Predicting dissolved N2O concentrations from drainage ditches in salt-affected farmlands
This study selected typical saline-alkali agricultural drainage ditches from the Qingtongxia Irrigation District in Ningxia as the subject of research.Based on key water quality parameters of the water body overlying the drainage ditches,including dissolved organic carbon(DOC),water temperature(WT),nitrate nitrogen(NO3--N),and electrical conductivity(EC),a backpropagation(BP)neural network predictive model for the dissolved concentration of Nitrous oxide(N2O)with optimized parameters was constructed.The model was further optimized using Genetic Algorithm(GA)and Ant Colony Optimization(ACO)to enhance the prediction accuracy and stability.The results indicate that an increase in EC significantly promotes the dissolved concentration of N2O in the water body overlying the drainage ditches.There is an extremely significant positive correlation between NO3--N and EC with the dissolved concentration of N2O,while WT and DOC show a significant negative correlation with the dissolved concentration of N2O.The effectiveness and reliability of the constructed model were verified using actual measured data of the dissolved concentration of N2O in the water body overlying the drainage ditches,with the correlation coefficient of the predicted values and actual measured values of the ACO-BP model all exceeding 0.70.Under the best conditions,the coefficient of determination(R2)reached 0.79,and the Mean Relative Error(MRE)was only 7.26％.
dissolved nitrous oxide in water bodiesBPNNoptimization algorithmwater quality monitoring
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