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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.
| Serija: | Springer Transactions in Civil and Environmental Engineering |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2020 |
| Knygos puslapių skaičius: | 492 |
| ISBN-10: | 9811557713 |
| ISBN-13: | 9789811557712 |
| Formatas: | 241 x 160 x 32 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation: Theory and Practice of Hazard Mitigation“
