Hybrid Approach for Water Demand Prediction Based on Fuzzy Congnitive Maps

Abstract

 In this study, we propose a new hybrid approach for time series prediction based on the efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms and artificial neural networks (ANNs). The proposed structure optimization genetic algorithm (SOGA) for automatic construction of FCM is used for modeling complexity based on historical time series, and artificial neural networks (ANNs) which are used at the final process for making time series prediction. The suggested SOGA-FCM method is used for selecting the most important nodes (attributes) and interconnections among them which in the next stage are used as the input data to ANN used for time series prediction after training. The FCM with proficient learning calculations and ANN have been as of now demonstrated as adequate strategies for setting aside a few minutes arrangement anticipating. The execution of the proposed approach is exhibited through the examination of genuine information of every day water request and the comparing expectation. The multivariate examination of recorded information is held for nine factors, season, month, day or week, occasion, mean and high temperature, rain normal, touristic action and water request. The entire approach was actualized in a clever programming device at first sent for FCM forecast. Through the exploratory investigation, the value of the new mixture approach in water request forecast is illustrated, by computing the mean outright blunder (as one of the outstanding expectation measures). The outcomes are promising for future work to this bearing.