Development and validation of risk profiles of West African rural communities facing multiple natural hazards

West Africa has been described as a hotspot of climate change. The reliance on rain-fed agriculture by over 65% of the population means that vulnerability to climatic hazards such as droughts, rainstorms and floods will continue. Yet, the vulnerability and risk levels faced by different rural social-ecological systems (SES) affected by multiple hazards are poorly understood. To fill this gap, this study quantifies risk and vulnerability of rural communities to drought and floods. Risk is assessed using an indicator-based approach. A stepwise methodology is followed that combines participatory approaches with statistical, remote sensing and Geographic Information System techniques to develop community level vulnerability indices in three watersheds (Dano, Burkina Faso; Dassari, Benin; Vea, Ghana). The results show varying levels of risk profiles across the three watersheds. Statistically significant high levels of mean risk in the Dano area of Burkina Faso are found whilst communities in the Dassari area of Benin show low mean risk. The high risk in the Dano area results from, among other factors, underlying high exposure to droughts and rainstorms, longer dry season duration, low caloric intake per capita, and poor local institutions. The study introduces the concept of community impact score (CIS) to validate the indicator-based risk and vulnerability modelling. The CIS measures the cumulative impact of the occurrence of multiple hazards over five years. 65.3% of the variance in observed impact of hazards/CIS was explained by the risk models and communities with high simulated disaster risk generally follow areas with high observed disaster impacts. Results from this study will help disaster managers to better understand disaster risk and develop appropriate, inclusive and well integrated mitigation and adaptation plans at the local level. It fulfills the increasing need to balance global/regional assessments with community level assessments where major decisions against risk are actually taken and implemented

Modeling an ontology on accessible evacuation routes for emergencies

Providing alert communication in emergency situations is vital to reduce the number of victims. However, this is a challenging goal for researchers and professionals due to the diverse pool of prospective users, e.g. people with disabilities as well as other vulnerable groups. Moreover, in the event of an emergency situation, many people could become vulnerable because of exceptional circumstances such as stress, an unknown environment or even visual impairment (e.g. fire causing smoke). Within this scope, a crucial activity is to notify affected people about safe places and available evacuation routes. In order to address this need, we propose to extend an ontology, called SEMA4A (Simple EMergency Alert 4 [for] All), developed in a previous work for managing knowledge about accessibility guidelines, emergency situations and communication technologies. In this paper, we introduce a semi-automatic technique for knowledge acquisition and modeling on accessible evacuation routes. We introduce a use case to show applications of the ontology and conclude with an evaluation involving several experts in evacuation procedures. © 2014 Elsevier Ltd. All rights reserved

ANDROID: An Inter-disciplinary Academic Network that Promotes Co-operation and Innovation among European Higher Education to Increase Society's Resilience to Disasters

Using knowledge, innovation and education to build a culture of safety and resilience at all levels is one of five priorities for action (PFA) that were identified in the Hyogo Framework for Action (HFA). The responsibility for such capacity building resides largely with educators such as higher education institutes, but the complexity of resilience poses a number of challenges. This paper describes ANDROID, an EU funded international partnership of higher education institutes and key actors in disaster resilience, which has been formed to develop innovative European education. ANDROID is based on an inter-disciplinary consortium of partners that comprises scientists from applied, human, social and natural disciplines. ANDROID set out to achieve this aim through a series of inter-linked projects, identified as work packages and led by a sub-group of international partners. This paper describes these projects and highlights key outputs achieved to date: an inter-disciplinary doctoral school; a survey capturing and sharing innovative approaches to inter-disciplinary working; a survey of European education to map teaching and research programmes in disaster resilience; a survey analysing the capacity of European public administrators to address disaster risk; emerging research and teaching concerns in disaster resilience; and, open educational resources

Risk Assessment Model for Pluvial Flood Prediction Using Fuzzy-Based Classification Technique

Both developed and developing countries are promoting risk management and refining the ability to alleviate the effects of disaster both man-made and natural, which have become a threat to human life and the world’s economy. The variability in climate change, rapid urbanization and fast-growing socio-economic development has naturally increased the risk associated with flooding. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. Machine learning can improve the risk management. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification approach for pluvial flood risk assessment. Keywords: Machine Learning, Pluvial Flood, Risk, Fuzzy Rule-Based, Prediction DOI: 10.7176/CEIS/12-1-07 Publication date: January 31st 202