Author(s)

Zhifeng Wang

Corresponding Author

Zhifeng Wang

Abstract

In recent years, extreme rainfall has occurred frequently, and flood disasters have become increasingly severe, posing new challenges to the flood control and disaster reduction system of high-density cities in northern China. The traditional early warning and emergency response mechanisms are no longer able to cope with the complex and ever-changing urban waterlogging risks. It is urgent to integrate new generation information technologies such as big data and artificial intelligence (AI) to build a smart flood control decision-making system with "full information integration, full process control, and full social participation". This article focuses on the practical problems of high-density urban areas with dense buildings, concentrated population, multiple causes of floods, and diverse and heterogeneous data sources. It innovatively proposes a multi-source heterogeneous data management scheme for flood control and drainage based on the "data lake" architecture. This architecture effectively integrates multi-source hydrological data, systematically studies data collection, cleaning, standardized storage, and real-time processing processes, breaks through the bottleneck of traditional data silos, and achieves efficient aggregation and intelligent analysis of cross departmental and cross platform data. The research results provide a solid data foundation and decision-making support for the dynamic perception of urban flood risks, accurate early warning and forecasting, and scientific emergency dispatch.

Keywords

Multi source hydrological data, Urban flood control, Drainage system, Dynamic regulation, Big data technology