Predicting Missing Value for Ozone Data Based on Optimal Site-Selection Strategy
Author:
Affiliation:
1. Jadavpur University,Department of Computer Science and Engineering,Kolkata,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10059459/10059604/10060138.pdf?arnumber=10060138
Reference15 articles.
1. Forecasts of tropospheric ozone in the Metropolitan Area of Rio de Janeiro based on missing data imputation and multivariate calibration techniques
2. Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan
3. Robust prediction of hourly PM2.5 from meteorological data using LightGBM
4. Lightgbm: A highly efficient gradient boosting decision tree;ke;Advances in neural information processing systems,2017
5. Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone
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