The predicting process of air quality using machine learning is essential for forecast pollution levels and simplifies time interventions by authorities. The methods involve in predicting air quality are analyzing the pollutant concentration and other environmental variables in the supplementary time.
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Here, we provide the step-by-step procedure to predict air quality,
We create a machine learning model to predict the air quality of future based on the historical data and applicable environmental factors.
EDA pictures us the pollutant distributions and their connection with environmental variables. It is used to detect patterns, seasonality of data or trends.
It offers the impermanent nature of data.
The datasets are divided sequentially by us and make sure that the test set must follow the validation and training in time. The models are trained on the training data and performance of the model is calculated by the validation set.
To enhance the performance of model, we deploy some tools like grid search, random search, or Bayesian optimization.
We create a web application or dashboard for the public that enable to view the air quality predictions. With the help of some platforms like Django, Flask or tools like Tableau used for this process.
Collect feedback or reviews from the users and experts in environment to retune the model for its advancements. Then progress the model frequently with new data to keep up the perfect predictions.
Outline the findings, challenges and the note the areas for development. The possible future advancements are listed below,
Thus, our well-executed and administered air quality prediction system plays a major role in urban planning, public health advisories, and improving public awareness about environmental problems in the quality of air which we inhale.
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Air pollution has become a severe problem due to urbanization, industrialization, and the burning of fossil fuels, among other factors. This paper focuses on the use of data mining techniques for predicting air quality using machine learning. The paper highlights the impact of pollutants such as PM2.5 (particulate matter 2.5), PM10 (particulate matter 10), CO (carbon monoxide), NOx (oxides of nitrogen), SO2 (Sulphur dioxide), and O3 (ozone) on human health, which include respiratory and cardiovascular diseases, asthma attacks, strokes, and even death. We propose using data mining and artificial intelligence techniques to solve the problem. Decision trees are used for classification and regression tasks and work by building a tree-like structure of decisions and their possible outcomes. The tree is constructed by recursively splitting the dataset based on the feature that provides the highest information gain or reduction in impurity until a stopping criterion is met. Decision trees are easy to understand and can handle both continuous and categorical features, making them a popular algorithm in machine learning. The paper also discusses the importance of data mining in machine learning and its ability to identify patterns and relationships that would have otherwise gone unnoticed. This paper offers a practical solution to predict air quality of Bengaluru for the next coming month by analysing the data from the previous 1 year. This provides insights into the use of decision trees and data mining for solving complex problems.
Data mining, Artificial intelligence (AI), Air Quality Index (AQI), Decision tree
By utilizing data mining methods and machine learning, air quality is forecasted in our study. We also discussed about various pollutants that affects the human health by causing several diseases. We carried out categorization and regression tasks by employing DT algorithm. Based on relevant features, we built the tree by repeatedly divides the dataset. Both categorical and continuous characteristics can be interpreted and managed by our suggested algorithm.
Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals’ wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city.
Pollution Detection, Linear Regression, Pollution Prediction, Machine Learning
Commonly, Particulate Matter (PM) value is an essential one to predict the nature of the air. Here, we employed image processing methodologies to identify the PM value. To detect the polluted areas in images, we performed edge detection and depth estimation approaches in image processing and it will identifies and measures the polluted region by using relevant image characteristics. We utilized a learning model to forecast the PM value on the gathered images.
Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, CatBoost, AdaBoost, and XGBoost. The results show that the CatBoost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The AdaBoost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with CatBoost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.
Particulate matter, Gaseous pollutants, Meteorological parameters, Climate action
To forecast the Air Quality Index (AQI), ML approaches are the most encouraging methodology and we utilized various ML techniques such as LightGBM, Random Forest, CatBoost, AdaBoost, and XGBoost. We conclude that, in the process of AQI prediction, CatBoost technique provides greater end results than other techniques and the AdaBoost technique provides the least end results among others.
The rise in air pollution emanating from fossil fuel consumption, tyre wear, quarrying and brick units and industries has its severe harmful effects on environment. Thus, it has made air pollution forecasting a crucial research area today. There is a need to analyze the trend and reasons for temporal variations in AQI levels of Pune city and also to find the locations worst affected by each of the 6 dominant pollutants. In this work, air pollutants concentration data of a year is extracted from Pune Smart City office, the Special Purpose Vehicle (SPV) for implementing Smart City Mission in Pune City. This work deals with pre-processing 1 year data concerning concentration levels of dominant AQI pollutants, then analysis and visualizations are done using data analytics tools of Tableau and Machine Learning decision tree algorithm. Correlation matrix features and Tableau visualizations gives deeper insights about the data. Supervised Machine Learning algorithm – Random Forest and Time Series forecast model of tableau are built to predict the air quality and best fit the data with maximum accuracy. Easily understandable Tableau dashboard trends define the temporal variations clearly. Data analysis provides the method to predict the future air pollution levels so that preventive measures can be implemented by people as well as administration hence making the city literally smart.
