Authors: Deolfa Josè Moisès, and Yong Sebastian Nyam, University of the Free State, South Africa. Flood hazards are complex events with severe consequences, particularly for rural riparian communities that depend on natural resources for their live...

Authors and Collaborators:  Shampa, Shammi Haque, Sonia Binte Murshed, Md. Hasanur Rahman, Md Rayhan, Shahriar Ahmed Toufiq, Mashfiqus Salehin, Bangladesh University of Engineering and Technology Erin Coughlan de Perez, Tufts University, USA Flash...

Author: Rhomir Yanquiling, University of Melbourne This research explores the factors influencing the accessibility and actionability of early warning systems (EWS) and risk reduction measures in last-mile communities in Northern Philippines. The stu...

Authors: Olumuyiwa Bayode Adegun (Federal University of Technology Akure), Tobi Eniolu Morakinyo (University College Dublin), Peter Elias (University of Lagos) This research investigates the use of early warning resources for extreme heat in slum com...

This case study documents the Kenya Red Cross Society’s (KRCS) implementation of Community Engagement and Accountability (CEA) initiatives in drought anticipatory action projects in Kitui and Kwale counties. KRCS used early warning systems and ...

Disasters are often portrayed as equal opportunity offenders, affecting everyone in the impacted area. Whilst the process of a hazard becoming a disaster will have a degree of impact on every individual or community,  these impacts will not be unifo...

The Developing Risk Awareness through Joint Action (DARAJA) project is a collaborative initiative aimed at enhancing weather and climate information services (WCIS) for urban communities, with a particular focus on vulnerable populations residing in ...

This issue of Southasiadisasters.net is titled the ‘Urgency of Heatwave Risk Management’. Heatwaves are not only here to stay but will accelerate in their frequency, severity, loss and damage, at all levels. Read 15 vibrant and realize perspectiv...

The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors, compared to existing models that mainly rely on ps...