Pengembangan Model Jaringan Syaraf Tiruan untuk Menduga Fluks Gas N2o dari Lahan Sawah

Artificial neural networks (ANN) model was used to estimate N 2 O gas flux emitted from rice paddies with several water regime treatments. The purpose of this study were to identify the relationship of micro-environment with different water regimes towards N2O gas flux, to predict the amount of N 2 O gas flux, and  to validate neural network models. Field experiment was conducted in the field laboratory of Civil and Environmental Engineering-IPB from February to August 2016. Land of paddy was treated with three water regime of continuous flooding water regime, wet water regime, dry water regime. ANN model with back propagation algorithm consisted of input layer with six nodes of micro- environment parameter and N2O gas was used as the model output. The total N 2 O flux for continuous flooding water regime, wet and dry regime were -25.95 mg/m 2 /season, 17.32 mg/m 2 /season, and 21.16 mg/m 2 /season. ANN each water regime was obtained the coefficient of determination (R 2 ) of was 1, so ANN model was acceptable and could be used to predict N 2 O flux. Key words: artificial neural networks, micro environment, N 2 O gas flux, paddy field