A Multi-Hazard Risk Assessment Model for a Road Network Based on Neural Networks and Fuzzy Comprehensive Evaluation

Changhong Zhou, Mu Chen, Jiangtao Chen, Yu Chen and Wenwu Chen ()
Additional contact information
Changhong Zhou: School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China
Mu Chen: School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China
Jiangtao Chen: School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China
Yu Chen: School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China
Wenwu Chen: School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China

Sustainability, 2024, vol. 16, issue 6, 1-16

Abstract: The frequency of extreme weather events has increased worldwide, leading to more intense natural disasters, which pose significant threats to human life and property safety. The main form of disaster occurrence is multi-hazard coupling and multi-hazard chaining. This paper constructs a road natural disaster risk assessment model using a fuzzy comprehensive evaluation method and neural network to quantitatively analyze road disasters with multiple hazards, and provides valuable insights for the predication of road natural disaster risk. Here, ten factors, including temperature, relative humidity, precipitation, elevation, slope, slope orientation, vegetation cover, geologic lithology, historical impact factors, and road density, were selected as input variables, and risk grade was selected as the output value (the evaluation results). The remaining hidden layers use the fully connected neural network. This model was first trained using historical data (from 2011 to 2021) obtained from road networks and natural disasters in Guangxi, China. Then, taking Lingchuan County as an example, the model was used to predict the risk of natural disasters on its roads, and, finally, the prediction accuracy of the model was determined by comparing the results with actual disaster situations. This study can provide theoretical support and technical operations for the development of subsequent early warning systems.

Keywords: road network; natural disasters; risk assessment; neural network; fuzzy evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/6/2429/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/6/2429/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:6:p:2429-:d:1357272

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().