Intelligent data analytics for decision-support systems in hazard mitigation : theory and practice of hazard mitigation / Ravinesh C. Deo, Pijush Samui, Ozgur Kisi, Zaher Mundher Yaseen, editors

Contributor(s): Material type: TextTextSeries: Springer transactions in civil and environmental engineeringPublisher: Singapore : Springer, ©2021Description: xx, 469 pages : 24 cmContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789811557712 (hardback)
Subject(s): Genre/Form: Additional physical formats: Print version:: Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation : Theory and Practice of Hazard MitigationDDC classification:
  • 363.346 23
Online resources:
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
Summary: 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
Item type Current library Call number Status Notes Date due Barcode Item holds
Books Books Junaid Zaidi Library, COMSATS University Islamabad 2nd Floor 363.346 INT 63327 (Browse shelf(Opens below)) Available Hardback 10001000063327
Total holds: 0

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

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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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