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  1. Home
  3. Comprehensive Drought Risk Assessment And Mapping In Taiwan: An Anp-ann Ensemble Approach.
  1. Home
  3. Comprehensive Drought Risk Assessment And Mapping In Taiwan: An Anp-ann Ensemble Approach.

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Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach.

Yuei-An Liou1, Trong-Hoang Vo2, Duy-Phien Tran2

  • 1Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Road, Jhongli District, Taoyuan City 320317, Taiwan.

The Science of the Total Environment
|August 30, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel ensemble approach using Analytic Network Process (ANP) and Artificial Neural Network (ANN) to map drought risk in Taiwan. The model accurately predicts drought impacts, aiding disaster management.

Keywords:
ANP-ANNDrought risk frameworkExposureHazardVulnerability

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Area of Science:

  • Environmental Science
  • Data Science
  • Risk Management

Background:

  • Drought poses significant risks to Taiwan's diverse ecological and socioeconomic systems.
  • Accurate drought risk assessment is crucial for effective mitigation and adaptation strategies.

Purpose of the Study:

  • To develop and validate a comprehensive drought risk map for Taiwan.
  • To evaluate the effectiveness of an ensemble learning approach combining ANP and ANN for drought risk prediction.

Main Methods:

  • Utilized an ensemble learning method integrating Analytic Network Process (ANP) for indicator weighting and Artificial Neural Network (ANN) for risk modeling.
  • Incorporated twenty indicators across hazard, exposure, and vulnerability to construct a holistic drought risk assessment framework.
  • Trained and validated the ANN model using extensive datasets, achieving high performance metrics.

Main Results:

  • The trained ANN model demonstrated high predictive accuracy (0.940), precision (0.946), recall (0.938), F1 score (0.942), and Kappa Index (0.923).
  • The drought risk map achieved validation accuracies between 0.717 and 0.851 in assessing crop damage and economic losses.
  • Fieldwork and statistical data validation confirmed the map's reliability across multiple reference sources.

Conclusions:

  • The ANP-ANN ensemble approach is a robust and effective method for rapid drought risk prediction.
  • The developed drought risk map provides a reliable tool for diverse ecological and socioeconomic scenarios in Taiwan.
  • This study highlights the potential of integrated modeling for enhancing disaster preparedness and management.