Book ChapterDOI
Predicting Ozone Layer Concentration Using Multivariate Adaptive Regression Splines, Random Forest and Classification and Regression Tree
Sanjiban Sekhar Roy,Chitransh Pratyush,Cornel Barna +2 more
- pp 140-152
TLDR
Evaluation of the prediction models indicates that the Multivariate Adaptive Regression Splines model describes the dataset better and has achieved significantly better prediction accuracy as compared to the Random Forest and Classification and Regression Tree.Abstract:
Air pollution is one of the major environmental worries in recent time. Abrupt increase in the concentration of any gas leads to air pollution. The cities are mostly affected due to the abundance of population there. One of the worst gaseous pollutants is OZONE (O3). In this paper, we propose three predictive models for estimation of concentration of ozone gases in the air which are Random Forest, Multivariate Adaptive Regression Splines and Classification and Regression Tree. Evaluation of the prediction models indicates that the Multivariate Adaptive Regression Splines model describes the dataset better and has achieved significantly better prediction accuracy as compared to the Random Forest and Classification and Regression Tree. A detailed comparative study has been carried out on the performances of Random Forest, Multivariate Adaptive Regression Splines and Classification and Regression Tree. MARS gives the result by considering less variables as compared to other two. Moreover, Random Forest takes a little more time for building the tree as the elapsed time was calculated to 45 s in this case. In addition, variable importance for each model has been predicted. Observing all the graphs Multivariate Adaptive Regression Splines gives the closest curve of both train and test set when compared. It can be concluded that multivariate adaptive regression splines can be a valuable tool in predicting ozone for future.read more
Citations
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Journal ArticleDOI
A Predictive Data Feature Exploration-Based Air Quality Prediction Approach
Ying Zhang,Wang Yanhao,Minghe Gao,Qunfei Ma,Jing Zhao,Rongrong Zhang,Qingqing Wang,Huang Linyan +7 more
TL;DR: An improved air quality prediction method based on the LightGBM model to predict the PM2.5 concentration at the 35 air quality monitoring stations in Beijing over the next 24 h is proposed and the experimental results show that the proposed method is superior to other schemes and proves the advantage of integrating the forecasting data and building up the high-dimensional statistical analysis.
Journal ArticleDOI
Regression trees modeling of time series for air pollution analysis and forecasting
Snezhana Georgieva Gocheva-Ilieva,Desislava Stoyanova Voynikova,M. Stoimenova,A. Ivanov,Iliycho Petkov Iliev +4 more
TL;DR: The results show that CART models fit well the data and correctly predict about 90% of measured values of PM10 with respect to the average daily European threshold value of 50 µg/m3.
Proceedings ArticleDOI
Random forests models of particulate matter PM10: A case study
A. Ivanov,Desislava Stoyanova Voynikova,M. Stoimenova,Snezhana Georgieva Gocheva-Ilieva,Iliycho Petkov Iliev +4 more
TL;DR: In this paper, the authors explored the possibilities of the random forest method for modeling the concentrations of fine particulate matter (PM) in Blagoevgrad, Bulgaria, using average daily data over 9 years and a large number of input variables as predictors.
Journal ArticleDOI
Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data
TL;DR: The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.
Journal ArticleDOI
Improvement of downscaled ozone concentrations from the transnational scale to the kilometric scale: Need, interest and new insights.
Honorine Gauthier-Manuel,Frédéric Mauny,Mathieu Boilleaut,Marie Ristori,Sophie Pujol,F. Vasbien,Anne-Laure Parmentier,Nadine Bernard +7 more
TL;DR: In this paper , a transnational modeling platform (PREV'EST) was used at 6 geographic points by comparison with data from 6 air quality monitoring stations, and several corrections were developed using MARS and integrating different sets of variables (mean temperature, relative humidity, rainfall amount, wind speed, elevation and date).
References
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Journal ArticleDOI
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Journal ArticleDOI
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