Dynamic Notifications in Smart Cities for Disaster Management

Chaudhari, Sampada; Bhagat, Amol P.; Tarbani, Nitesh; Pund, M. A.

doi:10.1007/978-981-10-8055-5_17

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Cities that aim to become a smart city use digital and networked technologies to address different types of problems, such as improving the quality of services, becoming more sustainable, growing the local economy, improving the quality of life, and increasing the safety and security of their inhabitants [16]. In this way, emergency response systems are among the main dimensions of a smart city due to the increase in disruptions caused by natural or man-made disasters [17], which have severe and negative effects on society.…”

Section: Chapter 2 -Literature Review 21 Introductionmentioning

confidence: 99%

“…In this context, to provide protection and safety to the smart city, it is essential to develop appropriate strategies to protect the city from such hazards. To this purpose, it is possible to apply DM and analysis techniques to analyze patterns and predict disasters, allowing the development of appropriate disaster management strategies from the data collected from disasters that have occurred in the past [16].…”

Section: Chapter 2 -Literature Review 21 Introductionmentioning

confidence: 99%

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Data-Driven Disaster Management in a Smart City

Gonçalves

Ferreira

Madureira

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Disasters, both natural and man-made, are complex events that result in the loss of human life and/or the destruction of properties. The advances in Information Technology (IT) and Big Data Analysis represent an opportunity for the development of resilient environments, since from the application of Big Data (BD) technologies it is possible not only to extract patterns of occurrences of events, but also to predict them.The work carried out in this dissertation aims to apply the CRISP-DM methodology to conduct a descriptive and predictive analysis of the events that occurred in the city of Lisbon, with emphasis on the events that affected buildings.Through this research it was verified the existence of temporal and spatial patterns of occurrences with some events occurring in certain periods of the year, such as floods and collapses that are recorded more frequently in periods of high precipitation. The spatial analysis showed that the city center is the area most affected by the occurrences, and it is in these areas where the largest proportion of buildings with major repair needs are concentrated.Finally, machine learning models were applied to the data, and the Random Forest model obtained the best result with an accuracy of 58%. This research contributes to improve the resilience of the city since the analysis developed allowed to extract insights regarding the events and their occurrence patterns that will help the decision-making process.

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Section: Chapter 2 -Literature Review 21 Introductionmentioning

confidence: 99%

Section: Chapter 2 -Literature Review 21 Introductionmentioning

confidence: 99%

Data-Driven Disaster Management in a Smart City

Gonçalves

Ferreira

Madureira

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

show abstract

“…In this environment, the Smart City (SC) concept emerges as the appropriate answer for overcoming the problems posed by globalization and urbanization [5]. Cities aiming to achieve SC status employ digital and networked technology to solve a variety of issues, including enhancing service quality, becoming more sustainable, boosting the local economy, improving quality of life, and ensuring the safety and security of their residents [6].…”

Section: Introductionmentioning

confidence: 99%

“…This data may contain a considerable quantity of contextual, geographical, or temporal information [7]. In catastrophe scenarios, BD plays a critical role in disaster management procedures because it is feasible to use data mining (DM) and analytical tools to examine trends and forecast disasters, allowing the creation of appropriate disaster management plans based on historical data [6]. In this sense, the use of BD technologies aids agents in decision-making by allowing them to recognize possible risks and, as a result, establish suitable plans to deal with catastrophic circumstances, so increasing the SC's resilience [2].…”

Section: Introductionmentioning

confidence: 99%