Prediction of Outbound Transportation Volume of Xinjiang Coal Railway by Integrating Sparrow Search with Long Short-Term Memory
To enhance the precision of predicting the Xinjiang's coal railway outbound volume transportation,a prediction model integrating the sparrow search algorithm and the long and short-term memory network(SSA-LSTM)is proposed.The model introduces the sparrow search algorithm to optimize the hyper-parameters of the LSTM model in order to improve the model prediction performance.Based on the data of Xinjiang coal rail outbound transportation volume from 2015 to 2022,the gray correlation analysis is employed to comprehensively evaluate the impact of factors,including economic and transportation aspects,ensuring that the selected factors exhibit a strong correlation with the prediction targets.Among the influencing factors,the GDP data is adjusted for Consumer Price Index(CPI)effects,and the refined data are then fed into the model for prediction.Finally,the model is applied to predict the Xinjiang's coal rail outbound transportation volume across short,medium,and long time horizons.The results demonstrate that the SSA-LSTM model outperforms both the BP neural network and the conventional LSTM model,achieving a Mean Absolute Percentage Error(MAPE)of 0.88%and a Root Mean Square Error(RMSE)of 49.9.Furthermore,incorporating CPI processing into the prediction process significantly reduces the prediction error,with MAPE and RMSE decreasing by 75.8%and 56.2%,respectively,compared to non-CPI-processed predictions.This study provides an effective approach for predicting Xinjiang's coal rail outbound transportation volume,offering important data insights that inform the strategic design of coal transportation routes out of Xinjiang.
万方数据
维普
