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

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.

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Ask Diana: A Keyword-Based Chatbot System for Water-Related Disaster Management

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Emergency Disposal Solution for Control of a Giant Landslide and Dammed Lake in Yangtze River, China

“…In the coming months, the director of the CWRC and his team will assess landslide hazards, reconstruct the damaged monitoring facilities, and revise the safety standards against landslides and dammed lakes. In addition, follow-up studies will pay special attention to predicting rainfall and flood used machine learning techniques [28][29][30], simulating and/or optimizing reservoirs operation based on advanced intelligent algorithms [31,32], and assessing the impacts of dammed lakes and dam collapse on the hydrological conditions [33,34], sediment deposition and erosion [35,36] as well as biodiversity [37] of the Yangtze River. After these studies were accomplished, the director of the CWRC said, "new achievements will contribute to landslide management in other countries or regions of the world".…”