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.

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 November 29, 2023, http://uilis.usk.ac.id/oer/items/show/3165.

Document Viewer