Olive Umubyeyi proposes the best evacuation strategies in the event of natural disasters

The increase in the frequency of natural disasters such as earthquakes, landslides, and floods is becoming a critical problem globally due to their effects on humans and the environment. Olive Umubyeyi, a University of Rwanda staff has conducted a research that proposes best evacuation strategies to avoid casualties, deaths, damages and others risks linked to natural disasters. The research is part of her PhD studies at Lund University-Sweden under the support of the Swedish International Development Cooperation Agency-Sida. The research targets the City of Kigali-Rwanda due to its landscape and intense rainfall leading to landslide and floods, which recently claimed the lives of people and damaged several properties.

According to Olive’s research, the total deaths from natural disasters recorded worldwide between 2006 and 2016 was 1.2 million, twice that from the 1990s. In Rwanda, both landslides and flood disasters in Kigali caused a total of 64 deaths, 7953 injured people, and 280 houses destroyed between 2005 and 2013.

The city of Kigali is rapidly growing in both population and urbanization, with 1,318,000 inhabitants on an area of 370 km2. The city is characterized by steep hills separated by valleys. Due to its landscape and intense rainfall, many areas of the city are prone to floods and landslides. Thus, there is an obvious need to efficiently plan evacuation as a strategy, among others, to handle emergency situations and reduce more disaster risks.

Olive explains that evacuation plans are developed to ensure the safety of affected people by efficiently and quickly moving them away from dangerous places to safe places in order to reduce the loss of life and damage. However, evacuation planning is a complex process, involving many stakeholders and management aspects.

Evacuation planning is tackled as a spatial optimization problem. She put it that the decision-making process for evacuation involves high uncertainty, conflicting objectives, and spatial constraints. Olive study presents a Multi-Objective Artificial Bee Colony (MOABC) algorithm, modified to provide a better solution to the evacuation problem.

The new approach (MOABC) combines random swap and random insertion methods for neighborhood search, the two-point crossover operator, and the Pareto-based method. For evacuation planning, two objective functions are considered to minimize the total traveling distance from an affected area to shelters and to minimize the overload capacity of shelters. The developed model was tested on real data from the city of Kigali, Rwanda.

MOABC for an evacuation model was applied to the selected study area of Kigali, which was chosen due to the high frequency occurrences of flood and landslide hazards. The area is large, highly populated, and has some improperly located settlements. The characteristics of urban areas can increase the complexity of the model. Some of the candidate shelters used in this study cannot accommodate all evacuees from the nearest building blocks, so some may walk a long distance to be evacuated and reach safe areas.

Umubyeyi recommends that future research should look into conceptualizing the model based on the characteristics of the area, including topography and behavior of evacuees, and to include other factors such as traffic, risks along evacuation paths, and socioeconomic conditions.

For more about the research, the link has details https://www.mdpi.com/2220-9964/8/3/110