The effects of air pollution, amongst the greatest problems facing our planet, are felt throughout all spheres of society, including the transportation sector. Its impacts can range from increased risk of illness to rising temperatures. One of the essential situations for improving inner-city general health and assisting in the creation of a sustainable environment is the ability to forecast air pollution concentrations with accuracy and effectiveness. The backpropagation model is employed in this research to predict the future concentration levels of PM2.5. Data on air quality are gathered and used in the experiment. Empirical Wavelet Transform (EWT) is used to break down the air quality data, which is then utilized to train and evaluate the model. 30% of the data collected is utilized during testing, and 70% was used to train the BP model. The evaluation criteria are applied after testing to determine the model's correctness. The Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), and Root Mean Square Error (RMSE) are the evaluation criteria used and their values were 0.0896, 0.8112, and 0.1162, respectively.
Wind is one of the most important natural factors that should be paid attention to when train running safely. The prediction of wind speed along the railway is of great significance to the safe running, dispatching and riding comfort of trains. A new wind speed prediction model along railway is proposed based on VMD-BGA-DBN. Firstly, variational modal decomposition is used to preprocess the original time series and obtain the decomposed sub-series. Then, binary-coded genetic algorithm selects the features of the sub-series for the deep belief network predictor. Finally, the optimized sub-series after feature selection processing are put into the deep belief network models to obtain the prediction results of each wind speed sub-series, and the prediction results of all sub-series are combined to generate the final wind speed prediction results. According to the prediction results, it can be seen that (a) among all decomposition strategies, the variational modal decomposition method adopted in this paper can better deal with the non-stationarity and randomness of wind speed timing data; (b) the proposed hybrid model has significant research potential.