Urban Air Quality Prediction Using Regression Analysis

Mahanta, Soubhik; Ramakrishnudu, Tene; Jha, Rajat Raj; Tailor, Niraj

doi:10.1109/tencon.2019.8929517

Cited by 23 publications

(8 citation statements)

References 9 publications

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“…The accuracy of the two algorithms differs significantly, as evidenced by group characteristics and the independent t-test. In [7] [8], the probabilistic model is utilized to uncover the qualities of urban communities [9] regarding various boundaries. At [10] [11] [12], accuracy was obtained in this experiment at 85.6%.…”

Section: Discussionmentioning

confidence: 99%

Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM

Reddy

Parvathy

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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To predict the air pollution level in a particular region area using a K-Nearest Neighbor algorithm compared with the Support Vector Machine algorithm, The Novel K-Nearest Neighbor Algorithm and the Support Vector Machine Algorithm are two groupings. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N = 5 iterations for each method. G power is set at 80%. The confidence interval is 95%, and the threshold value is 0.05%. The G-power test is around 80% accurate. The K-Nearest Neighbor algorithm (97.44%) has better accuracy when compared with the Support Vector Machine (70.32%). The K-Nearest Neighbor algorithm has the highest accuracy compared to the Support Vector Machine algorithm. In the prediction of air pollution, K-Nearest Neighbor has better performance when compared to the Support Vector Machine Algorithm.

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Section: Discussionmentioning

confidence: 99%

Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM

Reddy

Parvathy

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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“…In [9][10], the probabilistic model is utilized to uncover the qualities of urban communities [11] regarding various boundaries. In [12][13], accuracy was obtained in this experiment at 85.6%.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Air Pollution Level in Particular Region Area Using Logistic Regression and Naive Bayes

Reddy

Parvathy

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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To predict the air pollution level in a particular region area using an Innovative Logistic Regression algorithm compared with the Naive Bayes algorithm. The Logistic Regression Algorithm and the Naive Bayes Algorithm are two sets of algorithms. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N=5 iterations for each method. The threshold value is 0.05%, and the Confidence Interval is 95%. The G-power test used is about 80%. The innovative Logistic Regression algorithm (98.26%) has better accuracy when compared with Naive Bayes(97.32%). Logistic Regression has the highest accuracy in comparison to the Naive Bayes algorithm. Significance value for accuracy is 0.056(p>0.05), Precision 0.02(p<0.05) and recall 0.01(p<0.05) based on 2-tail analysis. Logistic Regression outperforms the Naive Bayes Algorithm in the prediction of air pollution.

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“…In our experiments, we used default hyperparameters of random forest in Sklearn: number of predictors = 100, max_depth = none (nodes expanded until all leaves are pure or contain two samples). This method has shown outstanding performance predicting AQI values [ 35 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors

Villanueva

Espezua

Castelar³

et al. 2023

Sensors

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A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.

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Urban Air Quality Prediction Using Regression Analysis

Cited by 23 publications

References 9 publications

Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM

Predicting Air Pollution Level in Particular Area Using KNN by Comparing Accuracy with SVM

Prediction of Air Pollution Level in Particular Region Area Using Logistic Regression and Naive Bayes

Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors

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