Machine Learning for Predicting NO2 Emissions from Coal-Fired Power Plants
Accurately estimating human emissions and predicting climate impacts requires effective and precise monitoring techniques. In this captivating chapter, we delve into the world of machine learning to develop a powerful model capable of predicting the NO2 output of coal-fired power plants. To train this model, we leverage a diverse range of data sources, including satellite observations from Sentinel 5, ground observed data from EPA eGRID, and meteorological observations from MERRA.
Developing a consistently accurate model solely based on remote sensing data presents challenges such as overfitting and generalization. However, fear not, as this chapter unveils a treasure trove of strategies to tackle these obstacles head-on. Through a skillful combination of preprocessing techniques, hyperparameter tuning, and feature engineering, we navigate the complexities of the data and create a robust machine learning model that yields precise predictions.
Join us on this exciting tutorial as we unlock the secrets of machine learning in predicting NO2 emissions from coal-fired power plants. Along the way, we demystify the intricacies of satellite observations, ground data analysis, and meteorological insights. By understanding and harnessing the power of machine learning, we pave the way for improved monitoring capabilities, more accurate emission estimates, and a deeper understanding of the climate impacts associated with coal-fired power generation.