Air pollutant prediction based on ARIMA-WOA-LSTM model

VAR-tree model based spatio-temporal characterization and prediction of O3 concentration in China.

In this paper, we explore the computational capabilities of advanced modeling tools to reveal the factors that shape the observed benzene levels and behavior under different environmental conditions. The research was based on two-year hourly data concentrations of inorganic gaseous pollutants, particulate matter, benzene, toluene, m, p-xylenes, total nonmethane hydrocarbons, and meteorological parameters obtained from the Global Data Assimilation System. In order to determine the model that will be capable of achieving a superior level of performance, eight metaheuristics algorithms were tested for eXtreme Gradient Boosting optimization, while the relative SHapley Additive exPlanations values were used to estimate the relative importance of each pollutant level and meteorological parameter for the prediction of benzene concentrations. According to the results, benzene levels are mostly shaped by toluene and the finest aerosol fraction concentrations, in the environment governed by temperature, volumetric soil moisture content, and momentum flux direction, as well as by levels of total nonmethane hydrocarbons and total nitrogen oxide. The types of conditions which provided the environment for the impact of toluene, the finest aerosol, and temperature on benzene dynamics are distinguished and described.

/pdf/the-explainable-potential-of-coupling-metaheuristics-12fiyals.pdf

The Explainable Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing VOCs’ Environmental Fate

Machine learning mechanism is establishing itself as a promising area for modelling and forecasting complex time series over conventional statistical models. In this article, focus has been made on presenting a machine learning algorithm with special attention to deep learning model in form of a potential alternative to statistical models such as Autoregressive Integrated Moving Average (ARIMA) and ARIMA-Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Further, an improved hybrid ARIMA-Long Short-Term Memory (LSTM) model based on the random forest lag selection criterion has been introduced. ARIMA model has been used to estimate the mean effect and the GARCH model is employed with the residuals obtained from the ARIMA model to estimate the volatile behaviour of the series. ARIMA-GARCH models act as superior statistical models over ARIMA models based on the lowest AIC and BIC values. LSTM model is employed on all normalised training data series. After which we built a comparison scenario independently between ARIMA, ARIMA-GARCH, LSTM and ARIMA-LSTM models on forecasting accuracy in terms of the lowest RMSE, MAPE and MASE values. The proposed random forest-based ARIMA-LSTM model proved its superiority over the conventional statistical models with an improvement to the tune of 8–25% for RMSE, 2–28% for MAPE and 2–29% for MASE. The proposed hybrid model has been successfully applied to volatile monthly price indices of pulses namely gram, moong and urad. This piece of work will enrich the literature on machine learning and further intrigue researchers to apply it to various other volatile data sets. 

An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique

Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM

Fine particulate matter (PM2.5) has a continuing impact on the environment, climate change and human health. In order to improve the accuracy of PM2.5 estimation and obtain a continuous spatial distribution of PM2.5 concentration, this paper proposes a LUR-GBM model based on land-use regression (LUR), the Kriging method and LightGBM (light gradient boosting machine). Firstly, this study modelled the spatial distribution of PM2.5 in the Chinese region by obtaining PM2.5 concentration data from monitoring stations in the Chinese study region and established a PM2.5 mass concentration estimation method based on the LUR-GBM model by combining data on land use type, meteorology, topography, vegetation index, population density, traffic and pollution sources. Secondly, the performance of the LUR-GBM model was evaluated by a ten-fold cross-validation method based on samples, stations and time. Finally, the results of the model proposed in this paper are compared with those of the back propagation neural network (BPNN), deep neural network (DNN), random forest (RF), XGBoost and LightGBM models. The results show that the prediction accuracy of the LUR-GBM model is better than other models, with the R2 of the model reaching 0.964 (spring), 0.91 (summer), 0.967 (autumn), 0.98 (winter) and 0.976 (average for 2016–2021) for each season and annual average, respectively. It can be seen that the LUR-GBM model has good applicability in simulating the spatial distribution of PM2.5 concentrations in China. The spatial distribution of PM2.5 concentrations in the Chinese region shows a clear characteristic of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The seasonal variation in mean concentration values is marked by low summer and high winter values. The results of this study can provide a scientific basis for the prevention and control of regional PM2.5 pollution in China and can also provide new ideas for the acquisition of data on the spatial distribution of PM2.5 concentrations within cities.

/pdf/spatio-temporal-characteristics-of-pm2-5-concentrations-in-1hac413x.pdf

Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016–2021

Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing

Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility

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