Artificial Intelligence and Disaster Management
Dr. Jaziar Radianti
Teknologidagene 2018Trondheim, 31 October 2018
Agenda1. Artificial Intelligence (AI) and Disaster Management2. Research on disaster management at CIEM3. AI and Limitations4. Concluding Remarks
1. AI and Disaster Management
Picture: Pixabay License CC0 Creative Commons
Picture: Pixabay License CC0 Creative Commons
What is Artificial Intelligence?
1950s: “Artificial Intelligence” is “the science and engineering of making intelligent machines”.
Picture: Pixabay License CC0 Creative Commons
McCarthy
AI is the broad concept of machine being able to carry out tasks in a “smart” way.
Source: world Economic forum, 2018. “Harnessing Artificial Intelligence for the Earth”
AI Opportunity for the Environment
Source: World Economic Forum, 2018. “Harnessing Artificial Intelligence for the Earth”
• prediction and forecasting, • early warning system, • resilience infrastructure, • resilience planning
Real-time disaster risk
mapping
Natural catashtropheearly warning
Social media enableddisaster
response A community disaster-
response dataand analytics
platform
Extreme weather eventmodelling and
prediction
Impacts and risk
mitigationanalytics
Smart Agriculture
Automatedflood center
Detectundergroundleaks in water
supply systems
Drones and AI for real-
time monitoring ofriver quality
AI-designedintelligent,
connected and liveable cities
Earth Dashboard?
Picture: Pixabay License CC0 Creative Commons
Common Global First Responders Capability Gaps
(Ihttps://internationalresponderforum.org/)
The ability to :1. know the location of responders and their proximity to risks and hazards in real time (i.e. accurate
geolocation of responders) 2. detect, monitor and analyze passive and active threats and hazards at incident scenes in real time
(e.g. Chemical, Biological, Radiation, Explosive, suspicious behavior, fast moving object).3. identify hazardous agents and contaminants rapidly.4. incorporate information from multiple and non-traditional sources (crowdsourcing, social media) into
incident command operations.
Figures: http://www.bbc.co.uk/science/earth/natural_disasters
AI Research in Disaster Risk Management
Crowdsourcing + machine learning
AI, Big Data, Social Media and Emergency Response
• Satellite• Crowdsourcing• Sensor and IoT• Mobile GPS,• Simulation• Combination of
various data• Unmanned
Aerial Vehicle
2. Research on disaster management at CIEM
• Top priority research centre at University of Agder, established in 2011
• Interdisciplinary/ multidisciplinary• Collaboration between Faculty of
Social Sciences and Faculty of Engineering and Science
• Integrated system for real-time TRACKing and collective intelligence in civilian humanitarian missions (12 partners, 8 countries
• CIEM contributions on AI part :– The threat detection module: detecting threat from social
media feed, messages sent by personnel on the ground and news reports.
– The decision support module: choosing one of the alternative actions/mitigation plans based on the predicted threat.(Named-entity recognition-NER and neural network)
Simulations
Fire Detection and Predition
Facial Expression Data Visualization
Smart Glasses + Deep Learning Resilience
AI and Social Media, Situational Awareness, Cybersecurity
H2020-MSCA-RISE-2018 (Marie Skłodowska-Curie Research and Innovation Staff Exchange)2019-2022
CIEMlab
Summary CIEM Research Areas
• Developing community resilience• Climate change, migration and disaster vulnerability• Supporting the next generation operations centre• Multi-level situational awareness• Decision support for humanitarian logistics• Cybersecurity and critical infrastructures
4. AI and limitations
Source: TRY
Sources of Limitations
• Discriminating algorithms/bias (racial, gender)
• Low transparency• Malevolent use of AI such as
autonomous weapons
Source: Angwin, J, et. al., 2016
5. ConcludingRemarks
Conclusions• To find a way to stay relevant in the face of
AI as we realize that AI improves our capabilities in different areas, including decision making
• Opportunities for AI and disaster management
• AI decisions are only as good as the data that humans feed them (to understand AI’s limitations)
• Encourage research on:– AI algorithm transparency, – Explainable AI, – AI risk analysis in various application landscape,– Ethics and AI ethics algorithm.
Picture: Wikipedia
”Perhaps we should all stop for a moment and focus not only on making our AI
better and more successful but also on the benefit of humanity
(Stephen Hawking at Web Summit, Lisbon, 2017)
Picture: CC0 Creative Commons
Thank you!
Jaziar Radianti ([email protected])
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