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Watershed Planning within a Quantitative Scenario Analysis Framework
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A comparative study on urban waterlogging susceptibility assessment based on multiple data-driven models.

Feifei Han1, Jingshan Yu2, Guihuan Zhou1

  • 1College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China.

Journal of Environmental Management
|May 23, 2024
PubMed
Summary
This summary is machine-generated.

This study compared four data-driven models for urban waterlogging susceptibility in Beijing. Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM) showed the best performance, while Building Density and Frequency of Heavy Rainstorms were key factors.

Keywords:
DISOMachine learningMaxEntUncertainty analysisUrban waterlogging susceptibility

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Area of Science:

  • Environmental Science
  • Urban Planning
  • Geographic Information Systems

Background:

  • Urban waterlogging is a growing concern, necessitating accurate prediction and susceptibility assessment.
  • Data-driven models offer an alternative to complex mechanistic models, incorporating socio-economic factors.
  • Existing research often lacks comprehensive model comparisons and interpretability analyses.

Purpose of the Study:

  • To compare the performance of four data-driven models for urban waterlogging susceptibility mapping.
  • To analyze the interpretability of these models and identify key influencing factors.
  • To develop an integrated approach for reducing prediction uncertainty.

Main Methods:

  • Four models were constructed: Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt).
  • Twelve explanatory variables were used to predict waterlogging susceptibility in Beijing's central area.
  • The Distance between Indices of Simulation and Observation (DISO) was employed for comprehensive model performance evaluation, and a geographical detector was used for interpretability analysis.

Main Results:

  • PSO-WELLSVM demonstrated the highest performance (DISOtest = 0.63), outperforming MaxEnt (DISOtest = 0.78).
  • MaxEnt excelled at identifying highly susceptible areas, while RF and SVM-RBF showed suboptimal performance and overfitting.
  • Building Density (BD) was the most influential factor, followed by Distance to Road and Frequency of Heavy Rainstorms (FHR). Interactions between factors, like BD and FHR, non-linearly increased susceptibility prediction power.

Conclusions:

  • The study highlights the effectiveness of PSO-WELLSVM for waterlogging susceptibility mapping and the importance of considering multiple factors.
  • Integrating multiple models significantly reduces prediction uncertainty compared to single models.
  • Understanding the interplay of factors like Building Density and heavy rainfall is crucial for effective urban waterlogging risk management.