DISCOVER THE WORLD OF disaster risk reduction, preparedness, response, and resilience - especially in the context of climate change
The term disaster brings dramatic pictures to the minds of many people – pictures of destruction, flooded houses and fields, famine, misery. Many people feel that a disaster can strike at any time, quite randomly, or see it as a divine sign. Disasters are indeed common – several hundred disasters that kill people or cause significant damage are recorded every year, affecting some 100-200 million people. However, they are not random, unforeseeable events. Almost everyone on Earth is exposed to potentially dangerous hydrometeorological (windstorms, floods, etc.) or geophysical (earthquakes, landslides, etc.) processes, called hazards. If we don’t understand those processes, prepare for them, plan our cities and infrastructure accordingly, or monitor those processes to generate early warnings, we indeed set ourselves up for disasters.
In this course, you will learn what causes those hazardous processes, how they can be detected, quantified, modelled, and monitored with geoinformation. You will also learn how this knowledge is used to understand risk – the situation that results when a hazard threatens populated areas or our infrastructure – and how that risk can be reduced, or even eliminated. Proper planning not only means giving rivers large floodplains to limit devastating flooding in cities, but also constructing buildings to specific earthquake-resistant standards. It also includes educating decision makers as well as regular citizens about those risks and what can be done about them. Good planning and preparedness, together with measures that allow a community affected by a hazard event to get back on its feet quickly, lead to disaster resilience – the ability to withstand shocks and to recover quickly.
In this course, you can choose whether you want to become an expert disaster risk manager, someone who is proficient in hazard and risk modelling, or someone who loves to work in data analysis, including remote sensing image analysis, machine learning or big data processing. All three experts are needed to make communities safer from hazards.
The Disaster Resilience specialisation offers exciting learning opportunities for students eager to focus on risk reduction, disaster preparedness and response, recovery, and the built-up of resilience, especially in the context of climate change. You will gain invaluable skills and knowledge tailored for various disaster resilience-related career opportunities, organised by learning pathways focused on managing, modelling or data analysis related to disaster risk.
What is disaster resilience
Learning pathways
In the Disaster Resilience specialisation, you can choose one of the following two learning pathways: disaster risk (i) managing, (ii) modeling & data analysis.
Learning pathway 1: Resilience Managing
Disaster risk is calculated using models, using different types of geodata. However, knowledge alone of an existing risk is not sufficient to reduce it and prevent or mitigate disasters. Instead, risk assessment often has to be fine-tuned or adjusted to specific locations, for example, a town or city. Then, specialists are needed to develop and execute strategies to reduce the risk. This can be done by reducing the hazard, such as giving rivers more spillover space before the water reaches the populated area. Or we can work on reducing the so-called vulnerability of people or structures, such as through better awareness-raising, or implementation of suitable building codes in areas susceptible to earthquakes. Finally, early warning and adequate action before an impending event such as a tropical storm arrives allows evacuation of people and securing of property. An increasing number of cities are employing dedicated resilience officers, such a those in the 100 Resilient Cities initiative (funded by the Rockefeller Foundation). Since much of risk management relates to appropriate urban planning, this learning pathway includes courses taught in Urban and Land Futures.
Learning pathway 2: Resilience Modelling & Data Analysis
The resilience managers require appropriate risk information. This is generated by experts in modelling who understand the different forms of quantitative risk modelling, or by advanced data analysis experts trained in machine learning or Big Data analysis. In this course, we focus on modelling approaches using statistical information from past events, such as earthquakes or landslides, or on methods that make use of physical information, such as the specific geotechnical and topographic properties of areas susceptible to landslides. The Resilience Modelling expert thus becomes proficient in the use, adaptation and further development of different types of models, including the relevant statistical data and machine learning methods that apply. The data analysis direction will emphasise 3D modelling with photogrammetry and computer science approaches, machine learning, and semantic scene analysis, as well as relevant statistical and programming elements. This is suitable for students who want to work in the disaster resilience domain, but who also have a strong quantitative interest, and who are interested in a career in academia, a space agency or a research institute. Students who wish to specialise in advanced data analysis will follow several courses in the GeoAI specialisation.
What will you learn?
As a graduate of the Master's in Geo-Information Science and Earth Observation with a specialisation in Disaster Resilience, you will have acquired different types of knowledge, but also specific skills and values.
Video Disaster Resilience explained
In this video, course coordinator Norman Kerle explains the Disaster Resilience specialisation within the Master's in Geo-information Science and Earth Observation.
Other master's and specialisations
Is this specialisation not exactly what you are looking for? Maybe one of the other specialisations suits you better. You can also find out more about related Master’s at the University of Twente:
- GeoAI specialisation
- Urban and Land Futures specialisation
- Resource security specialisation
- Master’s in Spatial Engineering
- Master’s in Sustainable Energy Technology