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<p>Effective coastal zone management requires the optimal integration of system components to enhance resilience while minimizing vulnerability under practical and economic constraints. This study presents an integrated coastal vulnerability assessment framework combining Multi-Criteria Decision Making (MCDM) techniques, metaheuristic optimization, and laboratory-scale physical verification within a unified decision-support system. Bacterial Foraging Optimization (BFO) was employed to determine the optimal management allocation ratios associated with key vulnerability-driving components, namely potential hydraulic energy from waves (PE), distance from the coast (DC), water quality (WQ), and wind speed (WS). The relative importance of these parameters was evaluated using Analytical Hierarchy Process (AHP), Weighted Sum Method (WSM), and Weighted Product Method (WPM) based on performance, reliability, and uncertainty criteria. The derived weights were incorporated into the formulation of a Coastal Vulnerability Index (CVI), which was subsequently minimized under cost constraints using BFO to identify the most effective system configuration. To provide experimental support for the optimization results, a laboratory-scale flume-based physical model was developed to simulate coastal processes, where sediment erosion was used as a proxy for vulnerability. The optimization results indicated a minimum CVI value of 1.859 for the optimal configuration. The laboratory flume experiments further demonstrated that the optimized scenario produced the lowest average sediment erosion (2.50 ± 0.07&#xa0;g), compared with higher erosion observed in alternative scenarios. The findings demonstrate that integrating decision-making techniques, optimization algorithms, and Experimental Verification improves the robustness and reliability of coastal vulnerability assessment. The proposed framework provides a proof-of-concept decision-support approach that may be adapted for future coastal management applications following site-specific calibration and field verification.</p>

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Optimizing coastal vulnerability reduction using ensemble decision-making and bacterial foraging optimization with experimental verification

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Ratnadeep Modak,
  • Sujit Kumar Pal,
  • Ashesh Rudra Paul,
  • Tilottama Chakraborty,
  • Satyabrata Saha,
  • Mrinmoy Majumder

摘要

Effective coastal zone management requires the optimal integration of system components to enhance resilience while minimizing vulnerability under practical and economic constraints. This study presents an integrated coastal vulnerability assessment framework combining Multi-Criteria Decision Making (MCDM) techniques, metaheuristic optimization, and laboratory-scale physical verification within a unified decision-support system. Bacterial Foraging Optimization (BFO) was employed to determine the optimal management allocation ratios associated with key vulnerability-driving components, namely potential hydraulic energy from waves (PE), distance from the coast (DC), water quality (WQ), and wind speed (WS). The relative importance of these parameters was evaluated using Analytical Hierarchy Process (AHP), Weighted Sum Method (WSM), and Weighted Product Method (WPM) based on performance, reliability, and uncertainty criteria. The derived weights were incorporated into the formulation of a Coastal Vulnerability Index (CVI), which was subsequently minimized under cost constraints using BFO to identify the most effective system configuration. To provide experimental support for the optimization results, a laboratory-scale flume-based physical model was developed to simulate coastal processes, where sediment erosion was used as a proxy for vulnerability. The optimization results indicated a minimum CVI value of 1.859 for the optimal configuration. The laboratory flume experiments further demonstrated that the optimized scenario produced the lowest average sediment erosion (2.50 ± 0.07 g), compared with higher erosion observed in alternative scenarios. The findings demonstrate that integrating decision-making techniques, optimization algorithms, and Experimental Verification improves the robustness and reliability of coastal vulnerability assessment. The proposed framework provides a proof-of-concept decision-support approach that may be adapted for future coastal management applications following site-specific calibration and field verification.