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May 16, 2026Water Practice & TechnologyOpen Access

A hybrid technique for flood inundation detection using Pareto scaling normalization and bayesian probability

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Authors

PHPayal HazraIndian Institute of Advanced ResearchKSKrunal B. SutharIndian Institute of Advanced ResearchKSKoyel SurPunjab Remote Sensing Centre

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Implication

Randomized trial assesses flood mapping and land use changes due to inundation, indicating significant environmental impacts.

Key Points

  • This research aims to develop an effective hybrid technique for flood inundation detection and analyze the subsequent land use changes.
  • Integrating geo-spatial technology with statistical modeling and field validation.
  • Using Sentinel-1 SAR data and Pareto scaling normalization with Bayesian probability for flood mapping.
  • Employing random forest classification of Sentinel-2 optical data for land use/land cover changes.
  • The 2020 flood inundation reached 1,440.53 km2 with over 1,800 mm rainfall, while 2024 saw 670 km2 inundation with only 350 mm rainfall, indicating non-climatic influences.
  • Surface water bodies expanded by 50.7% from 2019 to 2024, while agricultural land decreased by 2.1%.
  • Built-up areas nearly doubled in size from 558.77 km2 to 978.76 km2 during the study period.

Cite This Study

Hazra et al. (2026) studied this question.

synapsesocial.com/papers/6a080b4ea487c87a6a40d7b6https://doi.org/10.2166/wpt.2026.279
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