Air Quality Prediction using Machine Learning Algorithms –A Review

Madan, Tanisha; Sagar, Shrddha; Virmani, Deepali

doi:10.1109/icacccn51052.2020.9362912

Cited by 48 publications

(18 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Models such as the BP neural network and the convolutional neural network are widely used in air quality prediction tasks [ 33 ]. However, the BP algorithm in the above models makes it very easy for them to fall into a local optimum during the training process, and the large number of iterative calculations causes their convergence speed to be too slow.…”

Section: Methodsmentioning

confidence: 99%

An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

Jiang

Zhu

Shen

et al. 2022

Entropy

View full text Add to dashboard Cite

Air quality has a significant influence on people’s health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. Since the concentration data of air pollutants are time series, their time characteristics should be considered in their prediction. However, the traditional neural network for time series prediction is limited by its own structure, which makes it very easy for it to fall into a local optimum during the training process. The empirical mode decomposition fuzzy forecast model for air quality, which is based on the extreme learning machine, is proposed in this paper. Empirical mode decomposition can analyze the changing trend of air quality well and obtain the changing trend of air quality under different time scales. According to the changing trend under different time scales, the extreme learning machine is used for fast training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that our model improves the accuracy of both short-term and long-term prediction by about 30% compared to other models, which indicates the remarkable efficacy of our approach. The research of this paper can provide the government with accurate future air quality information, which can take corresponding control measures in a targeted manner.

show abstract

Section: Methodsmentioning

confidence: 99%

An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

Jiang

Zhu

Shen

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…By applying the classification and regression tree method, Gocheva-llieva et al [70] presented a model for forecasting daily PM 10 concentration with 90% accuracy in Ruse and Pernik (Bulgaria). Madan et al [71] mentioned that a variety of machine learning methods, including linear regression, decision tree, random forest, neural network, and support vector machine, have been used to predict quality of air. The air quality prediction model developed by Mahalingam et al [72] using the neural network algorithm and support vector machine proved effective.…”

Section: Figurementioning

confidence: 99%

Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

et al. 2022

View full text Add to dashboard Cite

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.

show abstract

“…According to Rogers (2019), the impacts of air pollution are not just limited to the environment but can harm historical sites and structures, creating irreparable harm with long-term implications (Rogers, 2019). Madan et al (2020) evaluated by comparing and evaluating almost 20 various literature review works on pollution, ML methods and their performance. Many efforts integrated meteorological data to estimate pollution levels better (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many efforts integrated meteorological data to estimate pollution levels better (2021). It was also discovered that the Artificial Neural-Network (ANN) and the boosting learning models beat other leading ML techniques (Madan et al, 2020). Madhuri et al (2020) Wind gusts, prevailing winds, humidity and warmth were all found have an effect on the hazardous pollutant concentration in the air.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Analysis of Air Pollution Using Machine Learning Techniques

Israfil

Bhatnagar

Juneja

et al. 2022

Asia-Pacific Journal of Management Research and Innovation

View full text Add to dashboard Cite

Air pollution is a major source of worry for all living things. India has one of the world’s highest levels of air pollution. Rising population, unplanned growth, increased automotive traffic, stubble burning, industrial waste, fossil fuel combustion, powerplant emissions and a variety of other causes all contribute considerably to air pollution in developing countries. Particulate matter (PM) 2.5 is the most concerning of all air pollutants since it causes major health problems in individuals. Prediction and management of air quality have therefore become critical. Several machine learning algorithms were used in this work to examine dataset results. The results of our work suggest that for future predictions, logistic regression and autoregression can be efficaciously utilised for the analysis and forecasting of levels of PM2.5 in the future. Countries can lower the prevalence of strokes, and chronic and acute respiratory illnesses such as asthma, and lung cancer by reducing air pollution levels.

show abstract

Air Quality Prediction using Machine Learning Algorithms –A Review

Cited by 48 publications

References 10 publications

An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Predictive Analysis of Air Pollution Using Machine Learning Techniques

Contact Info

Product

Resources

About