Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM2.5 Concentration Forecasting: A Case Study of Beijing, China

Machine learning-based Decision Tree J48 with grey wolf optimizer for environmental pollution control

ABSTRACTDue to industrialization, activities of human and urbanization, environment is getting polluted. Air pollution has become a main issue in the metropolitan areas of the world. To protect people from diseases, monitoring air quality plays an important thing. This air pollutant may lead to many health issues like respiratory and cardiac problems. The major air pollutants are NO, C₆H₆, CO, etc. Many research works have been done in predicting air pollution-based health issues, predicting air pollution levels, monitoring and controlling the polluted levels. But they are not efficient, cost of maintenance is high and insufficient tool for monitoring it. To overcome these issues, this paper implements hybrid algorithm of Decision Tree J48 and Grey Wolf Optimizer (DT-GWO). This DT-GWO is a better model to addresses the predicting of Air Quality Index (AQI), which minimizes the error rate, accurately and effectively predicting the air quality. The AQI values are categorised as good, moderate, unhealthy, very unhealthy and hazardous. The dataset used in this work is collected from Kaggle website which contains air pollutants details with air quality index values. Accuracy obtained for decision Tree J48 is 93.72%, grey wolf optimizer is 96.83% and our proposed work DT-GWO is 99.78%.

A novel hybrid prediction model for PM2.5 concentration based on decomposition ensemble and error correction

PM_2.5 concentration is an important index to measure the degree of air pollution. It is necessary to establish an accurate PM_2.5 concentration prediction system for urban air monitoring and control. Due to the nonlinear characteristics of PM_2.5 concentration, it is difficult to predict it directly. Therefore, a novel hybrid model for PM_2.5 concentration based on improved variational mode decomposition (IVMD), outlier-robust extreme learning machine (ORELM) optimized by hybrid cuckoo search (CS), and chimp optimization algorithm (ChOA), error correction (EC) is proposed named IVMD-ChOACS-ORELM-EC. First of all, an improved VMD based on energy loss coefficient, named IVMD, is proposed. IVMD decomposes the original data to obtain K IMF components. Then, a hybrid optimization algorithm based on ChOA improved by CS is proposed, named ChOACS. The hybrid optimization algorithm is used to optimize ORELM. On this basis, the prediction model ChOACS-ORELM is proposed, and the K IMF components are predicted by ChOACS-ORELM. Finally, the EC model based on decomposition ensemble is established to further improve the prediction accuracy. The PM_2.5 concentration data collected at hourly intervals in Beijing, Shanghai, Shenyang, and Qingdao in China are used as experimental data. The experimental results show that the correlation coefficients between the prediction results and the actual values of the four cities are 0.9999, and the prediction performance of the proposed model is better than that of all comparison models.

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

COVID-19 pandemic and its positive impacts on environment: an updated review

In December, 2019 in Wuhan city of China, a novel coronavirus (SARS-CoV-2) has garnered global attention due to its rapid transmission. World Health Organization (WHO) termed the infection as Coronavirus Disease 2019 (COVID-19) after phylogenic studies with SARS-CoV. The virus causes severe respiratory infections with dry cough, high fever, body ache and fatigue. The virus is primarily transmitted among people through respiratory droplets from COVID-19 infected person. WHO declared this COVID-19 outbreak a pandemic and since February, 2020 affected countries have locked down their cities, industries and restricted the movement of their citizens to minimize the spread of the virus. In spite of the negative aspects of coronavirus on the globe, the coronavirus crises brought a positive impact on the natural environment. Countries where the movement of citizens was seized to stop the spread of coronavirus infection have experienced a noticeable decline in pollution and greenhouse gases emission. Recent research also indicated that this COVID-19-induced lockdown has reduced the environmental pollution drastically worldwide. In this review, we have discussed some important positive impacts of coronavirus on environmental quality by compiling the recently published data from research articles, NASA (National Aeronautics and Space Administration) and ESA (European Space Agency).

Double decomposition and optimal combination ensemble learning approach for interval-valued AQI forecasting using streaming data

To forecast possible future environmental risks, numerous models are developed to predict the hourly values or daily averages of air pollutant concentrations using streaming data (a kind of big data collected from the Internet). On the one hand, real-time hourly data is massive and redundant, making it difficult to process. On the other hand, daily averages cannot reflect the fluctuations of air pollutant concentrations throughout the day. Therefore, a double decomposition and optimal combination ensemble learning approach is proposed for interval-valued AQI (air quality index) forecasting in this paper. In the first decomposition, considering the strong seasonal representation of AQI, the original data of each year is decomposed into four seasonal subseries on the basis of the Chinese calendar. Subsequently, we reconstruct the data of the same season in different years to get a new seasonal series to reduce the interference of seasonal changes on AQI forecasting. In the second decomposition, due to the nonlinearity and irregularity of interval-valued AQI time series, BEMD (bivariate empirical mode decomposition) is employed to decompose the interval-valued signals into a finite number of complex-valued IMF (intrinsic mode function) components and one complex-valued residue component with different frequencies to reduce the complexity of interval times series. Interval multilayer perceptron (iMLP) is utilized to model the lower bound and the upper bound simultaneously of the total components to obtain the corresponding forecasting results, which are merged to produce the final interval-valued output by an optimal combination ensemble method. Empirical study results show that the proposed model with different datasets and different forecasting horizons is significantly better than other considered models for its superior forecasting performances.

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

A Multi-Objective Optimization Problem for Optimal Site Selection of Wind Turbines for Reduce Losses and Improve Voltage Profile of Distribution Grids

In this paper, the optimal site and size selection of wind turbines (WTs) is presented considering the maximum allowable capacity constraint with the objective of loss reduction and voltage profile improvement of distribution grids based on particle swarm optimization (PSO as a multi-objective problem using weighted coefficients method. The optimal site, size, and power factor of the WTs are determined using PSO. The proposed method is implemented on 84- and 32-bus standard grids. In this study, PSO algorithm is applied to determine the size, site, and power factor of WTs considering their maximum size constraint (with constraint, variant size) and also not considering their maximum size constraint (without constraint, constant size). The simulation results showed that the PSO is effective to find the site, size, and power factor of WTs optimally in the single and multi-objective problem. The results of this method showed that the power loss is reduced more and voltage profile improved more considering WTs maximum allowable size versus not considering this constraint. Additionally, the multi-objective results showed that there is a compromise between the objectives in the multi-objective WTs site selection and the multi-objective problem solution is a more realistic and accurate approach in comparison with the single-objective problem solution.