A Decision Process for Optimizing Multi-Hazard Shelter Location Using Global Data

Abstract

Mitigating the effects of natural hazards through infrastructure planning requires integration of diverse types of information from a range of fields, including engineering, geography, social science, and geology. Challenges in data availability and previously siloed data have hindered the ability to obtain the information necessary to support decision making for disaster risk management. This is particularly challenging for areas susceptible to multiple types of natural hazards, especially in low-income communities that lack the resources for data collection. The data revolution is altering this landscape, due to the increased availability of remotely sensed data and global data repositories. This work seeks to leverage these advancements to develop a framework using open global datasets for identifying optimal locations for disaster relief shelters. The goal of this study is to empower low-income regions and make resilience more equitable by providing a multi-hazard shelter planning framework that is accessible to all decision-makers. The tool described integrates spatial multi-criteria decision analysis methods with a network analysis procedure to inform decisions regarding disaster shelter planning and siting.

Decision-making for community resilience: A review of decision support systems and their applications

“… 11 Floods P SM Secondary [ 16 ] France (City) Build a spatial decision support system guided by resilience assessment measures. 12 Multiple R O, SM, MCDA Secondary [ 41 ] Haiti (Country) Find optimal shelter locations following a risk minimization and service availability maximization approach. 13 Earthquakes RC O Secondary [ 42 ] USA (City) Allocate resources to building retrofits after disasters.…”

Satisfaction and challenges of beneficiaries of the Yolanda Permanent Housing Program in a component city of the Philippines

“…The post-disaster-resilient housing relates to the United Nations' Sustainable Development Goal (SDG) 11 working to create cities and settlements that are sustainable, comprehensive, safe and resilient [1]. Its importance could not be overlooked considering the 24.6 million people worldwide who were displaced and needed disaster-resilient shelter as estimated in 2019 [2]. Moreover, in 2021, the figure increased to 101.8 million with 10,492 deaths and estimated losses of 252.1 billion US$ [3].…”

Pre-Positioning Pusat Distribusi Bantuan Bencana Banjir di Kota Surakarta, Indonesia dengan Menggunakan Metoda Fuzzy Electre

“…Sebagian peneliti merumuskan persoalan sejenis sebagai mixed-integer non-linear programming problem (MINLP) (Abazari, Aghsami, & Rabbani, 2021;Bayram & Yaman, 2018). Beberapa makalah menyajikan persoalan pre-positioning pusat distribusi bantuan bencana sebagai persoalan kriteria-tujuan majemuk (Abazari et al, 2021;Bai, Gao, & Liu, 2018;Curran, Bates, & Bell, 2014;Geng, Hou, & Zhang, 2020;Godschall, Smith, Hubler, & Kremer, 2020;Mohammadi, Ghomi, & Jolai, 2016;Soltani-Sobh, Heaslip, Scarlatos, & Kaisar, 2016). Persoalan-persoalan tersebut diselesaikan dengan menggunakan berbagai metode, di antaranya berbagai teknik optimisasi metaheuristik (Abazari et al, 2021;Gu et al, 2018;Klibi, Ichoua, & Martel, 2018;Li, Nozick, Xu, & Davidson, 2012;Liang et al, 2019;Mohammadi et al, 2016;Mostajabdaveh et al, 2019;Ozbay, Çavuş, & Kara, 2019;Velasquez et al, 2019;Xu et al, 2018;Zhao, Chen, et al, 2019;Zhao, Coates, & Xu, 2019); teknik optimisasi eksak (Baskaya et al, 2017;Mostajabdaveh et al, 2019;Ozbay et al, 2019;Pérez-Galarce et al, 2017); dan teknik pemeringkatan (Curran et al, 2014;Geng et al, 2020;Godschall et al, 2020;Trivedi, 2018).…”