ICT for Disaster Management, Mauritius

Early Warning Systems for Disaster Risk Reduction in SIDS: Knowledge Discovery in Data Mining (KDDM) Perspective

Corlane Barclay, PhD, LLB, PMP

University of Technology, Jamaicaclbarclay@gmail.com

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 2

Presentation Overview

Leveraging data and data repositories for Early Warning Systems and Disaster Management Analytics– Disaster trends and statistics– Vulnerability of SIDS– EWS, benefits, opportunities– KDDM Overview– KDDM Process in EWS– Policy/Practical Implications– Conclusions

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 3

Vulnerable Countries

http://www.iied.org/files/images/ldc_sids.gif

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 4

Natural Disaster Classification

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 5

Global Disaster Trends & Impact

Annual Disaster Statistical Review 2012 Report

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 6

Economic Impact of Disasters

Investments in disaster riskmanagement are increasingly being seen less as acost and more of an opportunity to strengthen resilience,competitivenessandsustainability.

Investments in disaster riskmanagement are increasingly being seen less as acost and more of an opportunity to strengthen resilience,competitivenessandsustainability.

Investments in disaster riskmanagement are increasingly being seen less as acost and more of an opportunity to strengthen resilience,competitivenessandsustainability.

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 7

Agricultural Drought

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 8

Earthquake

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 9

Disaster Risks in SIDS

Small island developing states (SIDS) are relatively more vulnerable due to size, economic outlook and geography

SIDS have the world’s highest relative disaster risk Climate change will magnify disaster risk in SIDS Disasters challenge the economic resilience of SIDS Investments in disaster risk reduction and climate

change adaptation are likely to reap greater benefits in SIDS than in any other country group

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 10

Early Warning Systems

Combinations of tools and processes embedded within institutional structures coordinated by national/international agencies to provide timely and effective information, through identified institutions, that allows individuals/groups exposed to a hazard to take action to avoid or reduce their risk and prepare for effective response.

− knowledge of the risk, − a technical monitoring and warning service, − dissemination of meaningful warnings to at-risk groups, − and public awareness and preparedness to act

Can save lives, reduce risks and damages

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 11

An Example of How EWS works

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 12

State of Early Warning Systems in SIDS

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 13

Opportunities for EWS Data Analytics

Large datasets stored in repositories across different international and national agencies

Missing from the focus at different levels:− Knowledge management systems to facilitate learning

from the data− Data mining systems to facilitate knowledge discovery

from events, connection between disasters, impact, responses, and more

− Integration of improved decision support systems to ascertain the success of the EWS, acceptance use, stakeholders' attitude towards EWS

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 14

Prospects of Data Mining in Disaster Risk Reduction

Data → Information → Knowledge

Data mining is about knowledge discovery, KDDM

Process of analyzing large datasets to reveal trends, patterns, and relationships, which might otherwise have remained undetected

Successful application in multiple domains: education, health, commerce, etc

DATA MINING ALGORITHM TYPE ALGORITHMS/MODELS

UnsupervisedThe goal is not known or specified, and the task is to determine the best groupings

ClusteringAssociation RulesSequence Clustering

SupervisedThe goal is known or specified, and the task is to determine the relationship among the independent variables and the dependent variable

Decision TreesLinear regressionNeural NetworksLogistic RegressionNaïve Bayes

Other 3D MiningText MiningVisualization

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 15

Application of KDDM to Early Warning Systems

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 16

KDDM Process

EWS/Event Data

Environment Understanding

EWS/Event Data Understanding

Data Preparation

Knowledge Discovery & Extraction (KDE)

Model Evaluation

Deployment/Use

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 17

KDDM ProcessEnvironmentUnderstanding

Data Understanding

Data Preparation

Data Mining Evaluation Deployment/Use

Stakeholder consultationsDetermine key stakeholdersMulti agency involvementCommunity impact assessment

Define Data RequirementsVariablesData sourcesAccess Storage

Select Data Select Modeling TechniquesClusterAssociationDecision TreesVisualization

Evaluate ResultsReview results

Plan DeploymentDeployment strategy

Determine environmental objectivesBusinessNatural environmentCommunity

Data CollectionEarly Warning SystemsRelated/ Supporting systems

Integrate Collected Data

Generate Test Design

Approve Model Plan Monitoring, Review & MaintenanceMonitorAssess usageApply lessons

Project FeasibilityResource assessment (limitations, constraints, advantages)Disaster and risk management

Describe Data Clean Data Build ModelDetermine parameter settings

Interpret Results Produce Final Report

Determine Data Mining Goals

Explore Data Construct Data Test Model Determine Next Steps

Review ProjectIdentify lessons, opportunities

Develop DM Project Plan

Verify Data Quality Integrate Data

Format Data

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 18

KDDM Goals and Objectives: Examples

What the types of relationships that exist between the occurrence of floods in Kingston, Jamaica and systems in the Caribbean within the last decade

– How does this compare with another parish

– Another country in the region e.g. Haiti

What are the relationships between the type of disaster responses and the location

What is the general profile of flood systems that:

– Have victims > 100 in the Caribbean

– Occur at different categories/levels of intensity

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 19

KDDM Disaster Management Framework

Determine Requirements

Design Early Warning Systems Collect Data Apply KDDM for EWS Use

Disaster Management ProcessPlan → Prepare → Monitor → Respond → Recover → Learn

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 20

Policy Implications

This research promotes the use of alternative strategy in the management of EWS and disaster management

Promote the effective use/exploitation of data into knowledge

Obtain deeper understanding of root causes, triggers and relationships between different type of disasters to enable greater risk reduction in EWS

Promote expertise in data mining/KDDM application

Invest in secure repositories for collection, storage and dissemination of key data

Support the work, and extend the mandate of regional disaster management agencies

Continue research in related areas while adapting principles from other domains

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 21

Final Comments

Know the value of data, especially in context of leveraging knowledge for risk reduction in EWS

Invest in ICTs to help make this happen

EWS for Disaster Risk Reduction in SIDS A KDDM Perspective 22

KDDM Book Project

KDDM for Economic Development: Applications, Strategies & Techniques

Editors: – Professor Kweku-Muata Osei-Bryson,

Virginia Commonwealth University– Dr. Corlane Barclay, University of

Technology Jamaica Publisher: CRC Press Now accepting full chapter submissions:

kddm4dev@gmail com/clbarclay@gmail c