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- Title
基于 LSTM-RBF 的水路货运量预测.
- Authors
王鑫鑫; 沈晓攀; 王琪; 徐仟
- Abstract
Water transportation is an important part of transportation and freight, and the forecasting of water freight volume is of great value to the economic development. In recent years, with the change of the economic situation and the rapid development of multimodal transport, the data fluctuation of water transport freight volume has increased, and the difficulty of accurate prediction has become greater. Therefore, a combined forecasting model based on long short-term memory(LSTM)and radial basis function(RBF)was proposed, LSTM was used to accurately forecast each index to reduce the impact of index value error on target value prediction. Then the RBF neural network was trained to forecast the target value(waterway freight volume)which based on the future index value. The LSTM-RBF model not only avoids the defect of single influencing factor in time series forecasting, but also can bring the advantage of LSTM's long-term memory into RBF' s regression forecasting. The experimental results show that the LSTM-RBF model is superior to the other comparison models in terms of root-meansquare error and fitting degree, and this model has high accuracy in the forecasting of waterway freight volume.
- Publication
Science Technology & Engineering, 2023, Vol 23, Issue 18, p7995
- ISSN
1671-1815
- Publication type
Article