AQI bucket, Air Pollution, Python
Our research utilized the collection of air pollutants data to predict the air quality. We preprocessed the data and by utilizing data analytics tools of Tableau and ML based DT technique, we examine and visualize the data. In-depth view of data is provided by correlation matrix features and Tableau visualizations. We predict the air quality by developing a framework that comprises of Supervised ML approach-RF and Time Series prediction model of tableau.
Pollution is a critical and disturbing problem that people encounter daily in today’s world and also has an impact on the quality of air. The issue is so crucial that it cannot be overlooked and its effects are felt everywhere. The climatic variables that affect the AQI, such as NO2, NH3, SO2, CO, O3, fog ,temperature, smoke, dew, mist, benzene, toluene, xylene, etc. The AQI measures the severity of the pollutants present in the air. It classifies the severity of air quality into six categories, each with its own range of values. The categories are as follows: Good, which ranges from 0–50 on the AQI scale, indicating that the air quality is generally safe and healthy for everyone to breathe. Moderate, which ranges from 51–100 on the AQI scale, indicating that the air quality is acceptable but may pose a moderate risk for certain individuals, such as those with respiratory issues. Unhealthy for Sensitive Groups, which ranges from 101–150 on the AQI scale, indicating that the air quality is dangerous for certain individuals, for example the youth or the younger and older ones or people having respiratory problems. Unhealthy, which ranges from 151–200 on the AQI scale, indicating that the air quality is hazardous and can cause serious health problems for everyone. Very Unhealthy, which ranges from 201–300 on the AQI scale, indicating that the air quality is extremely dangerous and can cause severe respiratory and cardiovascular problems. Hazardous, which ranges from 301 and higher on the AQI scale, indicating that the air quality is life-threatening and can cause serious health problems even for those who are otherwise healthy. Overall, the AQI is an essential tool for assessing the severity of air pollution levels and determining the appropriate measures that need to be taken to protect public health. The suggested model aims to evaluate the air quality. The proposed model suggests a strategy for measuring future AQI data from the present and historical AQI data by using automated machine learning techniques. Threshold value might be specified as a similar parameter since TPOT increases the iterations in number, which increases the depth of the node. The data on air pollutants is obtained from the sensors, processed according to a single schema, and then saved as a dataset. This dataset has undergone many preprocessing operations, including normalization, discretization and attribute selection. The machine learning system would learn from the data (pertaining to point in the time) and database to offer the user with comparable statistics to minimize processing time and increase platform efficiency.
Automated machine learning, Tree-based pipeline optimization tool (TPOT), normalization, deep learning, local binary pattern
A major goal of our article is to predict the air quality and we recommended an approach to estimate the future air quality index through the utilization of automatic ML methods by considering present and previous data. We acquired the air pollutants data from sensors and we preprocessed the data for further procedures such as normalization, discretization and attribute selection.
According to the recent studies, statistics of the world health organization say that out of ten people, nine breathe unhealthy air that is not fit for human health. This is responsible for the death of over 7 million annually. Now, if we look in terms of air pollution in case of India, national quality standards of air is much low with compared to the guidelines given by WHO. In India, the concentration of ozone boosts noteworthy that is 17% during the last ten years. In this work, we have applied Support Vector Machine (SVM), K-Nearest Neighbor(KNN), Logistic Regression, Decision Tree, and Random Forest models for multiclass classification of machine learning to the data collected from Indore- MPPCB and Anand Vihar, Delhi –DPCC (2020-2022) that is available on Central Pollution Control Board, Ministry of Environment, Forest and Climate Change for AQI prediction. We compared the performance of these algorithms of machine learning using various performance metrics that is Accuracy, Precision, Recall, F1-Score, AUC ROC, Kappa Score, and MCC. However, we found that the Random Forest model is best suited for this work.
Support Vector Machine, K-Nearest neighbor, Matthews Correlation Coefficient
Our study performed the multi class categorization of ML to the data for the prediction of air quality index by employing various methods such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Decision Tree, and Random Forest. We also carried out the comparative analysis for all the methods, in that, Random Forest considered as an efficient method for AQI prediction.
Air pollution is one of the largest dangers to the environment and public health today. As a result, the ecology, the climate, and human health all suffer. Numerous techniques for monitoring air pollution have been tried and refined over time. The monitoring of air pollution, a prediction method, and actions taken to decrease its effects are all examined in this research. Additionally, the forecasting model has been used to forecast the concentration of polluting gases in the future. Using a machine learning model called Autoregressive Integrated Moving Average (ARIMA), we analyze the air pollution data obtained from the multisensory device in this research and use it to forecast air pollution in the future. A multimodal air quality device with five metal oxide chemical sensors inside has generated 9358 instances of hourly averaged data for the collection. It was located in a particularly dirty part of the city, on a field at street level. The dataset being considered has a public version in the UCI machine learning repository. We first pre-processed the dataset, then examined the air pollution caused by various chemicals, and lastly, we developed a model for calculating the concentration of various gases by using various machine learning methods. Finally, we created an ARIMA model to forecast future gas concentrations. Traditional time series models and machine learning techniques are used to anticipate air pollution in the future. We also calculated the accuracy of the ARIMA model and discovered 0.834 RMSE, 0.109 MSE, and 0.646 MAE.
