The non-stationarity, nonlinearity and complexity of the PM_2.5 series have caused difficulties in PM_2.5 prediction. To improve prediction accuracy, many forecasting methods have been developed. However, these methods usually do not consider the importance of data preprocessing and have limitations only using a single forecasting model. Therefore, this paper proposed a new hybrid decomposition–ensemble learning paradigm based on variation mode decomposition (VMD) and improved whale-optimization algorithm (IWOA) to address complex nonlinear environmental data. First, the VMD is employed to decompose the PM_2.5 sequences into a set of variational modes (VMs) with different frequencies. Then, an ensemble method based on four individual forecasting approaches is applied to forecast all the VMs. With regard to ensemble weight coefficients, the IWOA is applied to optimize the weight coefficients, and the final forecasting results were obtained by reconstructing the refined sequences. To verify and validate the proposed learning paradigm, four daily PM_2.5 datasets collected from the Jing-Jin-Ji area of China are chosen as the test cases to conduct the empirical research. The experimental results indicated that the proposed learning paradigm has the best results in all cases and metrics.

With the increasing uncertainty and complexity of disasters, the tourism industry in disaster-stricken areas is often hard hit, so it is urgent to improve the resilience of the tourism industry. Based on the perspective of regional economic resilience, this study takes Wenchuan earthquake as an example to explore the theoretical framework, research ideas and methods of tourist destination resilience. The industry policy should be included in the resilience construction to theoretically respond to the reasons for the improvement of the resilience and sustainability of tourism industry in the post-disaster recovery. Taking Sichuan tourism as the research object, this paper estimated the impact of Wenchuan earthquake on Sichuan tourism and the effect of industrial policies on the resilience of the tourism industry in the post-disaster recovery. The results show that the total revenue loss of Sichuan tourism industry brought by Wenchuan earthquake is 87.298 billion yuan, and the post-disaster recovery window period is 7 years (2008–2014). After the earthquake, the industrial policy measures taken have effectively improved the resilience of Sichuan’s tourism industry, making it reach the level that it would have been before the earthquake and assuming that there had been no earthquake.

Air pollution is a prominent problem all over the world, seriously endangering human life. To protect the environment and human health, timely and accurate air quality evaluations are imperative. Recently, with the increasing focus on air pollution, an evaluation tool that can offer intuitive air quality information is especially needed. Though the Air Quality Index (AQI) has played this role over the years, its intrinsic limitations discussed in this study (sharp boundary, biased evaluation, conservative strategy and incomplete criterion) are gradually apparent, limiting its air quality evaluation capability. Therefore, a novel paradigm, the Air Quality Fuzzy Comprehensive Evaluation (AQFCE), is proposed. In the preprocessing module, missing and reversal data are handled by a least square piecewise polynomial fitting and linear regression. An improved fuzzy comprehensive evaluation model is adopted to solve the AQI’s above limitations in the evaluation module. The early warning module provides a timely alert and recommendation. To validate the performance of the AQFCE, Beijing, Shanghai and Xi’an in China are selected for case studies, and daily and hourly concentration data of six conventional air pollutants from September 2018 to August 2019 are employed. For daily reports, the AQFCE and AQI have a high consistent rate and correlation coefficient regarding chief pollutants and levels, respectively, while examples show the level of the AQFCE is more reasonable. For hourly reports, AQI has antinomies and cannot reflect actual pollution, but the AQFCE is still effective. Current major pollutants, “weekend and holiday effect” and “peak type” of pollution are also revealed by the AQFCE. Experiment results prove that the AQFCE is accurate under different pollution conditions and an important supplement to the AQI. Furthermore, the AQFCE can provide health guideline for the public and assist the government in making environmental decisions and development policies.

Practical forecasting of air pollution components is important for monitoring and providing early warning. The accurate prediction of pollutant concentrations remains a challenging issue owing to the inherent complexity and volatility of pollutant series. In this study, a novel hybrid forecasting method for hourly pollutant concentration prediction that comprises a mode decomposition-recombination technique and a deep learning approach was designed. First, a Hampel filter was used to remove outliers from the original data. Subsequently, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) is employed to divide the original pollution data into a finite set of intrinsic mode function (IMF) components. Further, a feature extraction method based on sample-fuzzy entropy and K-means is proposed to reconstruct the main features of IMFs. In conclusion, a deterministic forecasting model based on long short-term memory (LSTM) was established for pollutant prediction. The empirical results of six-hourly pollutant concentrations from Baoding illustrate that the proposed decomposition-recombination technique can effectively handle nonlinear and highly volatile pollution data. The developed hybrid model is significantly better than other comparative models, which is promising for early air quality warning systems.