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Construction of flood loss function for cities lacking disaster data based on three-dimensional

Hong Lv¹, Yu Meng¹, Zening Wu¹

¹School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou, Henan 450001, PR China.

The Science of the Total Environment

|May 4, 2021

Summary

This summary is machine-generated.

This study develops a Flood Inundated Depth-Loss Rate Function (FILF) for cities lacking historical data, improving flood risk management. The novel method uses data transfer and optimization to create reliable loss estimation models for arid regions.

Keywords:

Beta distribution Correlation coefficient Data mining Space transfer Three-level optimization

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Area of Science:

Hydrology and Water Resources Engineering
Risk Management
Urban Planning

Background:

Reliable flood loss estimation is critical for effective flood risk management.
Existing Flood Inundated Depth-Loss Rate Functions (FILF) are difficult to apply in arid/semi-arid cities due to limited historical disaster data.
Increasing flood risks necessitate the development of new loss assessment models for data-scarce urban areas.

Purpose of the Study:

To develop a scientific and reasonable flood loss assessment model for cities lacking historical disaster data.
To construct a FILF for data-scarce inland arid and semi-arid plain cities.
To address the challenge of absent loss functions in urban flood risk management.

Main Methods:

Utilized the analogy principle to transfer flood loss rate data from cities with available data to those without, using amplified characteristic indices.
Established three-dimensional data processing rules, prioritizing object dimensional variance and the greatest correlation coefficient (CC) for joint dimension fitting.
Employed a three-level optimization process to construct the FILF for cities lacking disaster data.

Main Results:

Successfully constructed a FILF for eight property types in Zhengzhou City, China, with optimal function and array dimensions identified (F6 Biquadratic, D4-D6).
Achieved high correlation coefficients (CCs) exceeding 0.9935 (average 0.9971), indicating robust model fitting.
Demonstrated that function dimension is more sensitive to FILF than array dimension, with simulated 20-year flood loss reaching 2.46 billion yuan and showing clear spatial economic disparity.

Conclusions:

The developed method effectively constructs FILFs for cities with limited or no historical disaster data.
This approach provides a viable solution for urban flood risk management in data-scarce regions.
The findings highlight the importance of adaptable loss estimation models for diverse geographical and data availability contexts.