Detection of flood disaster system based on IoT, big data and convolutional deep neural network

Anbarasan, M.; Muthu, BalaAnand; Sivaparthipan, C. B.; Sundarasekar, Revathi; Kadry, Seifedine; Krishnamoorthy, Sujatha; Samuel, R. Dinesh Jackson; Dasel, A. Antony

doi:10.1016/j.comcom.2019.11.022

Cited by 179 publications

(64 citation statements)

References 13 publications

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“…It was shown in the paper that multi-layer neural network (MLNN) required a lower number of learning parameters than other architectures. The problem of big data analysis was addressed also in Anbarasan et al (2020), where authors used a combination of IoT and CNN in big data scenarios with the flood detection system, giving better results than other competitive methods. In addition to feature extraction, feature selection is also one of the key points in machine learning to eliminate the redundancy of data or avoiding the curse of dimensionality.…”

Section: Valuementioning

confidence: 99%

Computational Intelligence in the Context of Industry 4.0

Hosŏvský

Piteľ

Trojanová

et al. 2021

Implementing Industry 4.0 in SMEs

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Industry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

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Section: Valuementioning

confidence: 99%

Computational Intelligence in the Context of Industry 4.0

Hosŏvský

Piteľ

Trojanová

et al. 2021

Implementing Industry 4.0 in SMEs

View full text Add to dashboard Cite

show abstract

“…It can be used as an advanced level tool for detecting fine cracks, damaged structure and the extent of damages. UAVs comprises a different kind of sensors such as camera, infrared, ultra-violet, radiation and weather sensors along with spectrum analyzers [21]. It is a tool that can supply useful information to transportation planning related to real-time and situational information.…”

Section: A Big Data Sourcesmentioning

confidence: 99%

“…Social media can be used in various ways for disaster management. Social media data should be collected carefully and then process, analyze and decision making are necessary for disaster management [21]. Similarly, the decision about disaster can be easy spread-out using social media.…”

Section: ) Crowdsourcingmentioning

confidence: 99%

Climate Change Adaptation and Resilience through Big Data

Sarker¹,

Yang²,

Lv³

et al. 2020

IJACSA

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The adverse effect of climate change is gradually increasing all over the world and developing countries are more sufferer. The potential of big data can be an effective tool to make an appropriate adaptation strategy and enhance the resilience of the people. This study aims to explore the potential of big data for taking proper strategy against climate change effects as well as enhance people's resilience in the face of the adverse effect of climate change. A systematic literature review has been conducted in the last ten years of existing kinds of literature. This study argues that resilience is a process of bounce back to the previous condition after facing any adverse effect. It also focuses on the integrated function of the adaptive, absorptive and transformative capacity of a social unit such as individual, community or state for facing any natural disaster. Big data technologies have the capacity to show the information regarding upcoming issues, current issues and recovery stages of the adverse effect of climate change. The findings of this study will enable policymakers and related stakeholders to take appropriate adaptation strategies for enhancing the resilience of the people of the affected areas. Keywords-Disaster resilience; administrative resilience; community resilience; disaster management; environmental managementAs a new paradigm, a big data approach is considered as the most effective method for taking quick and effective decisions [9]. It can analyze the huge amount of data obtained from various sources such as weather data, social media, electronic and print media, non-governmental organizations, voluntary community organizations, and various social networking sites. Big data provides a big opportunity for communication which can help susceptible community people about an upcoming threat, challenges, risks and disasters [10]. Communication provides a way to communicate with each other before, during and after a disaster to inform the condition to one another and make preparation and also acts as a source of big data [11]. Big data encourage researchers and policymakers to conduct an in-depth analysis of communication data from a mobile phone, social media and other communication devices which has big rationality for disaster resilience.

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“…IoT relies on universal device addressing on devices, so they are easy to control and use for certain purposes. One of IoT implementations is in the case of early detection such as flood disasters [7] [8], fires [9], and or disorder such as theft [10] or robbery [11]. Figure 2 shows that IoT Framework can be installed on various sensor devices that have an IP address.…”

Section: Internet Of Thingsmentioning

confidence: 99%

Dissemination Application for Early Warning in a Community

Fahrudin

Kastaman

2020

IJAIT

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The development of sensor technology for early warning at an affordable price makes it possible to be implemented in the community. Therefore, an application is needed to send this early warning information to community members and warning response centers in real-time by considering the distance from the location of the incident. So, in this research, we proposed a dissemination application for early warning in a community (DissApps). The application was build using Code Igniter Framework, MySQL database, Mapquests, and Wablas API. This application can be used for disseminating emergency response situations such as thievery, a natural disaster such as earthquake, flood, forest fire.

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Detection of flood disaster system based on IoT, big data and convolutional deep neural network

Cited by 179 publications

References 13 publications

Computational Intelligence in the Context of Industry 4.0

Computational Intelligence in the Context of Industry 4.0

Climate Change Adaptation and Resilience through Big Data

Dissemination Application for Early Warning in a Community

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