Study Of The Transport Goods Volume And Navigation Scale In The Xiao Nan Hai Key Position

The Yangtze River has been called the "golden waterway" .after the completion of the Xian Xia Project reservoir will have a larger waterway improvement, ten thousand-ton vessels can reach Chongqing. Chongqing over the river for the hills and mountains, beaches and more radical stream, channel conditions are poor, only fleet that can use it kiloton seriously restricted the economic development of the hinterland, the need to take strong measures to be addressed.Prediction of the cargo volume of Waterway transportation is the main basis fordetermining the scale of water transport infrastructures. A reasonable and accurateprediction result of cargo volume has the most significant effect on determining the investment and benefit of future waterway transportation engineering projects, making developing strategy for future waterway transportation and giving full play to benefit of waterway transport facilities.Based on the amount of reference literatures on cargo volume forecast, The paper expatiates on principle, method and steps of forecasting waterway transport cargo volume; it studies and discusses the application of theory of grey system to cargo volume forecast, analyzes the model building ideas, model checking methods and scope of model application on the basis of grey system theory, improves the exponentially weighting grey model, modified residual error correction model and modified equal dimension model with new information and grey information filling in successively, thus widening the scope of the grey forecasting models, strengthening the applicability of grey models and increasing the accuracy of forecasting. Then, on the basis of brief introduction of the basic structure and learning rules of artificial neural network, the paper analyzes the deficiency of standard BP algorithm, and introduce L-M algorithm to modify the network which improves the convergence of the BP network greatly. Accordingly, a regression forecasting model and nonlinear combined forecasting model based on ANN are built. With the comparison of nonlinear combined model with the linear one, the superiority of nonlinear combined forecasting method is then concluded.