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

Vinod, Angela Maria; Venkatesh, D; Kundra, Dishti; Jayapandian, N.

doi:10.1007/978-3-030-84760-9_6

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…A disaster refers to any unexpected and sudden event that leads to the weakening and destruction of economic, social, and physical capabilities, resulting in life and financial losses, as well as the destruction of infrastructure facilities, economic resources, and employment opportunities within society [ 1 – 3 ]. While some disasters such as floods and hurricanes can be predicted to some extent, earthquakes remain less predictable [ 4 , 5 ]. Consequently, disaster preparedness becomes a fundamental principle in crisis management for any country, with a particular emphasis on safeguarding critical facilities like hospitals and their staff during such critical times [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Components of hospital personnel preparedness to evacuate patients in disasters: a systematic review

Moslehi,

Masbi,

Noori

et al. 2024

BMC Emerg Med

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Background During natural catastrophes, hospital staff members’ readiness for crisis management—particularly concerning patient evacuation and improving their safety—becomes paramount. This study aimed to identify the components contributing to hospital staff members’ preparedness to evacuate patients in an emergency. Method A systematic review was conducted by searching databases such as Scopus, Web of Science, PubMed, ProQuest, and grey literature through May 2023. Studies that offered unique qualitative or quantitative data regarding hospital personnel readiness to evacuate patients in an emergency were included. Thematic analysis and descriptive statistics were used to examine the extracted data points. Results In total, there were 274 scientific articles. The total number of unique studies decreased to 181 after removing duplicate articles. 28 papers that were deemed appropriate for additional study were found based on the titles and abstracts of these articles. Eighteen papers that met the inclusion criteria were selected for the systematic review after their entire texts were finally assessed. Hospital staff preparedness for patient evacuation was divided into four primary topics and nineteen sub-themes. The four primary themes that emerged were management, communication, individual issues, and training on the evacuation process. Conclusion The implementation of proper disaster evacuation training programs can be achieved by elevating the perceived sensitivity and protective motive of personnel and considering the personnel’s stages of change. Training hospital staff to properly evacuate patients during disasters is also significantly impacted by other factors, such as effective administration, leadership and prompt and efficient communication.

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Section: Introductionmentioning

confidence: 99%

Components of hospital personnel preparedness to evacuate patients in disasters: a systematic review

Moslehi,

Masbi,

Noori

et al. 2024

BMC Emerg Med

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“…The investigation emphasizes how susceptible the people of Lebanon are to natural calamities. Poor agricultural populations in developing nations are particularly vulnerable to climate variability, which can result in lost crop income and asset destruction [4]. Household well-being is greatly impacted by disasters in all social and economic categories.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Post-Disaster Analysis Using Image Processing Techniques

Gupta,

Helena Raj,

Lal

et al. 2024

E3S Web of Conf.

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Post-disaster recovery is a multifaceted system essential for rebuilding communities and infrastructure. Despite its importance, many limitations obstruct powerful recuperation, main to tremendous loss of life and monetary assets. This paper synthesizes varied approaches in the direction of sustainable restoration, highlighting the increasing reliance on technology for disaster management. Image processing strategies, pivotal in addressing these demanding situations, are reviewed across studies. Those strategies range from SLIC segmentation and Random forest classification to advanced deep learning models together with U-net and YOLOv8, machine learning algorithms like SVM, and image category methodologies along with bi-temporal analysis. Comparative evaluation reveals that those strategies presents promising consequences, with accuracies starting from 75% to over 94%. The paper gives a framework for understanding the role of various image processing strategies in improving disaster control strategies, emphasizing their implications for future studies and application.

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Cancer Prognosis by Using Machine Learning and Data Science: A Systematic Review

Mohan

Jayapandian

2023

Lecture Notes in Networks and Systems

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

Cited by 11 publications

References 26 publications

Components of hospital personnel preparedness to evacuate patients in disasters: a systematic review

Components of hospital personnel preparedness to evacuate patients in disasters: a systematic review

A Comparative Analysis of Post-Disaster Analysis Using Image Processing Techniques

Cancer Prognosis by Using Machine Learning and Data Science: A Systematic Review

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