Air Quality Prediction Based on Wavelet Analysis and Machine Learning

Abstract

This thesis takes the historical weather time series of Chongqing as experimental samples. Firstly, this thesis uses wavelet transform to organize the data, and then divides the sample data into training and test sets to verify the accuracy of the evaluation of the Naive Bayes Model. Secondly, the Naive Bayes Model is compared with currently used machine learning models such as SVM, XGBoost, bagging, and random forest. Finally, the results show that the Naive Bayes Model has high stability and accuracy for the air quality assessment of Chongqing, and it can be applied to the evaluation of urban ambient air quality.

Testing for the Multifractality in Air Pollutants Time Series Dynamics and Application to Tabuk Region Case Study

“…The data to be applied will be composed of the principal components of the AQI as recalled previously in ( 13) and (14). Tabuk region contains in fact many stations designated to measure the AQI, such as the stations in Tabuk city itself, Haql city, Umluj city, Alwajh city, and Tayma city.…”

“…In the following step, we estimate the coefficients of the model (14) as well as its wavelet version (15) for the approximation levels j = 1, 2, . .…”