First published 10/3/2026, 8:34:15 PM · Last updated 10/3/2026, 8:34:15 PM

Explainable Machine Learning-Based Multi-Hazard Risk Assessment and Upazila-Level Intervention Prioritization in the Chattogram Region, Bangladesh

A peer-reviewed journal article reports a study applying explainable machine learning to evaluate multiple hazards and prioritize subdistrict-level interventions in the Chattogram region of Bangladesh. The item was published by Elsevier BV on 2026-10-01.

Categories: severe-weather-and-natural-disasters, environment-and-climate, science-and-space

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence: 6/10How certain the assessment is based on the clarity and evidence in the cited reporting.
Geographic reach: 3/10How widely the event reaches, from a local area (1) to worldwide impact (10).
Global importance: 3/10The event's broader significance, from limited importance (1) to major global importance (10).
Impact magnitude: 5/10The scale or severity of the real-world impact, from minor (1) to extreme (10).
Positivity: 7/10How harmful or beneficial the event is, from strongly harmful (1) to strongly positive (10).
Urgency: 2/10How time-sensitive the event is, from low urgency (1) to immediate attention needed (10).

Why it matters

The research is intended to support local hazard risk assessment and intervention prioritization in Chattogram, Bangladesh.

Sources

Explainable Machine Learning-Based Multi-Hazard Risk Assessment and Upazila-Level Intervention Prioritization in the Chattogram Region, BangladeshMetadata provided by Crossref. Usage terms

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