Ghareb, M.I.; Bonakdari, H.; Wan Mohtar, W.H.M.; Al-Ansari, N.; Shahid, S. Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis. Water 2019, 11, 502.

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