Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation: Theory and Practice of Hazard Mitigation

477 Pages·2021·21.857 MB·English

by  Ravinesh C. Deo, Pijush Samui, Ozgur Kisi, Zaher Mundher Yaseen

Springer Transactions in Civil
and Environmental Engineering
Ravinesh C. Deo
Pijush Samui
Ozgur Kisi
Zaher Mundher Yaseen   Editors
Intelligent
Data Analytics
for Decision-Support
Systems in Hazard
Mitigation
Theory and Practice of Hazard Mitigation
Springer Transactions in Civil
and Environmental Engineering
Editor-in-Chief
T. G. Sitharam, Indian Institute of Technology Guwahati, Guwahati, Assam, India
Springer Transactions in Civil and Environmental Engineering (STICEE) publishes
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More information about this series at http://www.springer.com/series/13593
Ravinesh C. Deo Pijush Samui
(cid:129) (cid:129)
Ozgur Kisi Zaher Mundher Yaseen
(cid:129)
Editors
Intelligent Data Analytics
for Decision-Support
Systems in Hazard Mitigation
Theory and Practice of Hazard Mitigation
123
Editors
RavineshC. Deo Pijush Samui
Schoolof Sciences Department ofCivil Engineering
University of SouthernQueensland National Institute ofTechnology Patna
SpringfieldCentral, QLD,Australia Patna, Bihar, India
Ozgur Kisi Zaher MundherYaseen
Department ofCivil Engineering Faculty of Civil Engineering
Ilia State University TonDuc Thang University
Tbilisi, Georgia HoChiMinh City,Vietnam
ISSN 2363-7633 ISSN 2363-7641 (electronic)
SpringerTransactions in CivilandEnvironmental Engineering
ISBN978-981-15-5771-2 ISBN978-981-15-5772-9 (eBook)
https://doi.org/10.1007/978-981-15-5772-9
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This work is dedicated to my father Mr. Bisun
Deo (1944–2010) of Labasa, Fiji Islands
A farmer, a community worker and a source
of inspiration for the leading Editor
—Ravinesh C. Deo
Foreword
Globalenvironmentalchangedrivenbyhottertemperatures,unevendistributionof
precipitation,andelevatedgreenhousegasesbringsignificantchallengestohumanity
asawhole—withrisingincidencesandseverityofextremeweatherevents,including
drought, heatwaves, floods, tsunamis, tidal waves, earthquakes and other forms of
naturaldisasters.
Thesocioeconomicdevelopmentandhumanwell-beingdependsonhowpeople
drawonandmanagethenaturalresourcesavailabletothemwiththecurrentphase
ofahighlyfragileandchangingclimatesystem.Themitigationofdisasterimpacts
is a way to save humanity, their survival or well-being. Disaster risk reduction
through mitigation and adaptation is an integral part of social and economic
development, and is essential if development is to be sustainable for the future.
Thiseditedbookisaconsolidatedaccountofthetheoryandpracticeofdisaster
mitigation techniques using artificial intelligence and data analytics methods.
TheworkpresentedthereinsupportstheinitiativesofUnitedNationssustainable
Development Goal Target 11.5: “By 2030, significantly reduce the number of
deaths and the number of people affected and substantially decrease the direct
economic losses relative to global gross domestic product caused by disasters,
includingwater-relateddisasters,withafocusonprotectingthepoorandpeoplein
vulnerable situations”.
The studies presented in this edited book, through expert writers of various
chapters, are aimed at developing measures for adopting and implementing inte-
grated policies and plans towards mitigation and adaptation to climate change,
resilience to disasters, and developing and implementing in line with the Sendai
Framework for Disaster Risk Reduction 2015–2030, a science-based disaster risk
management.
Thechapterspresentedprovideveryrelevantmethodologiesusedbyresearchers
andpolicymakersthatdatascienceofferstosupportpositiveeconomic,socialand
environmental decisions and strengthening national and regional development
planning for disaster risk mitigation.
vii
viii Foreword
It is my hope that this book will serve a wide range of audience from graduate
students, novice researchers, academics, and people working in areas of meteo-
rology, hydrology, climate change and policy development.
April 2020 Dr. Ashok K. Mishra
Dean’sProfessorinCivilEngineering
Clemson University
Clemson, SC, USA
Preface
Data describes many concealed facets of humanity, environment and universe.
Intelligent analytics (big data techniques) explore such data to unveil real truths
about patterns and laws of nature. Natural hazards can be investigated and their
effectmitigatedthroughbigdatatechnique.Hazardstriggercatastrophicsocialand
hydrologicalimbalance,exacerbateclimateextremessuchasbushfires,droughtand
heatwaves and lead to aberration in water supply and its quality. They also hinder
domestic, industrial and agricultural needs that humans require for survival.
Hazards cause detrimental impact on urban and rural infrastructures, flora, fauna
andbiodiversity,bringinghealthandeconomicconsequencestodevelopingandthe
first world nations through their impact on food and water security.
Thebookdescribessomeofthelatestartificialintelligencemachinelearningand
intelligent analytics used to design disaster mitigation systems. Such systems
advance disaster policy and practice and provide end-users prior knowledge on
hazards, their monitoring and forecasting in a natural environment. These tech-
niques use an array of data, on-site measurements, satellite geographic positioning
systems and reanalysis products. Intelligent analytics can thus be used in fore-
warning and monitoring of extremes that support practical decision systems.
The book will increase a reader’s curiosity, offering unending knowledge and
opportunitiestolearnmoreaboutartificialintelligenceonwhichintelligentanalytic
methods are based. It will help readers to successfully learn how to develop
methods that monitor, analyse and forecast hydro-meteorological variables. The
tools can be practically implemented in construction of models for hazard
risk-mitigationdesignedasaframeworkforinexpertpredictivesystems.Generally,
predictive modelling is a consolidated discipline used to forewarn possibility of a
hazard. By fostering strategic decisions, expert system models can be a
cost-effective way to forewarn abnormal events with overarching aim to develop
and improve decision-support system in disaster mitigation.
The book has welcomed contributions from diverse authors including original
research,reviewsanddiscussionsanddebatesinlightoflatestintelligentanalytics.
The chapters are written by experts, dwelling on applications of primitive and
modern day soft computing strategies for disaster forecasting, ideas on
ix
x Preface
decision-supportsystemsfornaturalhazardmitigation.Itillustratesdata-intelligent
approaches that can advance knowledge in hydro-meteorological sciences. The
worksaugmentsmachinelearningwithpre-processingalgorithmsthatcanenhance
decision making, understanding and predictions, and explore interrelationships
between hydro-metrological and natural hazard mitigation systems via data-
intelligent approach. Extensions, applications and case studies advancing evolu-
tionary computing in hazard risk mitigating have also been welcomed.
The book provides description of relevant theory and practical applications. It
haspublishedsomeofthefinestcutting-edgeapplication.Chaptersaredrawnfrom
a consortium of experts in mathematics, computing, weather forecasting, meteo-
rology, hydrology, engineering, agriculture, economics, environmental science,
disaster management and policy-makers and climate advocacies.
It is the our hope that all readers will benefit significantly in learning about the
state-of-the-art machine learning models, decision support systems, including
disaster management science and policy perspectives.
Happy reading and learning!
Springfield Central, Australia Dr. Ravinesh C. Deo
January 2020