Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation

1. Front Matter

   Pages i-xx

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2. Drought Index Prediction Using Data Intelligent Analytic Models: A Review

   • Zaher Mundher Yaseen, Shamsuddin Shahid

   Pages 1-27

3. Bayesian Markov Chain Monte Carlo-Based Copulas: Factoring the Role of Large-Scale Climate Indices in Monthly Flood Prediction

   • Thong Nguyen-Huy, Ravinesh C. Deo, Zaher Mundher Yaseen, Ramendra Prasad, Shahbaz Mushtaq

   Pages 29-47

4. Gaussian Naïve Bayes Classification Algorithm for Drought and Flood Risk Reduction

   • Oluwatobi Aiyelokun, Gbenga Ogunsanwo, Akintunde Ojelabi, Oluwole Agbede

   Pages 49-62

5. Hydrological Drought Investigation Using Streamflow Drought Index

   • Anurag Malik, Anil Kumar, Sinan Q. Salih, Zaher Mundher Yaseen

   Pages 63-88

6. Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR

   • Salim Heddam

   Pages 89-107

7. Evolving Connectionist Systems Versus Neuro-Fuzzy System for Estimating Total Dissolved Gas at Forebay and Tailwater of Dams Reservoirs

   • Salim Heddam, Ozgur Kisi

   Pages 109-126

8. Modulation of Tropical Cyclone Genesis by Madden–Julian Oscillation in the Southern Hemisphere

   • Kavina S. Dayal, Bin Wang, Ravinesh C. Deo

   Pages 127-150

9. Intelligent Data Analytics for Time Series, Trend Analysis and Drought Indices Comparison

   • Kavina S. Dayal, Ravinesh C. Deo, Armando A. Apan

   Pages 151-169

10. Conjunction Model Design for Intermittent Streamflow Forecasts: Extreme Learning Machine with Discrete Wavelet Transform

    • Ozgur Kisi, Meysam Alizamir, Jalal Shiri

    Pages 171-181

11. Systematic Integration of Artificial Intelligence Toward Evaluating Response of Materials and Structures in Extreme Conditions

    • M. Z. Naser

    Pages 183-212

12. Machine Learning to Derive Unified Material Models for Steel Under Fire Conditions

    • M. Z. Naser, Huanting Zhou

    Pages 213-225

13. Energy Dissipation in Rough Chute: Experimental Approach Versus Artificial Intelligence Modeling

    • Sungwon Kim, Farzin Salmasi, Mohammad Ali Ghorbani, Vahid Karimi, Anurag Malik, Ercan Kahya

    Pages 227-249

14. Morphological Changes of Floodplain Reach of Jhelum River, India, from 1984 to 2018

    • Thendiyath Roshni, Dar Himayoun, Mohammad Danish Azim

    Pages 251-266

15. Spatial Modeling of Soil Erosion Susceptibility with Support Vector Machine

    • Omid Rahmati, Abolfazl Jaafari

    Pages 267-280

16. Spatial Prediction of Landslide Susceptibility Using Random Forest Algorithm

    • Omid Rahmati, Aiding Kornejady, Ravinesh C. Deo

    Pages 281-292

17. Artificial Neural Networks for Prediction of Steadman Heat Index

    • Bhuwan Chand, Thong Nguyen-Huy, Ravinesh C. Deo

    Pages 293-357

18. Daily Flood Forecasts with Intelligent Data Analytic Models: Multivariate Empirical Mode Decomposition-Based Modeling Methods

    • Ramendra Prasad, Dhrishna Charan, Lionel Joseph, Thong Nguyen-Huy, Ravinesh C. Deo, Sanjay Singh

    Pages 359-381

19. Machine Learning Method in Prediction Streamflow Considering Periodicity Component

    • Rana Muhammad Adnan, Mohammad Zounemat-Kermani, Alban Kuriqi, Ozgur Kisi

    Pages 383-403

20. Empirical Model for the Assessment of Climate Change Impacts on Spatial Pattern of Water Availability in Nigeria

    • Mohammed Sanusi Shiru, Eun-Sung Chung, Shamsuddin Shahid

    Pages 405-427

This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables. Predictive modelling is a consolidated discipline used to forewarn the possibility of natural hazards. In this book, experts from numerical weather forecast, meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making contribute towards an interdisciplinary framework to construct potent models for hazard risk mitigation. The book will help advance the state of knowledge of artificial intelligence in decision systems to aid disaster management and policy-making. This book can be a useful reference for graduate student, academics, practicing scientists and professionals of disaster management, artificial intelligence, and  environmental sciences. 

