Neural networks based-simple estimated model for greenhouse gas emission from irrigated paddy fields

Abstract

The current study aims to develop a simple model for estimating greenhouse gas emissions originating from paddy fields, utilizing backpropagation neural networks. The model integrated three input parameters: soil moisture, soil temperature, and soil electrical conductivity (EC), while generating estimations for two output parameters: methane (CH₄) and nitrous oxide (N₂O) emissions. The model was put into practice across three different irrigation systems, i.e., continuous flooded (FL), wet (WT), and dry (DR) regimes. For model training and validation, the input parameters were measured by a single 5-TE sensor. Concurrently, CH₄ and N₂O emissions were determined utilizing a closed chamber, and gas samples were subjected to laboratory analysis. Findings unveiled that the developed model accurately estimated CH₄ and N₂O emissions, demonstrating commendable coefficient of determination (R²) values ranging from 0.60 to 0.97 for validation process. Notably, the WT irrigation system exhibited the highest precision, boasting R² values of 0.97 for CH₄ and 0.73 for N₂O estimation, respectively. Conversely, the FL irrigation system has the lowest accuracy with R² values of 0.66 and 0.60. Despite variances in accuracy across irrigation systems, the overall performance remained deemed acceptable, warranting the model's applicability for estimating greenhouse gas emissions under diverse irrigation scenarios.