£128.90

Springer Intelligent Data Analytics for Hazard Mitigation Book

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Description

Advance your understanding of disaster management with this specialized text on intelligent data analytics for decision-support systems. This book explores how machine learning techniques can be applied to monitor, analyze, and forecast hydro-meteorological variables to improve hazard mitigation strategies. By focusing on predictive modeling, the content provides a way to forewarn the possibility of natural hazards through advanced data science. The text brings together expertise from various fields, including meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making. This interdisciplinary approach creates a framework for building potent models used in hazard risk mitigation. Whether you are a researcher or a professional in civil and environmental engineering, this book helps advance the state of knowledge regarding the use of technology in managing natural risks. It serves as a comprehensive guide for those looking to implement machine learning within the context of disaster prevention and response.

Key Features

Explores machine learning applications for monitoring and forecasting hydro-meteorological variables in disaster management.

Utilizes predictive modeling as a primary discipline to help forewarn the possibility of natural hazards.

Provides an interdisciplinary framework featuring contributions from meteorology, hydrology, and engineering experts.

Integrates perspectives from agriculture, economics, and disaster policy-making to build robust risk mitigation models.

Designed to advance technical knowledge regarding decision-support systems in civil and environmental engineering.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
31 July 2021
Listed Since
03 July 2021

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No barcode data available

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