Natural Disaster Prediction by Using Image Based Deep Learning and Machine Learning

Title

Natural Disaster Prediction by Using Image Based Deep Learning and Machine Learning

Creator

Vinod A.M.; Venkatesh D.; Kundra D.; Jayapandian N.

Description

In recent years, diseases and disaster have become more unpredictable. The advent of technology has not only making our lives easier but also technology-dependent. Nevertheless, the natural disasters cause great adversity by disrupting considerable human lives. Also, the disasters obstruct and affect many industries and services either directly or indirectly. Hence, it is necessary to study and observe data patterns and warning signs that lead to a natural disaster, its potential risk and its ability to resolve management strategies, which can be implemented immediately to minimize the socio-economic loss. This article reviews the state-of-the-art research works and findings through a technological perspective on data analysis, natural disaster prediction, and the utilization of technology for deploying management strategy. Also, this paper focuses on investigating the today's Industry 4.0 that utilizes cognitive computing. The primary aim of this article is to review the research ideas that leverage big data and data mining to observe and track patterns, which can impelment predictive analysis to anticipate the forthcoming disasters. Furthermore, this research work analyzed the posed predictive models by specifically using ANN (Artificial Neural Networks), sentiment model, and smart disaster prediction application (SDPA) to predict the flash flood. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Source

Lecture Notes in Networks and Systems, Vol-300 LNNS, pp. 56-66.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial neural networks; Big data; Data mining; Deep learning; Disaster prediction; Machine learning

Coverage

Vinod A.M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Venkatesh D., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Kundra D., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-303084759-3

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-030-84760-9_6

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115641117&doi=10.1007%2f978-3-030-84760-9_6&partnerID=40&md5=3acbe39289ba763cc78e35cf045874a6