With the rapid development of industrialization and urbanization, atmospheric pollution research is vital for regional sustainable development and related policies formulated by the government. Previous studies have mainly studied a single evaluation method to analyze the air quality index (AQI) or single air pollutant. This research integrated the Spearman coefficient (SC) correlation analysis, a random search (RS) algorithm and an excellent extreme gradient boosting (XGBoost) algorithm to evaluate the air pollution influence of industrialization and urbanization (APIIU). Industrialization, urbanization and meteorological indicators were used to measure the influence degree of APIIU on AQI and particulate matter 2.5 (PM_2.5), respectively. The main findings were: (1) the APIIU-AQI and APIIU-PM_2.5 of Henan Province, Hubei Province and Hunan Province had significant changes from 2017 to 2019; (2) the value of square of determination coefficient of real value (R²), the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of APIIU-AQI and APIIU-PM_2.5 in three provinces predicted by the SC-RS-XGBoost were 0.945, 0.103, 4.25% and 0.897, 0.205, 4.84%, respectively; (3) the predicted results were more accurate than using a SC-XGBoost, RS-XGBoost, traditional XGBoost, support vector regression (SVR) and extreme learning machine (ELM).

The evaluation of community disaster resilience is of great practical importance for building low-risk, sustainable, and disaster-resistant cities. With 12 communities in Luoyang as the objects, this paper adopts entropy weight TOPSIS and obstacle diagnosis to study the community disaster resilience of Luoyang from seven dimensions, such as demographic characteristics, economic development, and infrastructure. The results of the study are as follows: (1) the community disaster resilience of Luoyang (0.48) is at a medium level. Community capital is the main influencing factor of community disaster resilience. Government governance, community capacity, and community intelligence are the components that need attention in the construction of Luoyang’s community disaster resilience. (2) The community disaster resilience of Luoyang presents a decreasing trend from rural to urban areas. Moreover, communities with high disaster resilience are less than communities with low disaster resilience. (3) The obstacle to community disaster resilience of Luoyang focuses on population, economic development, and infrastructure. In addition, community trust, community dependence, popularization and intellectualization of disaster prevention information, and disaster information sharing also significantly restrict the construction of Luoyang community disaster resilience. (4) According to the results of sensitivity analysis, the entropy weight TOPSIS evaluation results are less sensitive. Moreover, changing the weight value, weight method, and evaluation method will not lead to major changes in the rankings.

To reasonably evaluate and predict the loss of rainstorm and flood disaster, this study is based on the rainfall data and rainstorm and flood disaster data of 18 cities in Henan Province from 2010 to 2020, using GIS technology and weighted comprehensive evaluation method to analyze the risk of rainstorm and flood disaster factors in various regions. The four risk factors of hazard risk, hazard-pregnant environment sensitivity, hazard-bearing body vulnerability, and disaster resilience were analyzed in compartment analysis. At the same time, a new rainstorm and flood disaster prediction model was constructed in combination with the hybrid PSO-SVR algorithm. The research results show that there are many rivers in Henan Province, the terrain tends to be higher in the west and lower in the east, and most areas are low plains, making most cities in Henan Province at a moderate risk level. For the more developed cities such as Zhengzhou, Luoyang, and Nanyang, the hazard risk, sensitivity, vulnerability, and disaster resistance are high, and they are prone to heavy rains and floods. For the economically underdeveloped, the terrain is high or hills, such as Sanmenxia City; Xinyang City and other places have low hazard risk and are not prone to rainstorms and floods. By constructing a hybrid PSO-SVR model, selecting two representative cities of Zhengzhou and Luoyang, and predicting the daily rainfall, the number of disasters, and the direct economic loss, the calculated RMSE and MAPE values are both less than GA-SVR, the traditional SVR, and BPNN models, which have verified the superiority of the model proposed in this study and the practical value it brings. To further verify the prediction accuracy of the hybrid model, the average value of RMSE and MAPE of other 16 cities are calculated, and the result is still smaller than other three models, and the study can provide some decision-making references for the urban rainstorm and flood management.

Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollution disaster risk. This research has integrated principal component analysis (PCA), a genetic algorithm (GA) and a backpropagation (BP) neural network to evaluate the regional disaster risk. Hazard risk, hazard-laden environment sensitivity, hazard-bearing body vulnerability and disaster resilience were used to measure the degree of disaster risk. The main findings were: (1) the air pollution disaster risk index of Liaoning Province, Beijing, Shanghai and Guangdong Province increased year by year from 2010 to 2019; (2) the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of each regional air pollution disaster risk index in 2019, as predicted by the PCA-GA-BP neural network, were 0.607, 0.317 and 20.3%, respectively; (3) the predicted results were more accurate than those using a PCA-BP neural network, GA-BP neural network, traditional BP neural network, support vector regression (SVR) or extreme gradient boosting (XGBoost), which verified that machine learning could be used as a method of air pollution disaster risk assessment to a considerable extent.

With the development of industrialization and urbanization, air pollution is becoming more and more serious. To protect people's health, reasonably predict air quality and provide suggestions for people's egress, this article constructs a short-term air quality prediction model based on K-Means-LSTM. The results show that the daily meteorological data of three provinces in Central China, Henan Province, Hubei Province and Hunan Province from 2017 to 2019 are selected, the daily average AQI is taken as the target variable, and the provincial capital city of each province from October to December 2019 is selected as the test data. The prediction accuracy of K-Means-LSTM model is better than LSTM, BPNN and XGBoost, indicating the practicability of the model proposed in this research.