Andres Bonilla
Papers
1
Total Citations
5
H-Index
1
About
Andres Bonilla is a researcher whose work lies at the intersection of atmospheric science and computational modeling, with a particular focus on aerosol characterization. His key research areas include aerosol size distribution analysis, remote sensing techniques, and the application of artificial neural networks to environmental data. Bonilla’s most cited paper, "Aerosol size distribution using sun-photometer and artificial neural network" (2008, 5 citations), introduces an innovative method for deriving aerosol size distribution—a critical parameter in regional atmospheric models—by combining sun-photometer measurements with neural network algorithms. This work, conducted at the LIDAR laboratory of the University of Puerto Rico at Mayagüez (UPRM), demonstrates his contribution to improving atmospheric modeling in the Caribbean region. Though his citation count is modest, Bonilla’s research represents a foundational effort in integrating machine learning with traditional remote sensing tools, offering a cost-effective and efficient approach to aerosol monitoring. His work underscores the potential of interdisciplinary methods to enhance environmental data analysis, making it a valuable reference for students and researchers exploring novel techniques in atmospheric physics and computational environmental science.
Research Focus
Key Achievements
Top Papers
- 1