Social Risk Early Warning of Environmental Damage of Large-Scale Construction Projects in China Based on Network Governance and LSTM Model

Junmin Fang,Jingrong Xu,Dechun Huang,Guangdong Wu

doi:10.1155/2020/8863997

Abstract

With the improvement of citizens’ risk perception ability and environmental protection awareness, social conflicts caused by environmental problems in large-scale construction projects are becoming more and more frequent. Traditional social risk prevention management has some defects in obtaining risk data, such as limited coverage, poor availability, and insufficient timeliness, which makes it impossible to realize effective early warning of social risks in the era of big data. This paper focuses on the three environments of diversification of stakeholders, risk media, and big data era. The evolution characteristics of the social risk of environmental damage of large-scale construction projects are analyzed from the four stages of incubation, outbreak, mitigation, and regression in essence. On this basis, a social risk early warning model is constructed， and the multicenter network governance mode of social risk of environmental damage in large-scale construction projects and practical social risk prevention strategies in different stages are put forward. Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. Compared with other classical models, the long short-term memory model has the advantages of strong processing capability and high early warning accuracy for time-sensitive data and will have broad application prospects in the field of risk control research. By using the network governance framework and long short-term memory model, this paper studies the environmental mass events of large-scale construction projects on the risk early warning method, providing reference for the government to effectively prevent and control social risk of environmental damage of large-scale construction project in China.

Highlights

Large-scale construction projects play an important role in the economic, political, cultural, and ecological environment, with the characteristics of multiagent participation, long period of construction and operation, and many factors involved, such as water conservancy, industrial construction, and real estate projects
Risk perception and risk amplification are the main inducements leading to the outbreak of social risk events. e conflicts between stakeholders and social contradictions caused by Complexity them are significantly higher than those of other types of risk events in terms of confrontation degree. erefore, it is urgent to scientifically identify the social risk development stage of large-scale construction project environmental damage and systematically construct the social risk early warning mechanism
Predicted value Actual value has 11 inputs, and the output value is the four stages of social risk evolution, which has 4 outputs. e conversion results of actual output of training data are compared with the expected output values to observe their consistency, which indicates that the model is effective; otherwise, the construction of the neural network is unreasonable and needs further improvement. e experimental results show that the actual output conversion results are basically consistent with the expected output

Summary

Introduction

Large-scale construction projects play an important role in the economic, political, cultural, and ecological environment, with the characteristics of multiagent participation, long period of construction and operation, and many factors involved, such as water conservancy, industrial construction, and real estate projects. On the basis of scientifically grasping the laws and characteristics of media communication of the generation, development, and change of social risks of environmental damage in large-scale construction projects, the use of big data technology can realize the comprehensive collection and monitoring of information of various factors such as stakeholders, regions involved, demands and wishes, target orientation, conflict nature, influencing factors, emotional changes, onlookers, development trends, and correlation degree.

Results

Conclusion