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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM26814

Android Based Disaster Management Application Using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM26814

Volume:

7 |

11 |

Nov 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

As

Aayush shah

PS

Prajwal Sananse

KP

Kishor Patil

RG

Rupesh Gujar

DNT

Dr. Naresh Thoutam

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Abstract

A good saying” Precaution is better than cure” for emergency situations. Natural disasters in India, many of them related to the climate of India, cause massive losses of Indian life and property. Droughts, flash floods, cyclones, avalanches, landslides brought on by torrential rains and snowstorms pose the greatest threats. Landslides are common in the Lower Himalayas. Parts of the Western Ghats also suffer from low-intensity landslides. Floods are the most common natural disaster in India. Therefore, developing a system which can monitor as well as providing instructions on basis of current disaster using Supervised learning, reducing time for quick actions, Google map API for locating nearby locations, Disaster prediction using data analytics. The mobile application will have four tabs: Alert buzzer, weather forecast, the menu tab and the communication forum. Furthermore, the system notifies the users through SMS and even through the forum whenever there is the alert notification posted on the application Using system can help a great deal in the planning and implementation of disaster mitigation schemes.

KEYWORDS

ML-Machine Learning SVM-Support Vector Machine NDRF-National Disaster Response Force SDRF-State Disaster Response Force

Cite This Article

Aayush shah, Prajwal Sananse, Kishor Patil, Rupesh Gujar, Dr. Naresh Thoutam, "Android Based Disaster Management Application Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 11, Nov 2023. https://doi.org/10.55041/IJSREM26814

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Publication Date

11/18/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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