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Earthquake and Flood Prediction Using Geosatellite Images and Machine Learning with a Flutter-Based Mobile Interface

Year: 2024

Type: article

Abstract: The increasing frequency and intensity of natural disasters like earthquakes and floods have heightened the need for advanced predictive mechanisms. This project explores the use of geosatellite imagery and machine learning algorithms for the prediction of earthquakes and floods. By analyzing satellite data, which provides real-time and historical views of environmental changes, terrain shifts, and atmospheric conditions, we aim to develop predictive models that can detect early indicators of these natural disasters. Techniques such as deep learning, image processing, and pattern recognition are employed to extract relevant features from satellite images, including ground deformation, surface water changes, and cloud patterns. The integration of environmental data like seismic activity records, rainfall, and soil moisture with satellite observations enhances the model’s accuracy. The system aims to provide early warning, enabling better disaster preparedness and mitigation strategies, thus reducing loss of life and damage to infrastructure.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14010956

Authors: Nikam, Nishidha, Memane, Poonam, Memane, Karishma, Chadchankar, Prof. Amar, Mote, Prof. Aparna V.

Institution: Zeal College of Engineering and Research, Pune

Language: English

Topic: Earthquake Detection and Analysis

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

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

Open Access status: green

OpenAlex ID: W6968034001

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