Early Warning Systems & Artificial Intelligence

1. Early Warning Systems &Artificial Intelligence A Presentation By Prof. T.V. Vijay Kumar Professor, School of Computer and Systems Sciences Concurrent Professor, Special Centre for Disaster Research Jawaharlal Nehru University New Delhi-110067

2. A disaster is a serious disruption, occurring over a relatively short time, of the functioning of a community or a society involving widespread human, material, economic or environmental loss and impacts, which exceeds the ability of the affected community or society to cope using its own resources. • Disaster very rarely start with a massive disruption of the system, rather they are rapid processes that propagate the initial effects through a complex system leading to major disruption of the system • Additionally difficulty is in the prediction of consequences of these events, as they are in the context of complex and interdependent social, infrastructure and natural environments. • Disaster management is not only concerned with predicting the course and consequences of these disasters, but mitigating those undesired consequences under time pressure. Disaster

3. Disaster Management Cycle

4. IT can make a strong impact by making unprecedented volumes of data related to disasters available to decision makers. • Processing and analysis of the large volumes of data related to disaster is a challenging task due to the unique characteristic and nature of each disaster. • There is a need to design a system that can monitor and analyze this data in order to disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss. • Pre-disaster - Detect the early warning signs and predict the occurrence ofpotential incidents • Disaster Mitigation and Preparedness • During-disaster - response to, control and process the disaster for the purpose of reducing the negative impact. • Disaster Response Disaster Management

5. Early Warning Systems Early warning systems are designed to disseminate relevant information effectively and efficiently, as alarms or warnings, to communities at risk during or before a disaster so that adequate steps can be taken to minimize the loss and damages associated with disasters.

6. An Example: EWS for Floods

7. Data • Unstructured Textual Data: news articles, incident activity reports, announcements etc. • Structured textual data– situational reports, damage assessment forms etc. • Remote sensing data – numerical measurements (mobile images and video devices) etc. • Spatial data – satellite data, aerial imagery data etc. • Voice and video: radio communication, news broadcast etc. • The challenge is to apply analytical techniques to diverse and heterogeneous data that can be static or dynamic • Static: data collected prior to a disaster event - population, location, emergency plans • Dynamic: real time event data produced during disaster – weather, river states, response unit locations, resource distribution Data Characteristics

8. Challenges -Availability of useful and comprehensible data • Data related to normal state and boundaries (potential hazards and repercussions) available • A system in normal state can be subjected to a disaster in different ways - difficult to express all possible scenarios • Aims • Need to ascertain the anomalies or deviations from the normal state, which is a challenging task • Need to describe and classify data as • relevant or irrelevant • for communication to different agencies for timely response • With the present day disaster management systems, the immediate and accurate decision making more and more relies on the capability of data analysis and processing especially in the face of Big Data. Data Analysis in Disaster Management

9. There is an urgent need to enhance the computational intelligence functionality of disaster management, such as, to develop scalable and real-time algorithms for time-sensitive decisions, to integrate structured, unstructured and semi-structured data, to deal with the imprecise and uncertain information, to extract dynamic patterns and outline the evolution of these patterns, to work in distributed environment, and to present the multi-scale, multi-level and multi-dimensional patterns through visualization approaches.

10. Analytics Required for Disaster Management Descriptive: A set of techniques for reviewing and examining the data set(s) to understand and analyze the data. Diagnostic: A set of techniques for determine what has happened and why Predictive: A set of techniques that analyze current and historical data to determine what is most likely to (not) happen Prescriptive: A set of techniques for computationally developing and analyzing alternatives that can become courses of action – either tactical or strategic – that may discover the unexpected Decisive: A set of techniques for visualizing information and recommending courses of action to facilitate human decision-making when presented with a set of alternatives.

11. Artificial Intelligence (AI) • AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. • Weak AI (simulated thinking) and Strong AI (actual thinking) • Narrow AI (Single task) and General AI (Multiple task) • SuperIntelligence – General and Strong AI • Computational intelligence is a branch of artificial intelligence that refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing • it comprises a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which mathematical or traditional modelling find it infeasible to solve them • It would be used to develop methods and algorithm that learns characteristics and patterns from available data in order to make predictions.

12. Disaster Mitigation (Reduction) • Concerned with reducing the risk of occurrence of the disaster and its possible consequences through proactive measures taken before an emergency or disaster occurs.  • CI can be used to determine the likelihood of incidents occurrence. • CI can help prevention of different threats posed by man-made disasters. • Detecting terrorist threats by analysing using computer networks, social networks, fusion of sensor data for nuclear threat detection, face recognition • CI can be combined with static data to monitor changing conditions and their impact

13. Disaster Preparedness (Readiness) • refers to measures taken to prepare for and reduce the effects of disasters. • The major challenge is evacuation planning, which needs to combine spatial data and capturing evacuee behaviours. • CI can be used to identify potential threats and safe areas. • CI can be used to analyse information sources like public safety websites to determine the queries likely to be asked by public and provide answer to them for public awareness and to suggest the resources required in case the event occurs • CI can estimate changing conditions and develop preparedness plans for them. • CI can be used for anomaly detection • Social network data sources like facebook and twitter can be used to propagate the warnings

14. Disaster Response • Concerned with rescuing from immediate danger and stabilization of the physical and emotional condition of survivors • CI can be used for crowd evacuation by computing the optimal route to emergency facility. • CI can be used for resource location-allocation and route planning of emergency supplies • CI can be used for real time analyses of content and amount of information provided by the wireless devices like mobiles (image and videos) and social networks in order to classify data in terms of user defined categories of information (e.g. needs, damage etc.) • CI can be used to design robots that can be used for search and rescue operations - identify new environments created as a result of a disaster

15. Summary • With the advent of IoTs and advanced technology driven sensor devices, large amount of data is getting generated at a rapid speed. This data needs to be captured and stored by EWS, as it has immense potential and provide enormous opportunities for monitoring and managing both natural and manmade disasters. • The use of artificial intelligence can enable EWS to mine early warning signals from the data, so that proactive and preventive measures for disaster mitigation, preparedness, response and recovery can be planned and timely alerts and warnings are disseminated to the relevant stakeholders.

16. Thank You