Social spider optimization-based projection pursuit model to predict annual maximum flood peak flow
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Abstract:
The prediction of annual maximum flood peak flow is affected by many complicated factors and highly indeterminate, making it difficult to deliver accurate forecasts by conventional statistical methods. In this paper, based on hydrological sequence itself, we proposed that the projection regression model be used to predict the annual maximum flood peak flow. In order to obtain the optimal parameters for the projection pursuit model and improve prediction accuracy, we proposed the hybrid intelligence-based prediction model for annual maximum flood peak flow, in which the delay correlation coefficient method was used to determine the regression prediction factor, the social spider optimization algorithm to optimize the optimal projection direction parameter a of the projection pursuit model, the least square method to determine the weight coefficient c of the polynomial, and qualified rate to control the number of parameter M. The annual maximum flood peak flow data of Yichang Gauging Station of Yangtze River (1882 - 2004) were used to test the proposed model. The results indicated that the model can obtain very good prediction results, with a mean absolute relative error of 8.61% in the training phase, and a mean absolute relative error of 10.51% in the testing phase. The model can produce stable results and can be effectively applied to the prediction of annual maximum flood peak flow.
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