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Intelligent data analytics for decis...

Deo, Ravinesh C.

Intelligent data analytics for decision-support systems in hazard mitigationtheory and practice of hazard mitigation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent data analytics for decision-support systems in hazard mitigationedited by Ravinesh C. Deo .. [et al.].
Reminder of title:	theory and practice of hazard mitigation /
other author:	Deo, Ravinesh C.
Published:	Singapore :Springer Singapore :2021.
Description:	xx, 469 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Hazard mitigationData processing.
Online resource:	https://doi.org/10.1007/978-981-15-5772-9
ISBN:	9789811557729$q(electronic bk.)

Intelligent data analytics for decision-support systems in hazard mitigationtheory and practice of hazard mitigation /
Intelligent data analytics for decision-support systems in hazard mitigationtheory and practice of hazard mitigation /[electronic resource] :edited by Ravinesh C. Deo .. [et al.]. - Singapore :Springer Singapore :2021. - xx, 469 p. :ill., digital ;24 cm. - Springer transactions in civil and environmental engineering,2363-7633. - Springer transactions in civil and environmental engineering..

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.

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.

ISBN: 9789811557729$q(electronic bk.)

Standard No.: 10.1007/978-981-15-5772-9doiSubjects--Topical Terms:

887217
Hazard mitigation
--Data processing.

LC Class. No.: GB5014 / .I57 2021

Dewey Class. No.: 363.346

Intelligent data analytics for decision-support systems in hazard mitigationtheory and practice of hazard mitigation /
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520 $a 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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