# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 2 Materials and Methods
### Indicator Selection  
Based on the hydrological yearbook, national meteorological data, the statistical yearbook, GIS extracted data, etc., for this study, we screened out 19 urban flood risk evaluation indicators from three perspectives: disaster-causing factors, exposure to the disaster-bearing environment, and vulnerability of the disaster-bearing body. Thus, we constructed the Liaocheng City Flood Risk Assessment Indicator System. See Table 2. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Datatypes** & **Metadata** & **Sources** \\ \hline \multirow{8}{*}{Meteorological and hydrological data} & & Haihe River Basin Hydrological Yearbook, National Meteorological (1950–2020) & Haihe River Basin Hydrological Yearbook, National Meteorological Science Data Centre data (China Terrestrial Climatological Data Daily Value Dataset V3.0) \\ \cline{2-3}  & Flood disaster data & Liaocheng City Water Conservancy Journal (1991–2020) \\ \hline \multirow{4}{*}{Basic geographic data} & District and county administrative boundary data & Ministry of National Natural Resources \\ \cline{2-3}  & Drainage map of Liaocheng City Resources Bureau \\ \cline{2-3}  & Topographic elevation data (12.5 DEM) & Geospatial Data Cloud \\ \cline{2-3}  & NDVI data & NASA MOD13A3 Dataset \\ \hline Socio-economic data & Demographic characteristics, economic statistics, municipal facility planning & Liaocheng City Statistical Yearbook 2020 \\ \hline \end{tabular}
\end{table} 
Table 1: Data sources in the study area. Disaster-causing factors refer to the various factors that may cause casualties, property damage, social unrest, etc., in various environments. Heavy precipitation is one of the main causes of flooding in Liaocheng City. Based on the day-by-day precipitation data collected between 1950 and 2020, the P-III frequency curve was used to fit the line, and the weights were set for each of the four return periods: 10 years, 50 years, 100 years, and 200 years. Seven indicators, namely, maximum 24 h rainfall, maximum 3-day rainfall, flood frequency, average annual precipitation, and precipitation variability of light rainfall, moderate rainfall, and heavy rainfall, were selected as the causative factors. A disaster-prone environment is a natural and human environment that nurtures the occurrence of disasters, such as floods. The natural environment exposed to the influence of disaster-causing factors has an indirect influence on the occurrence of internal floods, and it is this environment that reflects the formation of waterlogging. Among them, changes in the type of subsurface, river and lake systems, and topography are the keys to the risk of an urban flood disaster and its degree. To determine the disaster-prone environment of Liaocheng City, we mainly took into account the hydrological and geomorphological environments as well as four indicators, namely, the river network density, average elevation, average slope, and vegetation coverage. A disaster-bearing body can be characterized by various disaster risk factors, such as the population; people represent the main body affected and damaged by disasters. These factors also include urban roads, agriculture, industry, and other aspects. For this study, eight indicators, including population density, road density, and economic density, were selected to identify disaster-bearing bodies according to the aspects of population, urban roads, economy, agriculture, and healthcare. Among them, changes in population density, road density, and rural population were positively related to the vulnerability of disaster-bearing bodies, while changes in economic density, the average production value