Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation : Theory and Practice of Hazard Mitigation / edited by Ravinesh C. Deo, Pijush Samui, Ozgur Kisi, Zaher Mundher Yaseen.

By:

DEO, Revinesh C

Contributor(s):

Material type: Text

TextSeries: Springer Transactions in Civil and Environmental EngineeringPublication details: Singapore: Springer, C2021.Edition: 1st ed. 2021Description: 1 online resource (XX, 469 p. 225 illus., 205 illus. in color.) online resource. 29 cmISBN:

9789811557729
9789811557712
9789811557736
9789811557743

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:

006.3 23 DEO-I 2021 789507

Online resources:

Full text available from Springer Intelligent Technologies and Robotics eBooks 2021 English/International

Contents:

Chapter 1: Drought Index Prediction using Data Intelligent Analytic Models: A Review -- Chapter 2: Bayesian Markov Chain Monte Carlo based copulas: Factoring the Role of Large-scale Climate Indices in Monthly Flood Prediction -- Chapter 3: Gaussian Naive Bayes Classification Algorithm for Drought and Flood Risk Reduction -- Chapter 4: Hydrological Drought Investigation using Streamflow Drought Index -- Chapter 5: Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Vs Random Forest, MLPNN and MLR -- Chapter 6: Evolving Connectionist Systems versus Neuro-Fuzzy System for Estimating Total Dissolved Gas at Forebay and Tailwater of Dams Reservoirs -- Chapter 7: Modulation of Tropical Cyclone Genesis by Madden-Julian Oscillation in the Southern Hemisphere -- Chapter 8: Intelligent Data Analytics for Time-series, Trend Analysis and Drought Indices Comparison -- Chapter 9: Conjunction Model Design for Intermittent Streamflow Forecasts: Extreme Learning Machine with Discrete Wavelet Transform -- Chapter 10: Systematic Integration of Artificial Intelligence Towards Evaluating Response of Materials and Structures in Extreme Conditions. .

In: Springer Nature eBookSummary: 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. .

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Holdings ( 1 )
Title notes ( 2 )

Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode	Item holds
Books	Faculty of CS & IT Library CS & IT Shelf No. 43 New Arrival	Book	006.3 DEO-I 2021 789507 (Browse shelf(Opens below))	C 1	Available		789507

Total holds: 0

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. .