Air Quality Index Forecasting Research Articles

Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India.

The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues to rise in global cities due to urbanization and climate change, air pollution monitoring and forecasting models for effective air quality monitoring that gather and forecast information about air pollution concentration are essential in every city. Air quality predictions have evolved to be more helpful for management. Recently, better performance and ability have developed due to the involvement of machine learning (ML) and artificial intelligence (AI) in forecasting air quality in urban cities in India. This paper focuses on air pollution as a significant ecological problem that directly impacts human health and the distribution of an environmental system in urban areas. Hence, we have developed advanced models for daily AQI forecasting to understand the air effluence level in the upcoming days. In this research, six data-driven models have been developed and implemented for daily AQI forecasting in the study area; it is crucial for understanding the future air pollution levels to plan and control air pollution in the entire city. The developed model is applied to air quality datasets. A comparison of the performance of ML models tested here indicates that the XGBoost algorithm achieves the highest coefficient of determination (R2) and root-mean-square deviation (RMSE) value of 0.99 and lower values value of 4.65 than other models in the testing phase. The results of the artificial neural network (ANN) algorithm are slightly lower than the extreme gradient boosting (XGBoost model); the ANN model results are as R2, mean squared error (MSE), and RMSE values of 0.99, 13.99, and 198.88, respectively. All the models were subjected to a ten-fold cross-validation model. However, the RF cross-validation model outperforms other models; the RF model result shows the R2, RMSE, and MSE values of 0.99, 3.64, and 4.12, respectively. This study also employed two interpretable models, namely feature importance analysis and Shapley additive explanation (SHAP), to evaluate both the global and local methods in a manner that is independent of specific ML models. The feature importance shows that particle matter (PM) 2.5, PM10, carbon monoxide (CO), and nitrogen oxides (NOx) were the most influential variables. The results determined that such novel DL and ML models may improve the accuracy of AQI forecasts and understanding of air pollution, particularly in metropolitan cities.

Optimized ensemble deep random vector functional link with nature inspired algorithm and boruta feature selection: Multi-site intelligent model for air quality index forecasting

Air quality index (AQI) forecasting is complex due to its variability, instability, and inconsistent trends resulting from dynamic atmospheric conditions, various contaminants, and interactions between environmental factors. Advanced modeling techniques are needed to accurately forecast AQI values to capture subtle patterns and variations in air quality data. Thus, a new forecasting model is suggested in this study to improve the accuracy of AQI forecasting. The model integrates three-phase decomposition technique, a feature selection approach, and ensemble Deep Random Vector Functional Link (EDRVFL), optimized using adaptive teaching-learning-based optimization and differential evolution (ATLDE). The AQI series was first broken down into a group of intrinsic mode functions (IMFs) with different frequencies using multivariate variational mode decomposition (MVMD). Subsequently, a feature selection method based on the Boruta technique was applied to identify the most significant input variables. Finally, for daily AQI levels forecasting, ATLDE optimized the EDRVFL model (EDRVFL-ATLDE). Three daily AQI series gathered from Chengdu, Wuhan, and Taiyuan in China from January 1, 2018, to December 30, 2022, were used to test and confirm the proposed model via empirical research. Based on the results, the proposed model can yield the superior results for three cities (Chengdu: correlation coefficient (R = 0.987), root mean square error (RMSE = 5.583), Wuhan: (R = 0.987), (RMSE = 3.299), and Taiyuan: (R = 0.996), (RMSE = 4.521)) in China. The experimental findings demonstrated the feasibility of the three-phase hybrid methodology, outperforming all other models regarding forecast accuracy.

Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments

Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China

Multi-Site Air Quality Index Forecasting Based on Spatiotemporal Distribution and PatchTST-Enhanced: Evidence From Hebei Province in China

A Novel Combined Model for Air Quality Index Forecasting in Changchun

With the rapid development of the economy and continuous improvement in people’s living standards, the predictions of the air quality index have attracted wide attention. In this paper, a new feature selection method (Pearson-MI) and a combined model construction method (modified inverse variance method) were proposed to study the air quality index (AQI) and its influencing factors in Changchun. The Pearson-MI method selects the factors that affect the AQI of Changchun City from many influencing factors. This method reduces the RMSE of the LSTM model and XGBoost model by 27% and 5% and the MAE by 41% and 5%, respectively. A model that combines XGBoost, SVR, RF, and LSTM was constructed using the inverse variance method to predict the air quality index of Changchun City. The modified combined model resulted in a 2% reduction in RMSE and a 0.6% reduction in MAE compared with the unmodified combined model. The numerical results of our study show that the prediction accuracy of the modified combined model is obviously higher than that of the basic model, and the prediction accuracy is further improved under the Pearson-MI feature selection.

An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables

Forecasting the air quality index (AQI) is a critical and pressing challenge for developing nations worldwide. With air pollution emerging as a significant threat to the environment, this study considers seven study sites of the sub-tropical region in Bangladesh and introduces a novel hybrid deep-learning model. The proposed model, expressed as CLSTM-BiGRU, integrates a convolutional neural network (CNN), a long-short term memory (LSTM), and a bi-directional gated recurrent unit (BiGRU) network. Leveraging nineteen remotely sensed predictor variables and harnessing the grey wolf optimization (GWO) algorithm, the CLSTM-BiGRU model showcases its superiority in air quality forecasting. It consistently outperforms the benchmark models, yielding lower forecasting errors and higher efficiency (i.e., correlation coefficient ~1) values. Hence, this study underscores the feasibility and substantial potential of the hybrid deep learning model, which can provide precise forecasts of air quality index, and will be highly useful for relevant stakeholders and decision-makers. Furthermore, the adaptability and potential utility of this innovative model may be ascertained for air quality monitoring and effective public health risk mitigation in urban environments.

Predicting Air Quality Index using Ensemble Machine Learning

This study focuses on utilizing Ensemble Machine Learning to predict and forecast the Air Quality Index (AQI). The research is motivated by the adverse effects of industrialization and population growth on air quality, leading to detrimental impacts on human health. While numerous methodologies exist for air quality prediction, it is crucial to anticipate future air conditions to minimize their larger consequences. Hence, this study proposes an air quality evaluation system to facilitate future predictions. The study comprises three primary modules: Data Preparation, AQI Forecasting, and Evaluating Air Quality. The Data Preparation Module involves real-time data collection and formatting to ensure compatibility with subsequent modules. In this research, the Sparse Spectrum GPR (SSGPR) method is employed for AQI forecasting, while the cloud model is adopted for air quality evaluation. The study's findings demonstrate the capability of the proposed model to account for the uncertainty and randomness inherent in air quality prediction. Performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are employed to evaluate the models' effectiveness. Based on the evaluation results, it can be concluded that the Ensemble Machine Learning method utilized in this study effectively predicts and forecasts the Air Quality Index. These predictions play a crucial role in minimizing the adverse impact of air pollution on human health by providing insights into future air conditions. Overall, this research contributes significantly to comprehending and addressing the increasingly urgent challenges associated with air quality.

Air Quality Index Prediction Based on a Long Short-Term Memory Artificial Neural Network Model

<p>Air pollution has become one of the important challenges restricting the sustainable development of cities. Therefore, it is of great significance to achieve accurate prediction of Air Quality Index (AQI). Long Short Term Memory (LSTM) is a deep learning method suitable for learning time series data. Considering its superiority in processing time series data, this study established an LSTM forecasting model suitable for air quality index forecasting. First, we focus on optimizing the feature metrics of the model input through Information Gain (IG). Second, the prediction results of the LSTM model are compared with other machine learning models. At the same time the time step aspect of the LSTM model is used with selective experiments to ensure that model validation works properly. The results show that compared with other machine learning models, the LSTM model constructed in this paper is more suitable for the prediction of air quality index.</p> <p> </p>

