Journal
REMOTE SENSING
Volume 14, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/rs14030729
Keywords
emission; coal-fired power plant; remote sensing; machine learning; NO2
Categories
Funding
- NASA ACCESS [80NSSC21M0028]
- NASA Applied Science [17-HAQ17-0044]
- NSF Geoinformatics [EAR-1947893]
Ask authors/readers for more resources
This study tackles the challenges of overfitting and generalization by building machine learning models and using satellite observations, ground observed data, and meteorological observations to predict the emissions of coal-fired power plants. Techniques such as data washing, column shifting, and feature sensitivity filtering are employed. It presents a groundbreaking case study on remotely monitoring global power plants from space.
Effective and precise monitoring is a prerequisite to control human emissions and slow disruptive climate change. To obtain the near-real-time status of power plant emissions, we built machine learning models and trained them on satellite observations (Sentinel 5), ground observed data (EPA eGRID), and meteorological observations (MERRA) to directly predict the NO2 emission rate of coal-fired power plants. A novel approach to preprocessing multiple data sources, coupled with multiple neural network models (RNN, LSTM), provided an automated way of predicting the number of emissions (NO2, SO2, CO, and others) produced by a single power plant. There are many challenges on overfitting and generalization to achieve a consistently accurate model simply depending on remote sensing data. This paper addresses the challenges using a combination of techniques, such as data washing, column shifting, feature sensitivity filtering, etc. It presents a groundbreaking case study on remotely monitoring global power plants from space in a cost-wise and timely manner to assist in tackling the worsening global climate.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available