*4.2. Impact of Flash Flood Assessment*

The analysis of the disaster area after the occurrence of flash floods can better provide suggestions for regional development, flood prevention, and disaster reduction [33,86]. Table 3 lists four representative studies about the impact of flash flood assessment, which describe the impact of flash floods in terms of vegetation, agricultural products, topographic changes, and land cover.


**Table 3.** For impact of flash floods.

It can be concluded from Table 3 that hydrologic and hydraulic modeling are commonly used methods to study the effects of flash floods. Modern remote sensing technology can already use spaceborne imageries, airborne imageries, and unmanned aerial vehicle (UAV) systems to quickly and accurately map during or after a flood event. Free satellite data (Sentinel-2 images) were used to determine the impact of flash floods on Ras Ghareb city and the Wadi EI-Natryn region in Egypt [22,40]. Landsat-8 and MODIS data were used to describe the impact of flash floods on rice [87,88], Landsat TM data were used to map the extent of coastal floodplain flooding [89], and multispectral lkonos data were applied to a land use/land cover classification [90], all of which are useful for assessing the impact of flash floods. The combination of UAV data and field surveys can be used as observational data in conjunction with hydraulic models, which greatly promotes the understanding of the mechanism of flash floods [91]. Different from other studies, the backpack type MMS has been proven to be used for post-flood surveys and can ideally reproduce the flooding situation in mountainous areas [24].

#### *4.3. Identification of Flash Flood Hazard Areas*

Typically, due to the remote location of the flash flood area and the harsh weather, it is difficult to arrive at the scene to analyze the behavior of mountain torrents. In GIS environments, the most commonly used method involves drawing hazard maps of flash floods using hydrological and hydrodynamic models [30–33]. Table 4 lists five articles that use hydrological models or hydraulic models to map flash flood hazards.


**Table 4.** For identification of flash flood hazard areas.

Hydrological models can be used to predict the spatial ranges, depths, and speeds of flash flood disasters to determine the areas with high flash flood risks [34]. Twodimensional hydrodynamic models are considered to be the most promising model for accurate flash flood mapping [35], but such models usually require large amounts of input data. The AHP and soil conservation service curve number (CN) methods are commonly used methods for drawing flash flood hazard maps. The AHP is used to assign grades and weights and is usually used to assign weights to the causes of mountain torrents in the study of flash flood hazards [1,53,93]. The SCS model is commonly used in distributed hydrological models and research in arid and semiarid regions, which is a method developed by the U.S. Department of Agriculture (USDA) to estimate runoff and peak discharge [94]. According to specific circumstances, the hazard factors of flash floods selected by researchers are not exactly the same, but many hazard factors are recognized as necessary.

#### *4.4. Flash Flood Susceptibility Assessment*

Identifying areas susceptible to flash floods is one of the most effective measures to reduce losses caused by floods and achieve flood management [95,96]. For large-scale flash flood susceptibility analysis, machine learning methods, bivariate statistics, and multicriteria decision-making methods are mainly used [97]. The machine learning method is considered to be the most advanced and first considered method [36]. Table 5 lists four representative studies that use machine learning methods, bivariate statistics, and multicriteria decision-making methods to map susceptibility to flash floods.


**Table 5.** For susceptibility mapping of flash flood.

From Table 5, the conclusion that the flash flood susceptibility mapping technologies rely on various adjustment factors representing the physical characteristics of the study area can be obtained. The choice of conditional factors depends on the scale of the studied area because it is more difficult to obtain data of the same scale or the same resolution. Therefore, if the study area is larger, the number of factors selected may be smaller, which seems reasonable. Researchers should select factors for research based on actual conditions. Of course, using more extensive data and impact factors can more accurately define the flash flood susceptibility of the study area [99,100]. Land use, slope, rainfall, TWI, and distance to the river are the most commonly considered factors. Logistic regression, bivariate statistical analysis, and AHP are the most commonly used methods to calculate factor weights. The combination of AHP and GIS can also define the flash flood susceptibility zones. The machine learning method is considered to be the most advanced and first considered method [101]. The effect of the mixed model is better than that of the single model, as proven by a large number of examples. The K-nearest neighbor (kNN) and K-star (KS) stand-alone models and kNN–AHP and KS–AHP ensemble models were used to define and calculate the FFPI (flash flood potential index) in flash flood susceptibility mapping. The Bayesian belief network (BBN) model was combined with an extreme learning machine (ELM) and back propagation (BP) structure to develop a new ensemble learning model for predicting flash flood susceptibility [102]. This fact has also been emphasized by Wang et al. [3].
