뉴로 유전자 결합모형을 이용한 상수도 1일 급수량 예측

Abstract

Existing models that predict of Daily water supply include statistical models and neuralnetwork model. The neural network model was more effective than the statistical models. Onlyneural network model, which predict of Daily water supply, is focused on estimation of theoperational control. Neural network model takes long learning time and gets into localminimum.This study proposes Neuro Genetic hybrid model which a combination of genetic algorithmand neural network. Hybrid model makes up for neural network’s shortcomings. In this study,the amount of supply, the mean temperature and the population of the area supplied with waterare use for neural network’s learning patterns for prediction.RMSE(Root Mean Square Error) is used for a MOE(Measure Of Effectiveness). Thecomparison of the two models showed that the predicting capability of Hybrid model is moreeffective than that of neural network model. The proposed hybrid model is able to predict ofDaily water, thus it can apply real time estimation of operational control of water works andwater drain pipes. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 11.81% and the average error was lower than 1.76%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.