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Open AccessEditorial

Building an Intelligent Hydroinformatics Integration Platform for Regional Flood Inundation Warning Systems

1
Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan
2
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
3
Water Resources Agency, Ministry of Economic Affairs, Taipei 10617, Taiwan
4
Water Resources and Climatic Change Research Centre, National Hydraulic Research Institute of Malaysia, 43300 Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Water 2019, 11(1), 9; https://doi.org/10.3390/w11010009
Received: 30 November 2018 / Revised: 12 December 2018 / Accepted: 19 December 2018 / Published: 21 December 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
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. View Full-Text
Keywords: machine learning; early flood warning systems; hydroinformatics; database; flood forecast; Google Maps machine learning; early flood warning systems; hydroinformatics; database; flood forecast; Google Maps
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MDPI and ACS Style

Chang, L.-C.; Chang, F.-J.; Yang, S.-N.; Kao, I.-F.; Ku, Y.-Y.; Kuo, C.-L.; Amin, I.M.Z.M. Building an Intelligent Hydroinformatics Integration Platform for Regional Flood Inundation Warning Systems. Water 2019, 11, 9.

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