International Journal For Multidisciplinary Research

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Real-time Flood Prediction using a Big Data Approach

Author(s) Kishor Yadav Kommanaboina
Country United States
Abstract Floods have historically presented serious threats, putting infrastructure and lives in jeopardy throughout the world. There has never been a greater need for accurate and fast flood forecasts as climate change exacerbates existing situations. By using state-of-the-art algorithms and real-time integration to create a groundbreaking flood prediction platform, this research seeks to close important gaps. A comprehensive resource is created by combining a variety of datasets, including Internet of Things sensor streams, hydrological readings, and meteorological observations. For extremely accurate short-term flooding forecasts, advanced machine learning techniques—like deep neural networks and hybrid statistical methods—are trained on the live dataset. Pre-warnings from the suggested adaptive system are expected to become more and more dependable, enhancing readiness and reaction operations. Going forward, continually refining models with growing data repositories, exploring novel inputs, and extending the framework to other natural hazards will further strengthen resilience against these escalating threats.
Keywords Big Data, Data pipelines, Distributed Data Systems, Flood Prediction, Real-Time Data, Machine Learning, Deep Learning, IoT Sensors, Disaster Management, Data Integration.
Field Computer > Data / Information
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-09-30
Cite This Real-time Flood Prediction using a Big Data Approach - Kishor Yadav Kommanaboina - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.28244
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.28244
Short DOI https://doi.org/g688r5
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