Air Pollution Monitoring, Autoregressive Integrated Moving Average (ARIMA)
In our study, air pollution monitoring, forecasting techniques and the approaches to overcome its impact are all discussed. We gathered the air pollution data from multi- sensory devices that are preprocessed and examined to predict gas concentration in future by utilizing ML approach named Autoregressive Integrated Moving Average (ARIMA). We employed ML and conventional time series techniques to forecast the contaminated air in future.
Air pollution has become one of the environmental concerns in recent years due to its harmful threats to human health. To inform people about the air quality in their living areas, it is essential to measure the extent of pollution in the atmosphere. Air pollution sensors are assembled at static, fixed-site measurement monitoring stations to acquire data. The data can be processed at the fixed stations or transmitted to the server to predict the Air Quality Index (AQI). Some previous studies applied machine learning algorithms to predict the AQI. Even though those works showed good performance on specific data, the results are not consistent on different datasets. Moreover, to serve the need for low-cost AQI tracking and prediction, lightweight machine learning algorithms can be directly integrated into microcontroller hardware systems. This study proposed a new method that combines (i) air pollution data processing techniques and (ii) lightweight machine learning algorithms to enhance the AQI predicting performance. Three algorithms, namely Decision Tree, Random Forest, and XGBoost, were compared via three evaluation metrics: MAE, RMSE, and R2 to propose the best model in AQI prediction. Two different public datasets, which were both collected in different regions in India were used to verify our proposed method. XGBoost outperformed in predicting the AQI values. Thus, XGBoost is selected for the low-cost AQI prediction device assembled at fixed-site measurement stations.
Lightweight machine learning, Data analysis, XGBoost
An innovative technique is suggested in our paper that integrates air contaminated data processing approach and to improve the forecasting efficiency of AQI through the utilization of lightweight ML techniques. We compared various techniques such as Decision Tree, Random Forest, and XGBoost in terms of various performance metrics to find out the optimal model for the prediction process. Results show that, XGBoost achieved better outcomes.
The purpose of this study was to compare different machine learning models for predicting daily air quality index (AQI) and evaluating air quality grade (AQG). The study used publicly available data from 2014 to 2019 for six pollutants (PM10, PM2.5, NO2, SO2, CO, O3). Four models (random forest (RF), gradient boosting (GB), Lasso Regression (LASSO), and the Stacked Regressor) were used for predicting AQI, while six models (K-Nearest Neighbors (KNN), support vector machines (SVM), decision tree (DT), multilayer perceptron (MLP), random forest (RF), and the Stacked Classifier) were used for forecasting AQG. The individual models were evaluated using different statistical measures, such as R-squared (R2), root mean square error (RMSE), mean absolute error (MAE), accuracy score (ACC), Matthew’s Correlation Coefficient (MCC), and F1 score. The study found that the stack model performed consistently across all metric scores for AQI prediction. The stack model had an R2 score of 0.973, RMSE of 7.568, and MAE of 4.596, outperforming LASSO, GB, and RF. This indicates that the stack model was able to minimize the weaknesses of the individual models and provide a more accurate prediction. For AQG, the stack model also performed better across all metric scores, with an ACC of 0.970, MCC of 0.960, and F1 of 0.970, outperforming MLP, KNN, SVM, DT, and RF. The study concluded that stacked generalization machine learning models can be used for forecasting air quality index and grade with high efficiency and precision, mitigating the concerns of overfitting against individual models.
Air quality prediction, Stack models, Forecasting, Air quality classification
A comparison of various ML methods for the forecasting of Air Quality Index (AQI) and estimating of Air Quality Grade (AQG) is the major aim of our research. We utilized methods including RF, GB, LASSO, and the Stacked Regressor for AQI forecasting. For estimating AQG, we used methods like KNN, SVM, DT, MLP, RF, and the Stacked Classifier. As a consequence, we conclude that, the stacked model offers greater efficiency in both AQI and AQG forecasting.
In recent years, air quality has become a significant environment and health issue, this impact people’s everyday life. How to predict air quality accurately and precisely in urban cities has become one of the hot research issues. The majority of research papers talked about one, two, or three methods, and there rises a question as to select which method is superior to others. To resolve this issue, there is a need for a comprehensive study of various works done. This paper provides a comparative analysis of the most relevant studies related to air quality prediction. After studying comprehensively various experiments have been conducted using machine learning methods like linear regression (Linear R), lasso regression (Lasso R), ridge regression (RR), decision tree regression (DTR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). These experiments successfully resolve limitations like data instability, overfitting, and multicollinearity. RFR, XGBoost, and ANN perform better and help to resolve air prediction issues, and specifically, ANN outperforms all. Results and discussion of this paper provide a holistic view of methods to researchers. Compared with other various models, the precision of prediction data has been greatly improved.
Random forest, ANN
Various research studies based on air quality forecasting are compared in our article. We carried out several investigations by employing various techniques such as Linear R, Lasso R, RR, DTR, RFR, XGBoost, and ANN after thoroughly examining the previous studies. Various issues related to data instability, overfitting, and multicollinearity can overcome by this investigation. As a result, ANN provides highest outcomes than others in predicting air quality.
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