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Many Chinese cities have severe air pollution due to the rapid development of the Chinese economy, urbanization, and industrialization. Particulate matter (PM2.5) is a significant component of air pollutants. It is related to cardiopulmonary and other systemic diseases because of its ability to penetrate the human respiratory system. Forecasting air PM2.5 is a critical task that helps governments and local authorities to make necessary plans and actions. Thus, in the current study, we develop a new deep learning approach to forecast the concentration of PM2.5 in three major cities in China, Beijing, Shijiazhuang, and Wuhan. The developed model is based on the Informer architecture, where the attention distillation block is improved with a residual block-inspired structure from efficient networks, and we named the model ResInformer. We use air quality index datasets that cover 98 months collected from 1 January 2014 to 17 February 2022 to train and test the model. We also test the proposed model for 20 months. The evaluation outcomes show that the ResInformer and ResInformerStack perform better than the original model and yield better forecasting results. This study's methodology is easily adapted for similar efforts of fast computational modeling. [ABSTRACT FROM AUTHOR]

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances. [ABSTRACT FROM AUTHOR]

In this study, we propose an improved version of the adaptive neuro-fuzzy inference system (ANFIS) for forecasting the air quality index in Wuhan City, China. We propose a hybrid optimization method to improve ANFIS performance, called PSOSMA, using a new modified meta-heuristics (MH) algorithm, Slime mould algorithm (SMA), which is improved by using the particle swarm optimizer (PSO). The proposed PSOSMA-ANFIS has been trained with air quality index time series data of three years and has been applied to forecast the fine particulate matter (PM2.5), sulfur dioxide (SO2), carbon dioxide (CO2), and nitrogen dioxide (NO2) for one year. We also compared the proposed PSOSMA to other MH algorithms used to train ANFIS. We found that the modified ANFIS using PSOSMA achieved better performance than compared algorithms. Moreover, we analyzed the impacts of the lockdown of Wuhan City on the concentrations of PM2.5, NO2, CO2, and SO2. We compared the correspondence period with previous years, and we concluded that there are significant decreases in the concentrations of PM2.5, CO2, SO2, and NO2. • To provide efficient time-series forecasting model for air quality index based on modified ANFIS using SMA and PSO algorithms. • The developed PSOSMA improves the performance of the traditional ANFIS model and produces significant results. • We implement extensive comparisons with several existing MH algorithms to verify the efficiency of the PSOSMA-ANFIS model. • Study the impacts of lockdown on air quality of Wuhan City, the first lockdown city due to the spread of COVID-19. [ABSTRACT FROM AUTHOR]