Flood Forecasting Using Machine Learning Methods
Dublin Core
Title
Flood Forecasting Using Machine Learning Methods
Subject
Machine Learning Methods
Description
Flood disasters have had a great impact on city development. Early flood warning systems (EFWS) are promising countermeasures against flood hazards and losses. Machine learning (ML) is the kernel for building a satisfactory EFWS. This paper first summarizes the ML methods proposed in this special issue for flood forecasts and their significant advantages. Then, it develops an intelligent
hydroinformatics integration platform (IHIP) to derive a user-friendly web interface system through the state-of-the-art machine learning, visualization and system developing techniques for improving
online forecast capability and flood risk management. The holistic framework of the IHIP includes five
layers (data access, data integration, servicer, functional subsystem, and end-user application) and one database for effectively dealing with flood disasters.
hydroinformatics integration platform (IHIP) to derive a user-friendly web interface system through the state-of-the-art machine learning, visualization and system developing techniques for improving
online forecast capability and flood risk management. The holistic framework of the IHIP includes five
layers (data access, data integration, servicer, functional subsystem, and end-user application) and one database for effectively dealing with flood disasters.
Creator
Chang, Fi-John
Source
https://www.mdpi.com/books/pdfview/book/1151
Publisher
MDPI - Multidisciplinary Digital Publishing Institute
Date
2019
Contributor
Baihaqi
Rights
Creative Commons
Format
PDF
Language
English
Type
Textbooks
Files
Collection
Citation
Chang, Fi-John , “Flood Forecasting Using Machine Learning Methods,” Open Educational Resource (OER) - USK Library, accessed April 24, 2025, http://202.4.186.74:8004/oer/items/show/3165.