Heat Wave Monitoring Station Siting Using Risk Map Optimization
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Solution Overview
Problem
The current heat wave monitoring station site selection in China is inefficient and unscientific, with insufficient numbers and uneven spatial distribution, leading to missed and false reports due to the lack of consideration for regional heat stress adaptability and meteorological factors.
Innovation Solution
A method and system for siting heat wave monitoring stations based on risk evaluation, involving data preprocessing, heat wave feature parameter calculation, and iterative computation using optimization algorithms to determine optimal station locations, considering multi-source data and spatial distribution features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual site selection method is used, then flexibility in decision-making is maintained, but selection efficiency is low and scientificity is poor
Solution Approach 1:
The site selection process is divided into multiple independent modules: data acquisition module, heat wave risk evaluation module, spatial analysis module, and station layout optimization module. Each module handles specific tasks independently, improving overall efficiency while maintaining manageable complexity through modular architecture.
Solution Approach 2:
A computer-based evaluation system acts as an intermediary between manual decision-making and automated selection. The system processes meteorological data, calculates risk indices, and generates recommendations, reducing manual workload while preserving expert judgment through configurable evaluation criteria.
2Reliability
If sufficient number of monitoring stations is built, then monitoring coverage and accuracy are improved, but construction and maintenance costs increase
Solution Approach 1:
The system dynamically determines the optimal number and distribution of monitoring stations based on regional heat wave risk characteristics. High-risk areas receive denser station deployment while low-risk areas use fewer stations, adapting the network configuration to actual monitoring needs rather than applying uniform coverage.
Solution Approach 2:
Different regions receive differentiated station deployment strategies based on their specific heat wave risk profiles. Urban areas with high population density and heat island effects receive priority coverage, while rural areas use fewer strategically positioned stations, optimizing resource allocation according to local requirements.
3Measurement precision
If traditional temperature-only warning system is used, then system simplicity is maintained, but accuracy of heat wave detection is poor due to ignoring heat stress adaptability and other meteorological factors
Solution Approach 1:
The warning system combines multiple meteorological parameters (temperature, humidity, wind speed, pressure) into a composite heat wave risk index. This multi-factor evaluation approach captures the complex nature of heat stress more accurately than temperature alone, while the integrated index format maintains ease of interpretation.
Solution Approach 2:
The system transforms multiple meteorological parameters into a unified risk evaluation framework by calculating heat wave risk indices that reflect regional adaptability differences. This parameter transformation allows comprehensive evaluation while presenting results in a standardized, easily comparable format.
Data Source
AI summary
Disclosed is a method for siting heat wave monitoring stations based on risk evaluation, including: acquiring historical meteorological data of a target region, and preprocessing the historical meteorological data to generate a gridded associated meteorological data set; identifying historical high-temperature heat wave events based on the associated meteorological data set, and calculating parameters and summary indexes of heat wave feature of grids; evaluating station building priority of the grids based on spatial distribution features of the summary indexes; acquiring multi-source data, and evaluating a heat wave risk to generate a heat wave risk map; performing iterative computation using an optimization algorithm based on current station building information, temporal-spatial distribution features of meteorological factors and the heat wave risk map to determine alternative station building positions; and acquiring on-site survey information of each alternative station building position, and determining a position where a station is to be built.