Work

Flood Sense: Data Driven Predictions for Safer Communities

Year: 2025

Type: dissertation

Abstract: Flood is one of the most disastrous and catastrophic natural disasters in the world, which leads to significant human suffering, damage to infrastructure, and economic losses annually. Such disastrous occurrences cause mass casualties, property damage, and loss of vital public services, as well as damaging economic effects on impacted localities. The proposed system addresses these challenges by providing an Autonomous IoT-Enabled Early Warning Framework, which integrates various sensing technologies with smart analytics to provide complete, rapid, and efficient support for flood risk management.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16944043

Authors: Yalley, Ezra, Anirban, Das, Kumar, Saurav, Savasya, Sai BG

Institution: Lovely Professional University

Language: English

Topic: Flood Risk Assessment and Management

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: green

OpenAlex ID: W7077472208

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