Spatio-temporal prediction and factor identification of urban air quality using support vector machine

Liu, Chih-Chun; Lin, Tzu-Chi; Yuan, Kuang-Yu; Chiueh, Pei-Te

doi:10.1016/j.uclim.2021.101055

Cited by 40 publications

(9 citation statements)

References 35 publications

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“…Liu et al 16 presented a reliable AQI prediction model which includes initially decomposes the AQI using variational mode decomposition improved by sample entropy. Further using LSTM network is used to produce high quality time series data.…”

Section: Related Workmentioning

confidence: 99%

Optimized machine learning model for air quality index prediction in major cities in India

Natarajan,

Shanmurthy,

Arockiam

et al. 2024

Sci Rep

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Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.

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Section: Related Workmentioning

confidence: 99%

Optimized machine learning model for air quality index prediction in major cities in India

Natarajan,

Shanmurthy,

Arockiam

et al. 2024

Sci Rep

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“…However, the results were relatively modest, leading to inaccuracies in APCs predictions [18], [19]. In order to overcome these constraints, Artificial Intelligence (AI) techniques emerge in accurate APCs prediction and in the realm of pollution forecasting, ML algorithms, including Support Vector Machine (SVM) [20], Decision Tree (DT) [21], and Artificial Neural Network (ANN) [22] gained prominence. Nonetheless, these methods struggled to forecast extreme concentrations accurately and faced limitations due to observational constraints.…”

Section: Related Workmentioning

confidence: 99%

Toward Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management

Chatterjee,

Raju,

Navya Thara

et al. 2024

IEEE Access

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The Smart City (SC) framework has garnered global recognition for its transformative influence on society through innovative solutions. However, the extensive use of Internet of Things (IoT) devices in SCs raises concerns regarding electronic waste and resource consumption. Addressing this challenge necessitates integrating smart grid systems to safeguard SC residents' environment and wellbeing. Accurate air quality prediction is essential for informed societal decisions, safe transportation, and disaster preparedness. This study introduces a novel approach: Towards Cleaner Industries: Smart Cities' Impact on Predictive Air Quality Management (SPAM). The SPAM model utilizes a bidirectional stacking mechanism of long short-term memory neural networks, considering spatiotemporal correlations to forecast future air pollutant concentrations. Surpassing conventional methods, SPAM enhances accuracy while reducing computational complexity. Experimental findings demonstrate enhanced efficiency and accuracy, underscoring its practicality in industrial contexts. The SPAM model represents a significant advancement in promoting environmental sustainability within the SC framework.

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“…In recent years, with the improvement of computer computing power, arti cial neural networks 21−24 , support vector machines 25−27 Taipei with the help of support vector machines. After veri cation, the model has high accuracy in short-term time predictions in this region 32 . Although the statistical model based on the machine learning algorithms cannot give the quantitative relationship between the input variable and the output variable, because it can simulate the nonlinear relationship between the input variable and the output variable and does not need to pre-set complex mathematical expressions, so machine learning algorithms tend to be more accurate than traditional statistical models.…”

Section: Introductionmentioning

confidence: 98%

A method for calibrating measurement data of micro air quality monitor based on MLR-BRT-ARIMA combined model

Liu

Jiang

2022

Preprint

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In recent years, air pollution has become an increasingly important issue in the sustainable development of cities. Monitoring air pollutants is of great significance for government departments to effectively control air pollution. The development of micro air quality monitors provides the possibility for grid monitoring and real-time monitoring of air pollutants. However, affected by many factors, the measurement accuracy of the micro air quality monitors need to be improved. In this paper, a combined prediction model of Multiple Linear Regression, Boosted Regression Tree and AutoRegressive Integrated Moving Average model (MLR-BRT-ARIMA) is proposed to calibrate the measurement data of the micro air quality monitor. First, the multiple linear regression model is used to find the linear relationship between the concentration of various pollutants and the measurement data of the micro air quality monitor and obtain the predicted value of the concentration of various pollutants. Second, take the measurement data of the micro air quality monitor and the prediction value of the multiple regression model as the input, and use the boosted regression tree to find the nonlinear relationship between the concentrations of various pollutants and the input variables. Finally, the autoregressive integrated moving average model is used to extract the information hidden in the residual sequence, and finally the establishment of the MLR-BRT-ARIMA model is completed. This combined model combines the advantages of multiple linear regression and boosted regression trees. It can not only give the quantitative relationship between the explained variables and their influencing factors, but also the prediction accuracy is higher than the multiple linear regression and boosted regression tree models alone. Using the ARIMA model to correct the residuals can further improve the prediction accuracy of the model. Root mean square error, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of MLR-BRT-ARIMA model and other commonly used models such as multilayer perceptron neural network, support vector regression machine and nonlinear autoregressive models with exogenous input. The results show that no matter what kind of pollutant, the MLR-BRT-ARIMA combined model proposed in this paper has the best performance in the three indicators. Using this model to calibrate the measurement value of the micro air quality monitor can improve the accuracy by 82.4%~95.4%.

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Spatio-temporal prediction and factor identification of urban air quality using support vector machine

Cited by 40 publications

References 35 publications

Optimized machine learning model for air quality index prediction in major cities in India

Optimized machine learning model for air quality index prediction in major cities in India

Toward Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management

A method for calibrating measurement data of micro air quality monitor based on MLR-BRT-ARIMA combined model

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