Design And Implementation Of Forest Carbon Sink Measurement And Prediction System Based On BP Neural Network

Climate warming has become a hot issue in the world today,and forestry carbon sinks have also attracted much attention due to their potential in maintaining global climate stability.Forestry carbon sink refers to the CO₂ in the air stored in plants and soil,through forest absorption to reduce the concentration of CO₂ in the air.The key to the study of forestry carbon sinks is whether the estimation of forest biomass and forest carbon sinks is accurate and fast,and it is also necessary to solve the problem of excessive dependence on human and material resources in the development of forestry carbon sink projects.Therefore,this study uses BP neural network model and related software development technology to design and implement a forestry carbon sink measurement and prediction system based on B/S architecture from the perspective of interdisciplinary,helping China solve the problem of low efficiency and high cost of carbon sink development projects.Firstly,this paper uses BP neural network to estimate forest biomass.The seven parameters of DBH,tree height,altitude,soil type,slope direction,slope and geographical location of Chinese fir were used as input layer parameters.At the same time,the hidden layer of the model had a total of 448 nodes in three layers,and the output layer variables were aboveground biomass and underground biomass.The program is written to train the model on the Python software platform,and the BP neural network model is optimized internally.The R² of the final model in estimating aboveground biomass was 0.9992,and the R² of estimating underground biomass was 0.9829.The prediction accuracy of the model constructed in this paper is more ideal than that of the traditional binary regression model.Secondly,based on the biomass estimation model,the carbon sink measurement model is constructed.When constructing the model,this paper comprehensively considers various influencing factors such as sampling design,project boundary,carbon pool selection and so on.At the same time,in view of the problem that the calculation results of the actual net carbon sink caused by gas emissions such as CH₄ and N₂O in forestry carbon sink projects are not accurate enough,this study derives a specific calculation model based on the actual project development needs.The results showed that the average relative error of the model was 2.45%,and the range of absolute error was-1.626-1.994t/hm².Finally,this paper designs and implements forestry carbon sequestration measurement and forecasting system based on B/S architecture.From the function,the system is divided into seven modules:system management,project registration,project management,carbon sequestration forecast,carbon sequestration measurement,statistical analysis and integrated query.Through the effective development and utilization of forestry carbon sequestration information resources not only solve our country at present in the development of forestry carbon sequestration is too dependent on manpower,material resources and the problem of low efficiency,also can accelerate the rapid and vigorous growth of forestry carbon sequestration industry in China,contribute to the global climate governance Chinese wisdom and effective solutions.