Research Of Geological Environment Bearing Capicity Prediction System Based On The Composite Index And The BP Neural Network Model

The Central Committee of the Communist Party of China's Central Committee on the formulation of national economic and social development thirteenth five year plan proposed the green development and coordination concept in 2015,and focused on the detailed deployment section and explanation.Meanwhile the fifth part of the plan stresses the green development once again,promotes the harmonious coexistence concept between man and nature,and on this basis to promote construction and efficient use of resources.To achieve the completion of these major initiatives,researches on the geological environment carrying capacity is a very important part,those researches and human society,economic development have been bound together,effective prediction and researches on the geological environment carrying capacity are the valuable premise for the development of the human beings,the harmony of living in with nature,the better protection and use of the natural resources.Firstly,this article determined the research area----Tianjin Binhai New Area,this article built the index system for the geological environment of Binhai New Area capacity evaluation according to the relevant provisions and requirements of the geological.Based on the access of the relevant data,the fusion based entropy weights and comprehensive index method was used to deal with the evaluation of the experimental data,through the reading and thinking on the related geological information,determined the classification evaluation of each sample data;then used the prediction ability of BP neural network model forecast the bearing capacity of geological environment of each sample,the sample has been divided into two parts,one part through learning and training on BP neural network model can be obtained the minimum error prediction approach,the other part of the sample has been used to test the trained BP learning model has good prediction efficacy.Secondly,this article has compared the same sample data and used gray prediction method model to do the prediction,introduced the principles and the steps of gray forecasting model,and carried out the data processing and forecasting.When it does not require the complex time degree,the proposed algorithm on the basis of this article can make the error relatively small,that proved the algorithm in this article is effective.Finally,it has designed a simple prediction function using system based on this algorithm,and introduces the system's structure,its functions and interfaces.