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

<span lang="EN-US">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 (CH<sub>4</sub>) and nitrous oxide (N<sub>2</sub>O) 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, CH<sub>4</sub> and N<sub>2</sub>O emissions were determined utilizing a closed chamber, and gas samples were subjected to laboratory analysis. Findings unveiled that the developed model accurately estimated CH<sub>4 </sub>and N<sub>2</sub>O emissions, demonstrating commendable coefficient of determination (R<sup>2</sup>) values ranging from 0.60 to 0.97 for validation process. Notably, the WT irrigation system exhibited the highest precision, boasting R<sup>2</sup> values of 0.97 for CH<sub>4</sub> and 0.73 for N<sub>2</sub>O estimation, respectively. Conversely, the FL irrigation system has the lowest accuracy with R<sup>2</sup> 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.</span>