Student Research Reports
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Predicting Future Mosquito Habitts Using Time Series Climate Forecasting and Deep Learning
Country:United States of America
Student(s):Christopher Sun, Jay Nimbalkar, Ravnoor Bedi
Grade Level:Secondary School (grades 9-12, ages 14-18)
GLOBE Educator(s):Cassie Soeffing
Contributors:Dr. Rusty Low, IGES, scientist
Peder Nelson, OSU, sme
Dr. Erika Podest, NASA JPL, scientist
Andrew Clark, IGES, EO Researcher and Data Analyst
Report Type(s):International Virtual Science Symposium Report, Mission Mosquito Report
Protocols:Mosquitoes
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Presentation Poster:
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Language(s):English
Date Submitted:01/05/2023
Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing the preferred ecological conditions of mosquito larvae. After assembling a data set with atmospheric records and citizen-science larvae observations, a deep neural network is trained to predict larvae counts from ecological inputs. Time series forecasting is conducted on these variables and climate projections are passed into the initial deep learning model to generate location-specific mosquito larvae abundance predictions. The results support the notion of regional ecosystem-driven changes in mosquito spread, with high-elevation regions increasing in susceptibility to mosquito infestation.