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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/116131

    Title: Building an intelligent Hydroinformatics Integration platform for a regional flood inundation warning systems
    Authors: Li-Chiu Chang;Fi-John Chang;Shun-Nien Yang;I-Feng Kao;Ying-Yu Ku;Chun-Ling Kuo;Ir. Mohd Zaki bin Mat Amin
    Keywords: machine learning;early flood warning systems;hydroinformatics;database;flood forecast;Google Maps
    Date: 2018-12-21
    Issue Date: 2019-03-30 12:13:27 (UTC+8)
    Publisher: MDPI
    Abstract: 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. The IHIP provides real-time flood-related data, such as rainfall and multi-step-ahead regional flood inundation maps. The interface of Google Maps fused into the IHIP significantly removes the obstacles for users to access this system, helps communities in making better-informed decisions about the occurrence of floods, and alerts communities in advance. The IHIP has been implemented in the Tainan City of Taiwan as the study case. The modular design and adaptive structure of the IHIP could be applied with similar efforts to other cities of interest for assisting the authorities in flood risk management.
    Relation: Water 2019 11(1), p.9
    DOI: 10.3390/w11010009
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

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