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<p>The increasing frequency of floods in recent years, driven by climate change and human activities, has heightened flood hazards and underscored the need for effective flood management. Consequently, accurate flood mapping and modeling have become crucial for flood prediction, risk management, and hazard assessment. The transition from traditional flood modeling methods to more automated approaches has led to the development of various models, each offering different techniques for generating flood maps with varying degrees of accuracy. This study reviews the strengths and limitations of flood mapping approaches, including hydrodynamic models, Remote Sens technology, and multi-criteria decision-making, with a focus on data accessibility, validity, and accuracy. Future directions in flood mapping for reliable flood management are also explored based on recent advancements. The findings revealed that while in-situ data are often incorporated in flood modeling, its availability greatly affects the choice of flood mapping approach. This limitation can be addressed by utilizing Remote Sens datasets. The integration of multi-criteria decision-making in flood modeling has improved mapping accuracy by helping modelers optimize model parameters. As flood mapping techniques and hydroclimatic conditions evolve, operational flood forecasting systems are increasingly adopting ensemble-based probabilistic flood maps. The incorporation of artificial intelligence and climate change scenarios should also be considered in future flood mapping efforts. These insights enhance the understanding of the applicability, strengths, and limitations of each approach, with important implications for future flood studies, risk assessments, and hazard mitigation.</p>

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Flood mapping approaches: a review of models, data accuracy, limitations, and future perspectives

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Amirah Nadiah Abd Rahman,
  • Faridah Othman,
  • Wan Zurina Wan Jaafar,
  • Ahmed El-Shafie,
  • Cia Yik Ng

摘要

The increasing frequency of floods in recent years, driven by climate change and human activities, has heightened flood hazards and underscored the need for effective flood management. Consequently, accurate flood mapping and modeling have become crucial for flood prediction, risk management, and hazard assessment. The transition from traditional flood modeling methods to more automated approaches has led to the development of various models, each offering different techniques for generating flood maps with varying degrees of accuracy. This study reviews the strengths and limitations of flood mapping approaches, including hydrodynamic models, Remote Sens technology, and multi-criteria decision-making, with a focus on data accessibility, validity, and accuracy. Future directions in flood mapping for reliable flood management are also explored based on recent advancements. The findings revealed that while in-situ data are often incorporated in flood modeling, its availability greatly affects the choice of flood mapping approach. This limitation can be addressed by utilizing Remote Sens datasets. The integration of multi-criteria decision-making in flood modeling has improved mapping accuracy by helping modelers optimize model parameters. As flood mapping techniques and hydroclimatic conditions evolve, operational flood forecasting systems are increasingly adopting ensemble-based probabilistic flood maps. The incorporation of artificial intelligence and climate change scenarios should also be considered in future flood mapping efforts. These insights enhance the understanding of the applicability, strengths, and limitations of each approach, with important implications for future flood studies, risk assessments, and hazard mitigation.