A Scalable Nitrous Oxide (N2O) Emissions Predictor Utilizing Fuzzy Logic and Impulse Response Measurements

Nitrous oxide (N2O) is a powerful greenhouse gas with a global warming potential 298 times that of CO2, significantly contributing to climate change through agricultural activities, particularly the application of fertilizers. Accurate and scalable prediction of N2O emissions is essential for sustainable agriculture and climate mitigation. Current methods, such as LSTM-DLM neural networks proposed in previous MITACS projects, are computationally intensive, require extensive training data, and depend on costly equipment throughout the entire season (e.g., LICOR gas analyzers), which limits their practical application. This proposal outlines a scalable, cost-effective approach that combines fuzzy logic and impulse response measurements to rapidly and accurately predict N2O emissions across diverse agricultural landscapes.

Faculty Supervisor:

Tet Yeap

Student:

Partner:

Invest Ottawa

Discipline:

Engineering

Sector:

Information and cultural industries; Management of companies and enterprises; Professional, scientific and technical services; Transportation and warehousing

University:

University of Ottawa

Program:

Accelerate