AI-Driven Methane Emission Prediction in Rice Paddies: A Machine Learning and Explainability Framework

Rice cultivation accounts for roughly 10% of worldwide anthropogenic greenhouse gas emissions, making it a significant source of methane (CH4) Despite modest observational constraints, estimates of worldwide CH4 emissions from rice agriculture range from 18–115 Tg CH4 yr−1. CH4 is a potent greenhouse gas, and its oxidation produces tropospheric ozone (O3), which is harmful to public health and crop production when combined with nitrogen oxides (NOx) and sunlight. Elevated O3 levels reduce air quality, crop productivity, and human respiratory health. This study presents an AI-driven framework that combines ensemble learning, hyperparameter optimisation (HPs), and SHAP-based explainability to enhance CH4 emission predictions from rice paddies in India, Bangladesh, and Vietnam. The model consists of two stages: (1) a classification stage to distinguish between zero and non-zero CH4 emissions, and (2) a regression stage to estimate emission magnitudes for non-zero situations. The framework also incorporates O3 and asthma incidence data to assess the downstream impacts of CH4-driven ozone formation on air quality and health outcomes. Understanding the factors that drive optimal model performance and the relative importance of features affecting model outputs is still an ongoing field of research. To address these issues, we present an integrated approach that utilises recent improvements in model optimisation and employs SHapley Additive ExPlanations (SHAP) to find the most relevant variables affecting methane (CH4) emission forecasts. In addition, we developed a web-based artificial intelligence platform to help policymakers and stakeholders with climate strategy and sustainable agriculture by visualising methane fluxes from 2018 to 2020, ensuring practical applicability. Our findings show that ensemble learning considerably improves the accuracy of CH4 emission prediction, minimises uncertainty, and shows the wider benefits of methane reduction for climate stability, air quality, and public health.

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Publication Details

Journal
Methane
Published
2025-11-12
DOI
https://doi.org/10.3390/methane4040028
Primary Topic
Atmospheric and Environmental Gas Dynamics
Type
article
Field-Weighted Citation Impact
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article

AI-Driven Methane Emission Prediction in Rice Paddies: A Machine Learning and Explainability Framework

Fathima Nuzla Ismail, Abira Sengupta, Shanika Amarasoma
Methane
Atmospheric and Environmental Gas Dynamics
article

AI-Driven Methane Emission Prediction in Rice Paddies: A Machine Learning and Explainability Framework

Fathima Nuzla Ismail, Abira Sengupta, Shanika Amarasoma
article en

Abstract

Rice cultivation accounts for roughly 10% of worldwide anthropogenic greenhouse gas emissions, making it a significant source of methane (CH4) Despite modest observational constraints, estimates of worldwide CH4 emissions from rice agriculture range from 18–115 Tg CH4 yr−1. CH4 is a potent greenhouse gas, and its oxidation produces tropospheric ozone (O3), which is harmful to public health and crop production when combined with nitrogen oxides (NOx) and sunlight. Elevated O3 levels reduce air quality, crop productivity, and human respiratory health. This study presents an AI-driven framework that combines ensemble learning, hyperparameter optimisation (HPs), and SHAP-based explainability to enhance CH4 emission predictions from rice paddies in India, Bangladesh, and Vietnam. The model consists of two stages: (1) a classification stage to distinguish between zero and non-zero CH4 emissions, and (2) a regression stage to estimate emission magnitudes for non-zero situations. The framework also incorporates O3 and asthma incidence data to assess the downstream impacts of CH4-driven ozone formation on air quality and health outcomes. Understanding the factors that drive optimal model performance and the relative importance of features affecting model outputs is still an ongoing field of research. To address these issues, we present an integrated approach that utilises recent improvements in model optimisation and employs SHapley Additive ExPlanations (SHAP) to find the most relevant variables affecting methane (CH4) emission forecasts. In addition, we developed a web-based artificial intelligence platform to help policymakers and stakeholders with climate strategy and sustainable agriculture by visualising methane fluxes from 2018 to 2020, ensuring practical applicability. Our findings show that ensemble learning considerably improves the accuracy of CH4 emission prediction, minimises uncertainty, and shows the wider benefits of methane reduction for climate stability, air quality, and public health.

MethaneVol. 4(4)
University of the Visual & Performing Arts (LK), University at Buffalo, State University of New York (US), University of Otago (NZ)
Openalex Percentile: Top 68%
Atmospheric and Environmental Gas Dynamics
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