Research On Water Conservancy Investment Forecasting Based On Improved BP Neural Networ

Water conservancy projects have the characteristics of diverse scales,high investment,high risk,and long construction period,which can bring significant economic,social,and ecological benefits to the local area during construction and operation.Since the 13 th Five-Year Plan,the country has attached great importance to the construction of water conservancy projects.The investment estimate of water conservancy projects plays an important role in the smooth implementation of projects and the maximization of economic benefits.Therefore,improving the quality of investment estimation in the early stage of water conservancy projects is urgently needed.Investment estimation in the early stage of water conservancy project can help the project team determine the project scale,scope,and objectives,and then formulate a reasonable construction plan and timetable;evaluate the feasibility of the project,conduct risk assessment,predict its economic and social benefits,and help decision-makers make correct decisions;optimize project design during the design phase,avoid excessive or insufficient design,and improve the efficiency and quality of water conservancy projects;develop scientific and reasonable cost control strategies to ensure the financial sustainability of the project throughout its life cycle.Traditional investment estimation cannot meet modern management needs.With the development of computers,some new technologies and methods are used for investment estimation in the early stage of projects to improve their accuracy and predictive ability.This article collects and summarizes relevant literature at home and abroad,analyzes the advantages and disadvantages of existing cost estimation methods,combines the characteristics and cost features of hydraulic engineering,and relies on existing cost databases of engineering projects to propose a new type of hydraulic engineering investment estimation model.Firstly,the concepts of hydraulic engineering,investment estimation,BP neural network,genetic algorithm(GA),and principal component analysis(PCA)are explained.Based on the above research,a GA-BP neural network-based hydraulic engineering investment estimation model is determined.Secondly,the cost of hydraulic engineering is evaluated,including sorting out relevant literature,valuation methods,and industry norms at home and abroad,and combining actual engineering cases to determine the characteristic parameters that affect the cost.The principal component analysis method is used to screen out the main index system to assist project decision-makers in more accurately evaluating engineering costs.Finally,a new intelligent optimization algorithm is created by implementing GA-BP neural network using MATLAB,and the implementation steps are introduced in detail.A series of simulations are performed using an established hydraulic engineering database.The simulation results show that compared with ordinary BP neural networks,the model proposed in this study has higher estimation accuracy,faster estimation speed,and stronger estimation result stability,meeting the accuracy requirements of hydraulic engineering investment estimation.Based on the PCA-GA-BP neural network model,the prediction accuracy and modeling efficiency of water conservancy investment were improved.According to the validation results in this article,the relative error and convergence speed of traditional BP neural network prediction were 9.09% and 43 steps,respectively,while the improved BP neural network prediction had a relative error and convergence speed of3.22% and 10 steps,respectively.On the one hand,the combination of GA-BP neural network solves the problems of network topology structure and local optimization,representing the latest advances in the field of neural network prediction.On the other hand,the application of this method in the prediction of water conservancy investment fills the gap in traditional methods and provides a new efficient and reliable prediction method for this field.The method can accurately predict the investment situation of the project,has strong practicability,and has wide application value and promotion potential.