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.
Highlights
IntroductionIt is worth mentioning that rainfall is the only input element source for the hydrologic cycle.excessive rainfall and the scarcity of it on Earth affect the tremendous flooding and severe drought that occur over short and long intervals [1]
It is worth mentioning that rainfall is the only input element source for the hydrologic cycle.excessive rainfall and the scarcity of it on Earth affect the tremendous flooding and severe drought that occur over short and long intervals [1]
This paper proposes the hybrid models by combining the adaptive neuro-fuzzy inference system (ANFIS) model and different optimization methods (e.g. particle swarm optimization algorithm (PSO), genetic algorithm (GA), and differential evolutionary (DE) algorithm) for forecasting monthly rainfall
Summary
It is worth mentioning that rainfall is the only input element source for the hydrologic cycle.excessive rainfall and the scarcity of it on Earth affect the tremendous flooding and severe drought that occur over short and long intervals [1]. The accuracy of rainfall forecasting can help the preparation of efficient structural and non-structural designs for disaster management. The accuracy of rainfall time series forecasting depends on the methods used (e.g., stochastic or deterministic) for uncertainty mitigation. The forecasted rainfall provided by dynamical models is often prone to large error at the local scale [2]. Statistical models are simple to implement and operate and have been found to forecast more efficiently the smooth changes in rainfall at the local scale. Statistical models are often preferred for rainfall forecasting at the local scale [3,4,5,6]. The motivation was to explore reliable and robust hybrid intelligence models that were capable of mimicking the existing non-linear pattern in rainfall by analyzing the historical information and understanding the internal mechanism of the time series data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
