WebGIS for Disaster Management and Emergency Response

Abdalla, Rifaat; Esmail, Marwa

doi:10.1007/978-3-030-03828-1

Cited by 11 publications

(10 citation statements)

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“…Adapun tindakan antisipatif tersebut dapat berupa pengaturan tata guna lahan, perencanaan tata ruang, penyusunan data, penyusunan peta kerentanan bencana, serta pemantauan dan pengembangan [4]. Untuk mengurangi dampak yang diakibatkan dari kebakaran, maka diperlukan pemetaan zona risiko bencana karena dapat mengantisipasi faktor yang mempengaruhi kejadian kebakaran dan memahami perilaku dinamis penanganan kebakaran [5]. Upaya untuk mengantisipasi dampak dari bencana kebakaran di wilayah perkotaan Kabupaten Kutai Timur salah satunya dapat dilakukan dengan melakukan pemetaan kerentanan bencana kebakaran.…”

Section: Pendahuluanunclassified

Tipologi wilayah berdasarkan tingkat kerentanan di wilayah perkotaan Kabupaten Kutai Timur

Ghozali

Kautsar

2022

Jur. Reg. Jur. Pemb. Wil. dan Perenc. Part.

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<p class="Abstract">Tingkat kejadian bencana kebakaran di wilayah perkotaan Kabupaten Kutai Timur dari tahun 2012 – 2019 mengalami peningkatan jumlah kejadian yang signifikan, terutama pada Kawasan Perkotaan Kabupaten Kutai Timur. Di sisi lain, pemerintah daerah setempat hingga saat ini belum memiliki instrumen pengendalian kejadian kebakaran. Oleh karena itu, penelitian ini bertujuan untuk melakukan pemetaan kerentanan kebakaran di wilayah perkotaan Kabupaten Kutai Timur. Untuk menghasilkan tipologi, analisis overlay pada software ArcGIS digunakan dengan mempertimbangkan faktor fisik, sosial, sarana permukiman, dan kebijakan. Selain itu, pembobotan faktor dengan AHP yang melibatkan tujuh <em>stakeholder</em> kunci digunakan untuk mengetahui faktor prioritas sesuai dengan kondisi kawasan. Hasil analisis diperoleh bahwa kerentanan fisik memiliki bobot 0,45, kerentanan sosial memiliki bobot 0,33, dan kerentanan ekonomi memiliki bobot 0,22. Dari prioritas faktor ini diperoleh bahwa Kawasan Perkotaan Kutai Timur terdapat tiga tipologi kerentanan kebakaran. Desa Swarga Bara, Desa Singa Gembara, dan Desa Sangatta Selatan memiliki tingkat kerentanan rendah, sedangkan Kelurahan Teluk Lingga memiliki kerentanan sedang, serta Kelurahan Singa Geweh dan Desa Sangatta Utara memiliki tingkat kerentanan tinggi.</p>

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Section: Pendahuluanunclassified

Tipologi wilayah berdasarkan tingkat kerentanan di wilayah perkotaan Kabupaten Kutai Timur

Ghozali

Kautsar

2022

Jur. Reg. Jur. Pemb. Wil. dan Perenc. Part.

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“…IT-based crisis management [33] provides decision-making support while raising public awareness of disasters, supports the communication and dissemination of information and alerts and promote the implementation of crisis management-related regulations. While Disaster management model [31] is based on Geospatial Information Technology (GIS), however, it is limited to certain (natural) disasters. It is also available for effective use of satellite positioning, remote sensing and GIS, for disaster monitoring and management.…”

Section: Related Workmentioning

confidence: 99%

Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

Bouzidi

Boudries

Amad

2022

Preprint

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Networking continues to increase day by day to invest increasingly our daily, creating huge volume of various and precise data: it is not easy to collect content, especially in crisis times. We focus on Smart Education proposed as primary tool of a hybrid of Deep Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM)-based model to retrieving content efficiently: CNN is used to extract meaningful features from multiple sources, enabling to have qualitative and sure information, notably with an efficient fake news security, and LSTM is used to maintain long-term dependencies in the extracted features with recurrent connections. This model has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach makes it possible to integrate artificial intelligence technologies, deep learning, social media and detecting or avoiding fake news into the crisis management model. It is based on an extension of our previous approach, namely disaster management based on short-term memory and education: this experience constitutes a background for this model. It combines representation training with awareness and education, while retrieving pattern information by combining various search results from multiple sources. We have extended it to improve our disaster management model and evaluate it in the case of Covid-19 while obtaining promising results, through past programs and experience that have shown overwhelmingly positive effects of education for vulnerability reduction and disaster risk management, in the pusuit of Environmental Sustainability.

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“…Finally, a frontier of great interest for research and public administrations lies in the possibility of integrating, in a single database, various sources of real-time data using sensors and short and long-range communications as well as alert and monitoring systems that are prepared by both central and international protocols as well as spontaneous user reports [32], [35]- [37].…”

Section: Planning An Evacuation Modelmentioning

confidence: 99%

Risk Reduction in Transport System in Emergency Conditions: A Framework for Decision Support Systems

Gangi

Belcore

2021

WIT Transactions on the Built Environment

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Risk analysis and assessment allow analysts and decision-makers to measure whether the losses (economic and human lives) are tolerable and define appropriate actions. Moreover, planning tools and models applied to the emergency decision management (EDM) and spatial decision support system (SDSS) improve the collective interest in increasing the resilience for societal risks. Starting from recent improvements offered by the liberalisation for the GPS data and open data services, this paper aims to suggest an architecture for an open data Web SDSS framework. Furthermore, it integrates into its modular structure the possibility to analyse both networks and evacuees behaviours through the integration of the transportation system model (TSM) models with geographical information systems (GIS), information technology systems (ITS) and volunteered geographic information (VGI). The main advantage of such a system is coping with incoming and in-progress emergencies, utilising on-time data, connecting multiple databases and sources of information, and implementing a participatory process involving different stakeholders.

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WebGIS for Disaster Management and Emergency Response

Cited by 11 publications

References 0 publications

Tipologi wilayah berdasarkan tingkat kerentanan di wilayah perkotaan Kabupaten Kutai Timur

Tipologi wilayah berdasarkan tingkat kerentanan di wilayah perkotaan Kabupaten Kutai Timur

Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

Risk Reduction in Transport System in Emergency Conditions: A Framework for Decision Support Systems

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