Climate Zones Classification for Agriculture Applications using Machine Learning
Modern climate classification techniques typically employ intra-annual variability of climate data--e.g. temperature and precipitation--in order to segment geographical regions into meaningful zones with similar physical characteristics. For instance, common classification schemes like the Koppen-Geiger rely on human-defined clusters. Here we show a generalizable method for creating meaningful climatically-relevant zones tailored to each user’s needs. To accomplish this, we first explored several clustering methods by comparing performances and weighing potential advantages and/or disadvantages to each technique. We found out that our streamlined clustering procedure can benefit the real state and agricultural sectors especially, by taking into consideration climate change conditions.
Dr. Gaitan is the Chief Scientist and Head of AI at ClimateAI. He did his doctoral studies at the University of British Columbia (Vancouver, Canada) working with William Hsieh in machine learning applications in the environmental sciences. He also holds a Bachelor degree in Civil Engineering and a Master degree in Hydrosystems from the Pontificia Universidad Javeriana (Bogota, Colombia). He is a member of the American Meteorological Society’s (AMS) Artificial Intelligence Committee. He previously worked as a VP of Weather Forecasting at Arable Labs and as Research Scientist for the South Central Climate Science Center at the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey.