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Data Fusion and Aggregation Methods to Develop Composite Indexes for a Sustainable Future

Abdullah Konak

TL;DR

This work addresses constructing composite drought risk or resilience indices from multiple indicators. It systematically compares five objective weighting methods—VAR, ENT, PCA, CRITIC, and a novel application of DEA—in simulated and practical contexts to assess how distribution, correlation, and scaling affect index construction. The study demonstrates distinct behavior across methods, highlights the sensitivity of index outcomes to weighting choices, and provides guidance for selecting weighting schemes that balance discrimination and robustness for policy-relevant risk assessments. The findings have practical implications for informing drought resilience policies and monitoring through more robust, data-fusion-based composite indicators.

Abstract

Research on environmental risk modeling relies on numerous indicators to quantify the magnitude and frequency of extreme climate events, their ecological, economic, and social impacts, and the coping mechanisms that can reduce or mitigate their adverse effects. Index-based approaches significantly simplify the process of quantifying, comparing, and monitoring risks associated with other natural hazards, as a large set of indicators can be condensed into a few key performance indicators. Data fusion techniques are often used in conjunction with expert opinions to develop key performance indicators. This paper discusses alternative methods to combine data from multiple indicators, with an emphasis on their use-case scenarios, underlying assumptions, data requirements, advantages, and limitations. The paper demonstrates the application of these data fusion methods through examples from current risk and resilience models and simplified datasets. Simulations are conducted to identify their strengths and weaknesses under various scenarios. Finally, a real-life example illustrates how these data fusion techniques can be applied to inform policy recommendations in the context of drought resilience and sustainability.

Data Fusion and Aggregation Methods to Develop Composite Indexes for a Sustainable Future

TL;DR

This work addresses constructing composite drought risk or resilience indices from multiple indicators. It systematically compares five objective weighting methods—VAR, ENT, PCA, CRITIC, and a novel application of DEA—in simulated and practical contexts to assess how distribution, correlation, and scaling affect index construction. The study demonstrates distinct behavior across methods, highlights the sensitivity of index outcomes to weighting choices, and provides guidance for selecting weighting schemes that balance discrimination and robustness for policy-relevant risk assessments. The findings have practical implications for informing drought resilience policies and monitoring through more robust, data-fusion-based composite indicators.

Abstract

Research on environmental risk modeling relies on numerous indicators to quantify the magnitude and frequency of extreme climate events, their ecological, economic, and social impacts, and the coping mechanisms that can reduce or mitigate their adverse effects. Index-based approaches significantly simplify the process of quantifying, comparing, and monitoring risks associated with other natural hazards, as a large set of indicators can be condensed into a few key performance indicators. Data fusion techniques are often used in conjunction with expert opinions to develop key performance indicators. This paper discusses alternative methods to combine data from multiple indicators, with an emphasis on their use-case scenarios, underlying assumptions, data requirements, advantages, and limitations. The paper demonstrates the application of these data fusion methods through examples from current risk and resilience models and simplified datasets. Simulations are conducted to identify their strengths and weaknesses under various scenarios. Finally, a real-life example illustrates how these data fusion techniques can be applied to inform policy recommendations in the context of drought resilience and sustainability.

Paper Structure

This paper contains 10 sections, 12 equations, 4 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Objective Methods
  3. Variance-based Weighting Method
  4. Entropy-based Weighting Method
  5. Principal Component Analysis
  6. Data Envelopment Analysis
  7. Criteria Importance through Intercriteria Correlation (CRITIC)
  8. Computational Experiments
  9. Results
  10. Conclusions

Figures (4)

  • Figure 1: Boxplot of indicator weights under the Normal Scenario.
  • Figure 2: Boxplot of indicator weights under the Normal-Mixed simulation scenario.
  • Figure 3: Boxplot of indicator weights under the Normal-Correlated simulation scenario.
  • Figure 4: Boxplot of indicator weights under the Systemic Correlated simulation scenario.