• disaster risk management
• artificial intelligence
• machine learning algorithms
• environmental sciences
• hydrology and water resources
• climate science
• decision system design
• big data
• modelling

• School of Sciences, University of Southern Queensland, Springfield Central, Australia

  Ravinesh C. Deo

• Department of Civil Engineering, National Institute of Technology Patna, Patna, India

  Pijush Samui

• Department of Civil Engineering, Ilia State University, Tbilisi, Georgia

  Ozgur Kisi

• Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

  Zaher Mundher Yaseen

Professor Ravinesh Deo is currently an Associate Professor, and Associate Editor for Stochastic Environmental Research & Risk Assessment, ASCE Journal Hydrologic Engineering journal (USA), Director for Postgraduate Data Science Programs and a Research Leader in Artificial Intelligence at the University of Southern Queensland, Australia. As Applied Scientist with proven leadership in artificial intelligence, his research aims to develop decision-systems with machine learning, heuristic and metaheuristic algorithms to improve real-life predictive systems, especially using deep learning, convolutional neural network and long- short-term memory network. He was awarded many internationally competitive fellowships including Queensland Government U.S. Smithsonian Fellowship, Australia-India Strategic Fellowship, Australia-China Young Scientist Exchange Award, Japan Society for Promotion of Science Fellowship, Chinese Academy of Science Presidential International Fellowship and the Endeavour Executive Fellowship. He is a member of numerous scientific bodies and has won multiple Publication Excellence Awards in both School and University Categories, Head of Department Research Award, Dean’s Commendation for Postgraduate Research Supervision, BSc Gold Medal for Academic Excellence and he was also the Dux of Fiji in Year 13 examinations. Professor Deo held visiting positions at the United Stations Tropical Research Institute (Panama), Chinese Academy of Sciences, Peking University, Northwest Normal University, University of Tokyo, Kyoto and Kyushu University, University of Alcala Spain, McGill University and National University of Singapore. He has undertaken knowledge exchange research programs in Singapore, Japan, Europe, China, USA and Canada and also secured international standing by researching innovative problems with global researchers. He has published several Books with Springer Nature, Elsevier and IGI Global and achieved more than 185publications, more than 140 Q1 journals, including refereed conferences, Edited Books and book chapters. Professor Deo’s research papers have been cited over 4,000 times that provides a Google Scholar H-Index as 36 and a Field Weighted Citation Index exceeding 3.5.

 

Professor Pijush Samui is currently an Associate Professor at National Institute of Technology, Patna, India. He is an established researcher in the application of Artificial Intelligence (AI) for solving different problems in engineering. He developed a new method for prediction of response of soil during an earthquake. He has given charts for prediction of response of soil during an earthquake and developed equations for prediction of lateral spreading of soil due to earthquake. He developed equations for determination of seismic liquefaction potential of soil based on strain energy and prediction of seismic attenuation. He developed efficient models for prediction of magnitude of reservoir induced earthquake. He has developed models for determination of medical waste generation in hospitals with equations used for practical purpose. The developed models can be used for clean India project. He determined frequency effects on liquefaction by using Shake Table. He has applied AI techniques for determination of bearing capacity and settlement of foundation and equations for determination of bearing capacity and settlement of shallow foundation. He also developed equations for determination of compression index and angle of shearing resistance of soil. he developed equations for prediction of uplift capacity of suction caisson.  He also developed equations for determination of fracture parameters of concrete. His active research activity is evident from his extensive citation of publications in google scholar (total frequency of 1280) with H-Index of 22. Dr Samui has published journal papers, books/book chapters and peer reviewed conference papers that involved co-authors from Australia, India, Korea and several other nations. He is also holding the position of Visiting Professor at Far East Federal University (Russia).

 

Professor Ozgur Kisi is currently a Professor of Engineering at the Ilia Sate University, Georgia. He received his B.Sc. in Civil Engineering from the Cukurova University, Turkey (1997), his M.Sc. in Hydraulics from the Erciyes University, Turkey (1999), and his Ph.D.  from Istanbul Technical University, Turkey (2003). His research fields include developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling, suspended sediment modeling, forecasting and estimating hydrological variables such as precipitation, streamflow, evaporation, evapotranspiration, groundwater, lake level, hydroinformatics and trend analysis. He is an active participant in numerous national research projects and supervisor of several MSc and PhD works. He is serving asan Editorial Board Member of several reputed journals (e.g. Journal of Hydrologic Engineering (ASCE), Arabian Journal of Geosciences, ISH Journal of Hydraulic Engineering, Irrigation & Drainage Systems Engineering and Austin Journal of Irrigation). He has also served as a Guest Editor for Special Issues published in Hydrological Hazards in a Changing Environment-Early Warning, Forecasting, and Impact Assessment (Advances in Meteorology) in 2015. He is also serving as a Reviewer for more than 80 journals indexed in Science Citation Index (SCI) in the fields of hydrology, irrigation, water resources and hydro-informatics (e.g. ASCE Journal of Hydrologic Engineering, ASCE Journal of Irrigation and Drainage Engineering, Water Resources Research, Journal of Hydrology, Hydrological Processes, Hydrology Research, Water Resources Management, Hydrological Sciences Journal, Journal of Hydroinformatics, Water

Science and Technology). He has authored more than 200 research articles, 2 chapters and 24 discussions. He is the recipient of the 2006 International Tison Award, given by the International Association of Hydrological Sciences (IAHS). He is a member of Turkish Academy of Science (selected in 2012).

 

Dr. Yaseen is a Senior Lecturer in Civil Engineering at Ton Duc Thang University, Vietnam with an expertise in machine learning, advanced data analytics and environmental sciences. He has expertise in Hydrology, Water Resources Engineering, Hydro-logical processes modeling, Environmental Engineering and Civil Engineering. He has recently published in top hydrology and water resources journals such as Journal of Hydrology, Water Resources Management and Stochastic Environmental research and Risk Assessment. 

 

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