scholarly journals Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

2019 ◽  
Vol 9 (19) ◽  
pp. 4069 ◽  
Author(s):  
Huixiang Liu ◽  
Qing Li ◽  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

scholarly journals Air Quality Index Prediction using Machine Learning Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 7489-7492
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Learning Algorithms ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Pollution Monitoring ◽  
Quality Data ◽  
Support Vector

— The global environment is presently facing a key issue of air pollution. The four air pollutants which are becoming a concerning intimidation to human health are respirble particulate matter, nitrogen oxide, particle matter, and sulfur dioxide. A vast amount of air quality data is collected in different monitoring stations throughout the world. The collected data can be analyzed to forecast the air quality index (AQI) of future. This paper proposes machine learning algorithms such as random forest, support vector machine, self adaptive resource allocation to predict the future AQI. Tamil Nadu Pollution Control Board (TNPCN) deployed air pollution monitoring station in five regions. Air pollutant of PM10, PM2.5, SO2 and NO2 are monitord and AQI is calculated.. The data collected from January 2019 to November 2019 by TNPCN and also AQI of previous five years were used This system attempts to predict the level of pollutant PM,SO2,NO2 in the air to detect the AQI.

scholarly journals Comparative Analysis of Machine Learning Regression Algorithms on Air Pollution Dataset

2020 ◽  
pp. 125-136
Author(s):  
Sumit Upadhyay
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Accuracy Score ◽  
Implementation Phase ◽  
The Government ◽  
Respirable Particulate

Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. Examining and protecting air quality has become one of the most essential activities for the government in many industrial and urban areas today. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO(2)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O(3)), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. The main objective of this paper to build a model for predicting Air Quality Index(AQI) of the specific cities using various types of machine learning algorithms namely Multiple Linear Regression, K Nearest Neighbours(KNN), Support Vector Machine(SVM) and Decision Tree. And also evaluate and compare the performance of every algorithm based on their accuracy score and errors. Air Pollution dataset is publicly available on different government sites. The implementation phase dataset is divided as 80% for the training of different models and the rest of the dataset is used for testing the model.

scholarly journals Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa

2021 ◽  
Vol 4 (3) ◽  
pp. 44
Author(s):  
Calorine Katushabe ◽  
Santhi Kumaran ◽  
Emmanuel Masabo
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Pollutant Concentrations ◽  
Kampala City ◽  
Air Pollutant Concentrations

The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not exist. Here in Africa, dirty air is brought about by the growth in industrialization, urbanization, flights, and road traffic. Air pollution remains such a silent killer, especially in Africa, and if not dealt with, it will continue to lead to health issues, such as heart conditions, stroke, and chronic respiratory organ unwellness, which later result in death. In this paper, the Kampala Air Quality Index prediction model based on the fuzzy logic inference system was designed to determine the air quality for Kampala city, according to the air pollutant concentrations (nitrogen dioxide, sulphur dioxide and fine particulate matter 2.5). It is observed that fuzzy logic algorithms are capable of determining the air quality index and therefore, can be used to predict and estimate the air quality index in real time, based on the given air pollutant concentrations. Hence, this can reduce the effects of air pollution on both humans and the environment.

scholarly journals Air Quality Estimation Using Dendritic Neural Regression with Scale-Free Network-Based Differential Evolution

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1647
Author(s):  
Zhenyu Song ◽  
Cheng Tang ◽  
Jin Qian ◽  
Bin Zhang ◽  
Yuki Todo
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Differential Evolution ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Scale Free Network ◽  
Quality Estimation ◽  
Scale Free ◽  
Free Network

With the rapid development of the global economy, air pollution, which restricts sustainable development and threatens human health, has become an important focus of environmental governance worldwide. The modeling and reliable prediction of air quality remain substantial challenges because uncertainties residing in emissions data are unknown and the dynamic processes are not well understood. A number of machine learning approaches have been used to predict air quality to help alleviate air pollution, since accurate air quality estimation may result in significant social-economic development. From this perspective, a novel air quality estimation approach is proposed, which consists of two components: newly-designed dendritic neural regression (DNR) and customized scale-free network-based differential evolution (SFDE). The DNR can adaptively utilize spatio-temporal information to capture the nonlinear correlation between observations and air pollutant concentrations. Since the landscape of the weight space in DNR is vast and multimodal, SFDE is used as the optimization algorithm due to its powerful search ability. Extensive experimental results demonstrate that the proposed approach can provide stable and reliable performances in the estimation of both PM2.5 and PM10 concentrations, being significantly better than several commonly-used machine learning algorithms, such as support vector regression and long short-term memory.

scholarly journals A Literature Review on Prediction of Air Quality Index and Forecasting Ambient Air Pollutants using Machine Learning Algorithms

2020 ◽  
Vol 5 (8) ◽  
pp. 1148-1152
Author(s):  
Radhika M. Patil ◽  
Dr. H. T. Dinde ◽  
Sonali. K. Powar
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Literature Review ◽  
Air Pollutants ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Ambient Air Pollutants

Day by day the air pollution becomes serious concern in India as well as in overall world. Proper or accurate prediction or forecast of Air Quality or the concentration level of other Ambient air pollutants such as Sulfur Dioxide, Nitrogen Dioxide, Carbon Monoxide, Particulate Matter having diameter less than 10µ, Particulate Matter having diameter less than 2.5µ, Ozone, etc. is very important because impact of these factors on human health becomes severe. This literature review focuses on the various techniques used for prediction or modelling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.

Changes in Air Quality of the Yuxi City Urban Area

2014 ◽  
Vol 1021 ◽  
pp. 225-228
Author(s):  
Cheng Qiu ◽  
Hong Chen ◽  
Chun Li Ye ◽  
Yan Jun Yang ◽  
Chang Bing Ye
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Area ◽  
Health Problem ◽  
Quality Index ◽  
Quality Standards ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Air Quality Standards

Air pollution causes health problem. The paper simply analyzed the changes of air quality in the Yuxi city urban area from 2006 to 2012. In the Yuxi city urban area between 2006 and 2012, SO2 levels increased about 43.9 percent; NO2 levels increased about 13.3 percent; PM10 levels in 2012 decreased about 1.5 percent. By evaluating the air quality in the Yuxi city urban area, the results showed that air quality index was the maximum in 2009, and the quality of the air in Yuxi became worse from 2006 to 2012, air pollution in 2009 was the heaviest between 2006 to 2012. After adopting P.R.C EPA air quality standards (GB3095-2012) in 2013, the first air pollutant in Yuxi is PM10, and then it is SO2 among SO2, NO2 and PM10.Much should beend done to reduce the amount of PM10 and SO2 released.

Forecasting Air Quality Index Using an Ensemble of Artificial Neural Networks and Regression Models

2017 ◽  
Vol 28 (5) ◽  
pp. 893-903 ◽  
Author(s):  
S. Sankar Ganesh ◽  
Pachaiyappan Arulmozhivarman ◽  
Rao Tatavarti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Regression Models ◽  
Quality Index ◽  
Gradient Descent ◽  
Air Quality Index ◽  
Support Vector ◽  
Artificial Neural

Abstract Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multiple linear regression (MLR) consisting of batch gradient descent (BGD), stochastic gradient descent (SGD), mini-BGD (MBGD) and CGD algorithms, and support vector regression (SVR), are implemented. In these models, the AQI is the dependent variable and the concentrations of NO2, CO, O3, PM2.5, SO2, and PM10 for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

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