Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM_2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM_2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other seven models, achieving an R (correlation coefficient) of 0.9686 and an RMSE (root mean square error) of 4.6491 μg/m³. The RMSE values of CNN, GRU and LSTM models are 57.00%, 35.98% and 32.78% higher than that of the CNN-GRU-LSTM method, respectively. The forecasting results reveal that the CNN-GRU-LSTM predictor remarkably improves the performances of benchmark CNN, GRU and LSTM models in overall forecasting. This research method provides a new perspective for predictive forecasting of ambient air pollution PM_2.5 concentrations. The research results of the predictive model provide a scientific basis for air pollution prevention and control.

Anthropogenic sources of fine particulate matter (PM_2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM_2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM_2.5 concentration in Shanghai. The PM_2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM_2.5 concentration in Shanghai. The PM_2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM_2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM_2.5 concentration predicting models were the PM_2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM_2.5 in the next day and the input influence factors, the PM_2.5 showed the closest relation with the PM_2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM_2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM_2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM_2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

Surface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM_2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent unit (BiGRU). The data for meteorological factors and air pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs to the models. The W-CNN-BiGRU-BiLSTM hybrid model demonstrated strong performance during the predicting phase, achieving an R (correlation coefficient) of 0.9952, a root mean square error (RMSE) of 1.4935 μg/m³, a mean absolute error (MAE) of 1.2091 μg/m³, and a mean absolute percentage error (MAPE) of 7.3782%. Correspondingly, the accurate prediction of surface PM_2.5 concentrations is beneficial for air pollution control and urban planning.

Accurate prediction of air pollution is a difficult problem to be solved in atmospheric environment research. An Artificial Neural Network (ANN) is exploited to predict hourly PM2.5 and PM10 concentrations in Chongqing City. We take PM2.5 (PM10), time and meteorological elements as the input of the ANN, and the PM2.5 (PM10) of the next hour as the output to build an ANN model. Thirteen kinds of training functions are compared to obtain the optimal function. The research results display that the ANN model exhibits good performance in predicting hourly PM2.5 and PM10 concentrations. Trainbr is the best function for predicting PM2.5 concentrations compared to other training functions with R value (0.9783), RMSE (1.2271), and MAE (0.9050). Trainlm is the second best with R value (0.9495), RMSE (1.8845), and MAE (1.3902). Similarly, trainbr is also the best in forecasting PM10 concentrations with R value (0.9773), RMSE value (1.8270), and MAE value (1.4341). Trainlm is the second best with R value (0.9522), RMSE (2.6708), and MAE (1.8554). These two training functions have good generalization ability and can meet the needs of hourly PM2.5 and PM10 prediction. The forecast results can support fine management and help improve the ability to prevent and control air pollution in advance, accurately and scientifically.

Fine particulate matter (PM_2.5) affects climate change and human health. Therefore, the prediction of PM_2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM_2.5 concentration employing an artificial neural network (ANN). The annual change in PM_2.5 in Liaocheng from 2014 to 2021 shows a gradual decreasing trend. The air quality in Liaocheng during lockdown and after lockdown periods in 2020 was obviously improved compared with the same periods of 2019. The ANN employed in the study contains a hidden layer with 6 neurons, an input layer with 11 parameters, and an output layer. First, the ANN is used with 80% of data for training, then with 10% of data for verification. The value of correlation coefficient (R) for the training and validation data is 0.9472 and 0.9834, respectively. In the forecast period, it is demonstrated that the ANN model with Bayesian regularization (BR) algorithm (trainbr) obtained the best forecasting performance in terms of R (0.9570), mean absolute error (4.6 μg/m³), and root mean square error (6.6 μg/m³), respectively. The ANN model has produced accurate results. These results prove that the ANN is effective in monthly PM_2.5 concentration predicting due to the fact that it can identify nonlinear relationships between the input and output variables.

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.