Studies On Theory And Practice Of Forest Resources Dynamic Prediction Based On GIS In ANN

GIS originated in 1960s, with the development and application of more than 30 years the traditional GIS which functions are gather, storage, management, query and visual output of data can't meet with the demands of social and regional sustainable development. Integration between GIS with expert system and ANN becomes one of tendency.Thesis primarily studies prediction model of the forest resources inventory management information system in ANN based on finished information system of the forest resources inventory management of NingXia Hui-autonomous region. By primary data process, visual output of GIS and ANN model, thesis has expanded the spatial assistant decision function.ANN has already applied in many aspects since it originated in 1940s and applied in design of existing model and bettering its model and arithmetic today. ANN is an artificial nonlinear dynamic system based on the recognition of cerebra neural network theory. ANN is a theoretic cerebra neural network mathematic model and an information processing system based on imitating cerebra neural net structure.The method of ANN based on example does not need to deal with internal structure of mathematic method, suppose premise, decide factor weight. ANN model is simple because it maps integration of object.Thesis forecasts forest resources of Liancheng forest fields in Gansu province by improved three-layer BP network. ANN is trained through fast BP algorithm with variable learning rate that mixed with momentum factor. This algorithm can improve network's reliability.Thesis summarizes the basic thought of forest resources forecast, compares many forecasting method based on pioneer achievement. Thesis evaluates actuality of experimental district in GIS spatial analysis method through GIS spatial database and then puts foreword the theory and method of dynamic prediction based on GIS in ANN. Thesis establishes forest volume prediction model of red-birch's five-age group and the wooded area prediction model by rolling prediction and multi-step prediction of ANN which structures are 5-25-5 and 4-10-1. Thesis predicts red-birch's five-age group volume and thewooded area of experimental district from 2000 to 2004. In order to evaluate the precision of the model, the author establishes the GM(1,1) model. The average of relative error is 6.733214 percent through GM(1,1) model of forest volume and the ANN model's is 0.027571 percent. The average of relative error is 1.41434 percent through GM(1,1) model of wooded area and the ANN model's is 0.03863 percent. We can conclude that the model of dynamic prediction Based on GIS in ANN is better than the model of GM(1,1) by the comparison ANN model to GM(1,1) model.In this study, the model emphasizes particularly on time series of geological entity and at the same time it realizes the integration of the spatial model and the attributive model by integrating complicated spatial and attributive character of forest resources.Program is realized by MATLAB. The ANN toolbox of MATLAB established many tool functions based on ANN theory. Prediction is easily realized and program codes are easily written and integration with visual basic is easy by ANN toolbox.At last, thesis puts forward measures of forestry development according to prediction and actual experimental district condition, analyzes model's character and points out model's limitation for improving in further study.