There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and interval predictions of PM2.5 concentration simultaneously. Firstly, we optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based learning, and Lévy flight. The experiments show that the improved Sparrow Search Algorithm (FOSSA) outperforms SSA-based algorithms. In addition, the improved Sparrow Search Algorithm (FOSSA) is employed to optimize the initial weights of probabilistic forecasting model with autoregressive recurrent network (DeepAR). Then, the FOSSA–DeepAR learning method is utilized to achieve the point prediction and interval prediction of PM2.5 concentration in Beijing, China. The performance of FOSSA–DeepAR is compared with other hybrid models and a single DeepAR model. Furthermore, hourly data of PM2.5 and O₃ concentration in Taian of China, O₃ concentration in Beijing, China are used to verify the effectiveness and robustness of the proposed FOSSA–DeepAR learning method. Finally, the empirical results illustrate that the proposed FOSSA–DeepAR learning model can achieve more efficient and accurate predictions in both interval and point prediction.

Daily weather conditions are closely related to every field of production and life, and the forecasting of weather conditions plays an important role in social development. Based on the data characteristics of urban weather conditions, a deep learning network was designed to forecast urban weather conditions, and its feasibility was proved by experiments. In view of the non-stationary and seasonal fluctuation of the time series of daily weather conditions in Shenzhen from 2015 to 2019, empirical mode decomposition (EMD) was used to carry out the stationary processing for the daily minimum humidity, minimum pressure, maximum temperature, maximum pressure, maximum wind speed and minimum temperature. The decomposed components, residual sequence and original sequence were reconstructed according to the degree of relevance. On this basis, a long short-term memory (LSTM) neural network for the Shenzhen daily weather forecast was used, using the advantages of the LSTM model in time-series data processing, using the grid search algorithm to find the optimal combination of the above parameters and combining with the gradient descent optimization algorithm to find optimal weights and bias, so as to improve the prediction accuracy of Shenzhen weather characteristics. The experimental results show that our design of the EMD-LSTM model has higher forecasting precision and efficiency than traditional models, which provides new ideas for the weather forecast.

The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of particulate matter (PM10 and PM2.5). First, the improvement complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the concentration data of PM10 and PM2.5 into a set of intrinsic mode functions (IMFs) with different frequencies. In addition, wavelet transform (WT) is utilized to decompose the IMFs with high frequency based on sample entropy values. Then the GTOA algorithm is used to optimize ELM. Furthermore, the GTOA-ELM is utilized to predict all the subseries. The final forecast result is obtained by ensemble of the forecast results of all subseries. To further prove the predictable performance of the hybrid approach on air pollutants, the hourly concentration data of PM2.5 and PM10 are used to make one-step-, two-step- and three-step-ahead predictions. The empirical results demonstrate that the hybrid ICEEMDAN-WT-GTOA-ELM approach has superior forecasting performance and stability over other methods. This novel method also provides an effective and efficient approach to make predictions for nonlinear, nonstationary and irregular data.