A new perspective on air quality index time series forecasting: A ternary interval decomposition ensemble learning paradigm

Accurate forecasting of the air quality index (AQI) plays a crucial role in taking precautions against upcoming air pollution risks. However, air quality may fluctuate greatly in a certain period. Existing forecasting approaches always face the problem of losing valuable information on air quality status, even in the interval models of recent research. To address this issue, this paper suggests a new AQI forecasting perspective and paradigm built upon ternary interval-valued time series (TITS), multivariate variational mode decomposition (MVMD), multivariate relevance vector machine (MVRVM), mixed coding particle swarm optimization (MCPSO), and meteorological factors, which is able to capture the trend and volatility changes of AQI concurrently. The proposed paradigm involves four procedures: TITS construction in terms of the daily minimum, daily mean, and daily maximum AQI, multi-scale decomposition via MVMD, individual forecasting by MCPSO-optimized MVRVM, and ensemble learning forecasting using a simple addition approach. Experiments based on datasets collected from four municipalities in China demonstrated that the presented paradigm can hit higher accuracy than other comparable models, and the application analysis also shows that it has application potential in the AQI online forecasting system. To conclude, the proposed paradigm provides a promising alternative for AQI time series forecasting.

Analysis and forecasting of air quality index based on satellite data

Objective The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI. Materials and methods Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla). Results AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores. Conclusions Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.

Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine

Air Quality Index Forecasting via Genetic Algorithm-Based Improved Extreme Learning Machine

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.

An ensemble LSTM-based AQI forecasting model with decomposition-reconstruction technique via CEEMDAN and fuzzy entropy.

Air quality affects people’s daily life. Air quality index (AQI) is an essential indicator for controlling air pollution and ensuring public health, whose accurate forecasting can provide timely air pollution warnings and remind people to take protective measures against air pollution in advance. To address this issue, this paper developed a new ensemble learning model for AQI forecasting. In this study, (1) the signal decomposition technique complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is introduced to decompose the nonlinear and nonstationary AQI history data series into several more regular and more stable subseries firstly. (2) Fuzzy entropy (FE) is selected as the feature indicator to recombine the subseries with similar trends to avoid the problem of over-decomposition and reduce the computing time. (3) An ensemble long short-term memory (LSTM) neural network is established to forecast each reconstructed subseries, whose values are superimposed to predict the AQI value eventually. To validate the predicting performance of the proposed model, daily AQI data of Wuhan, China, dating from January 1, 2019, to February 28, 2022, is used as the experiment case. And comparative analysis is made between the proposed model and other common-used forecasting models. Benchmarking results of the numerical study demonstrate that the proposed model is superior to the other forecasting models with better AQI prediction accuracy.

An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach

Accurate, timely air quality index (AQI) forecasting helps industries in selecting the most suitable air pollution control measures and the public in reducing harmful exposure to pollution. This article proposes a comprehensive method to forecast AQIs. Initially, the work focused on predicting hourly ambient concentrations of PM2.5 and PM10 using artificial neural networks. Once the method was developed, the work was extended to the prediction of other criteria pollutants, i.e., O3, SO2, NO2, and CO, which fed into the process of estimating AQI. The prediction of the AQI not only requires the selection of a robust forecasting model, it also heavily relies on a sequence of pre-processing steps to select predictors and handle different issues in data, including gaps. The presented method dealt with this by imputing missing entries using missForest, a machine learning-based imputation technique which employed the random forest (RF) algorithm. Unlike the usual practice of using RF at the final forecasting stage, we utilized RF at the data pre-processing stage, i.e., missing data imputation and feature selection, and we obtained promising results. The effectiveness of this imputation method was examined against a linear imputation method for the six criteria pollutants and the AQI. The proposed approach was validated against ambient air quality observations for Al-Jahra, a major city in Kuwait. Results obtained showed that models trained using missForest-imputed data could generalize AQI forecasting and with a prediction accuracy of 92.41% when tested on new unseen data, which is better than earlier findings.

Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm

AbstractThis research presents a hybrid model for multi‐step, interval forecasting of air quality indices. An efficient preprocessing module is applied to split the raw data into various sub‐series, and the optimal mode of data input is determined through feature selection. A multi‐objective optimization algorithm is proposed to tune the parameters of kernel extreme learning machine to achieve high accuracy and stability. An evaluation with several error criteria, benchmark models, and critique is conducted using three daily air quality index datasets from three cities of China. Empirical results indicate that the developed model achieves superior predictions of air quality indices, which may be useful in policies for mitigating air pollution.

Multivariate Air Quality Forecasting With Nested Long Short Term Memory Neural Network

Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic in the fields of sustainable and smart industrial environment design. There are mainly two obstacles that hinder the existing machine learning (ML) and deep learning (DL) technologies providing accurate forecasting results to protect the environment, which include the intercorrelation between different AQI components and the highly volatile AQI pattern changes. In this article, a novel DL framework combining multiple nested long short term memory networks (MTMC-NLSTM) is proposed for accurate AQI forecasting enlightened with the federated learning. The performance of the proposed MTMC-NLSTM model is compared with conventional ML models, DL methods, as well as hybrid DL models. The experimental results show that the performance of the proposed method is superior to those of all compared models.

Multi-scale deep learning and optimal combination ensemble approach for AQI forecasting using big data with meteorological conditions

Faced with the rapid update of nonlinear and irregular big data from the environmental monitoring system, both the public and managers urgently need reliable methods to predict possible air pollutions in the future. Therefore, a multi-scale deep learning (MDL) and optimal combination ensemble (OCE) approach for hourly air quality index (AQI) forecasting is proposed in this paper, named MDL-OCE model. Before normal modeling, all original data are preprocessed through missing data filling and outlier testing to ensure smooth computation. Due to the complexity of such big data, slope-based ensemble empirical mode decomposition (EEMD) is adopted to decompose the time series of AQI and meteorological conditions into a finite number of simple intrinsic mode function (IMF) components and one residue component. Then, to unify the number of components of different variables, the fine-to-coarse (FC) technique is used to reconstruct all components into high frequency component (HF), low frequency component (LF), and trend component (TC). For purpose of extracting the underlying relationship between AQI and meteorological conditions, the three components are respectively trained and predicted by different deep learning architectures (stacked sparse autoencoder (SSAE)) with a multilayer perceptron (MLP). The corresponding forecasting results of three components are merged by OCE method to better achieve the ultimate AQI forecasting outputs. The empirical results clearly demonstrate that our proposed MDL-OCE model outperforms other advanced benchmark models in terms of forecasting performances in all cases.

Air Quality Index Prediction in Realtime Using SVM based model in Machine Learning

The air quality index is an index to decide the situation of the air quality. The air quality index is a measure of how air pollutants impact a persons' fitness within a time period. It is a standardized degree this is used to suggest the pollutant (so2, no2, pm 2.5, pm 10, etc.) levels. We designed a model that could estimate the air quality index based totally on ancient records of a few preceding years. The performance of this model is progressed through making use of numerous Estimation-Problem logics. Our model could be able to correctly predict the air quality index of a complete county or any nation or any bounded area supplied with the ancient records of pollutant concentration. In our model by implementing a support-vector machine, we achieved better performance than other models and for that our model gets an accuracy of 96%. With the help of support-vector machine, our model estimates the air quality to predict the air quality index of a given location primarily based totally on its ancient records of the pollution of a few preceding years. Our purpose is to increase a non-linear updatable version for real-time air quality index forecasting, to doubtlessly update the models presently being used.