Subway Waterlogging Risk Assessment Using Bayesian Networks
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Solution Overview
Problem
Traditional methods for predicting subway waterlogging risks rely heavily on historical data and expert experience, leading to limited accuracy due to data incompleteness and subjective judgment, necessitating improved methods for precise and timely risk management.
Innovation Solution
An auxiliary decision-making method using a Bayesian network that integrates meteorology, geology, and drainage system data, employing preprocessing, risk assessment, and model construction to optimize decision-making for subway waterlogging risk disposal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods relying on historical data and expert experience are used for predicting subway waterlogging risks, then the prediction process is simple and easy to implement, but the prediction accuracy is limited due to data incompleteness and subjective judgment
Solution Approach 1:
The patent introduces a Bayesian network as an intermediary computational model that systematically processes multiple data sources (meteorological data, geological data, drainage system data, historical waterlogging data) and expert knowledge. This intermediary model objectively quantifies the relationships between various risk factors and predicts waterlogging risks, replacing subjective expert judgment while maintaining interpretability and scientific rigor.
Solution Approach 2:
The patent combines multiple types of data (meteorological, geological, drainage system, historical waterlogging) and expert knowledge into a composite Bayesian network model. This composite approach integrates diverse information sources with different characteristics, creating a more robust and accurate prediction system that overcomes the limitations of single-data-source methods.
2Reliability
If comprehensive data integration and Bayesian network modeling are implemented, then prediction accuracy and decision-making support are significantly improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex prediction system into distinct functional modules: data acquisition module (collecting meteorological, geological, drainage, and historical data), data preprocessing module (cleaning and standardizing data), Bayesian network construction module (building the probabilistic model), risk prediction module (calculating waterlogging risks), and decision-making support module (generating disposal recommendations). This segmentation makes the complex system more manageable, maintainable, and implementable.
Solution Approach 2:
The Bayesian network model serves multiple functions simultaneously: it predicts waterlogging risks, identifies key risk factors, quantifies uncertainty, and provides decision-making support. This multi-functionality reduces the need for separate systems for each task, thereby managing overall system complexity while enhancing reliability.
3Loss of time
If real-time monitoring data and dynamic risk assessment are used, then timely disposal decisions can be made, but the data processing workload and computational burden increase
Solution Approach 1:
The patent performs preliminary actions by pre-building the Bayesian network model structure and pre-processing historical data during off-peak periods. This preparation work includes defining the network topology, setting prior probabilities, and establishing conditional probability tables, which significantly reduces the computational burden during real-time risk assessment and enables faster response times.
Solution Approach 2:
The system implements feedback mechanisms where real-time monitoring data continuously updates the Bayesian network model, and the model's risk predictions feed back into the decision-making process. This closed-loop feedback enables dynamic risk assessment and timely disposal decisions while optimizing data processing through iterative refinement rather than exhaustive re-computation.
Data Source
AI summary
Disclosed are an auxiliary decision-making method and system for urban subway waterlogging risk disposal based on a Bayesian network. The method includes: acquiring waterlogging data and basic data of a target city subway, preprocessing the waterlogging data, obtaining first evaluation data and second evaluation data from the preprocessed waterlogging data, performing risk assessment on the first evaluation data and the second evaluation data according to risk degrees to obtain a risk value, adjusting the risk value according to the monitoring data to determine a risk degree, and constructing an auxiliary decision-making model for waterlogging risk disposal according to the risk level to optimize the auxiliary decision-making for waterlogging risk disposal. The method not only can improve the accuracy of auxiliary decision-making for urban subway waterlogging risk disposal, but also has good interpretability, and can be directly applied to the auxiliary decision-making system for urban subway waterlogging risk disposal.