Abstract. Flooding is one of the most disruptive natural disasters, causing substantial loss of life and property damage. Coastal cities in Asia face floods almost every year due to monsoon influences. Early notification of flooding events enables governments to implement focused preventive actions. Specifically, short-term forecasts can buy time for evacuation and emergency rescue, giving flood victims timely relief. This paper proposes a novel multi-strategy-mode waterlogging-prediction (MSMWP) framework for forecasting waterlogging depth based on time series prediction and a machine learning regression method. The framework integrates historical rainfall and waterlogging depth to predict near-future waterlogging in time under future meteorological circumstances. An expanded rainfall model is proposed to consider the positive correlation of future rainfall with waterlogging. By selecting a suitable prediction strategy, adjusting the optimal model parameters, and then comparing the different algorithms, the optimal configuration of prediction is selected. In the actual-value testing, the selected model has high computational efficiency, and the accuracy of predicting the waterlogging depth after 30 min can reach 86.1 %, which is superior to many data-driven prediction models for waterlogging depth. The framework is useful for accurately predicting the depth of a target point promptly. The prompt dissemination of early warning information is crucial to preventing casualties and property damage.

The complex formation mechanism and numerous influencing factors of urban waterlogging disasters make the identification of their risk an essential matter. This paper proposes a framework for identifying urban waterlogging risk that combines multi-source data fusion with hydrodynamics (MDF-H). The framework consists of a source data layer, a model parameter layer, and a calculation layer. Using multi-source data fusion technology, we processed urban meteorological information, geographic information, and municipal engineering information in a unified computation-oriented manner to form a deep fusion of a globalized multi-data layer. In conjunction with the hydrological analysis results, the irregular sub-catchment regions are divided and utilized as calculating containers for the localized runoff yield and flow concentration. Four categories of source data, meteorological data, topographic data, urban underlying surface data, and municipal and traffic data, with a total of 12 factors, are considered the model input variables to define a real-time and comprehensive runoff coefficient. The computational layer consists of three calculating levels: total study area, sub-catchment, and grid. The surface runoff inter-regional connectivity is realized at all levels of the urban road network when combined with hydrodynamic theory. A two-level drainage capacity assessment model is proposed based on the drainage pipe volume density. The final result is the extent and depth of waterlogging in the study area, and a real-time waterlogging distribution map is formed. It demonstrates a mathematical study and an effective simulation of the horizontal transition of rainfall into the surface runoff in a large-scale urban area. The proposed method was validated by the sudden rainstorm event in Futian District, Shenzhen, on 11 April 2019. The average accuracy for identifying waterlogging depth was greater than 95%. The MDF-H framework has the advantages of precise prediction, rapid calculation speed, and wide applicability to large-scale regions.

Flood disasters have become one of the most threatening natural disasters in the world, in which waterlogging is the most common form in the context of highly urbanized megacities. The formation of flood disaster is related to many factors and involves information from multiple sources, making it difficult be predicted. This paper integrates multi-source information data, classifies the study area into different categories according to hydrological analysis results, and combines hydrodynamic theory and ArcGIS to get the quantitative prediction of the range and depth of waterlogging under different rainfall inputs. The evaluation results provide the government with accurate and timely information of waterlogging risks and locations in order to improve promptness of emergency management such as evacuation and managing traffics.