A Robust Early Warning System for Preventing Flash Floods in Mountainous Area in Vietnam

and and property where it passes. on a hydrological and geomorphological concept connected to the river basin, with the principle that flash floods will only occur where there is a high potential risk and when rainfall exceeds the threshold. In the model used to build flash-floods risk maps, the parameters of the basin are analyzed and evaluated and the weight is determined using Thomas Saaty’s analytic hierarchy process (AHP). The flash-floods early-warning software is built using open source programming tools. With the spatial module and online processing, a predicted precipitation of one to six days in advance for iMETOS (AgriMedia— Vietnam) automatic meteorological stations is interpolated and then processed with the potential risk maps (iMETOS is a weather-environment monitoring system comprising a wide range of equipment and an online platform and can be used in various fields such as agriculture, tourism and services). The results determine the locations of flash floods at several risk levels corresponding to the predicted rainfall values at the meteorological stations. The system was constructed and applied to flash floods disaster early warning for Thuan Chau in Son La province when the rainfall exceeded the 150 mm/d threshold. The system initially supported positive decision-making to prevent and minimize damage caused by flash floods.


Purpose of this Work and its Background
Therefore, in addition to urgent measures to overcome immediate consequences, such as support for money, food, medicine and construction materials for people in need, the construction of an early-warning system to mitigate flash flooding is necessary for more sustainable and proactive natural disaster prevention and control measures.
In the Digital Age, many countries have established disaster early-warning systems . However, this study is the first research in Vietnam to integrate rainfall data with statistical weather data, satellite image analysis, an MCA (Multi Criteria Model) model, webGIS, Flash Flood Potential Index (FFPI) and iMETOS data (auto-rainfall station) to analyze and provide flash-flood risk information to residents.
Different from a conventional flood, a flash flood refers to a flood with high velocity, containing many debris and occurring unexpectedly in minor basins with sloping terrain, thus resulting in massive destruction. The development of flash floods is closely linked to the intensity of rainfall, climate conditions, topographic features, human activities and the flood drainage conditions of basins. Flash floods normally appear only a few hours after heavy rain exceeds a threshold level . Unfortunately, the data on flash floods are usually in short supply and not systematic; thus, it is hard to use common methods to make hydrological predictions for flash-flood forecasts or warnings .
The primary theoretical basis of this research is the basin approach . In this approach, the basin is considered to be a relatively closed system consisting of small tributaries, and, when it rains, the parameters of the buffer surface will determine the mode of flow movement and accumulation in basin boundaries. In mountainous areas with large basin slopes, when rainfall exceeds the threshold, the surface flow will accumulate to form a flash flood. Each basin produces a different mechanism of flash-flood formulation. In this research, the basin parameters were analyzed and processed using the multi-criteria analysis (MCA) model, through which the entire basin area was evaluated by pixel and the results map is described in detail, meeting the requirements of the district-level research scale (1:10,000) and commune-level scale (1:5000).
A mountainous district in the northwest of Son La province (3/4 is high mountain- Figure 1), Thuan Chau is located along National

Materials Research
Study Site [17] lowest point is Song Da (200 m). In the rainy season, Thuan Chau suffers a lot of natural disasters, such as landslides or flash floods.
Over the past few years, for many reasons (including climate change and deforestation), flash floods have started to grow in terms of intensity and frequency, causing severe damage to local communities. As such, research on the development of an early-warning system for flash floods at district level has become an imperative, urgent and practical requirement. With this system, information alerts can be transmitted to different people in various ways, such as message boards, SMS and web pages or can be converted to traditional warning signals (speakers, gongs). Accordingly, local people and managers can make appropriate decisions to prevent natural disasters.
In river basins, to develop a map of flash-flood and mud-flood risks, factors such as landslide, maximum rainfall, the cumulative value of surface topography, surface characteristics, soil characteristics, the weathered shell of the surface and the average slope of tributaries are included as the input data for the analytical model, constructed with a detailed level of research .
The general principle of the model is that flash floods will only occur in locations with high potential risks and when rainfall exceeds the flood level. This concept is illustrated in Figure 2. Depending on the research map scale, a number of information layers reviewed will be different. For the 1:10,000 scale for the district Research Methodology Data Used in the Research level, the information layers included in the evaluation are: mean annual rainfall of several years (RM), topographic wetness index (TWI), average slope of tributes (SB), landslide density (LS), geomorphology (GM), soil (S), forest (FR) and river density (RD). The evaluation rating is divided into five levels: Level 1: very low; Level 2: low; Level 3: medium; Level 4: high; and Level 5: very high .
The ratings for each information layer are determined by the expert coefficient and pairwise comparative analysis (analytic hierarchy process, AHP) .
There are nine types of map to be collected from difference sources. This research collected not only statistical data but also real-time information for rainfall amount from the auto-rainfall stations (iMETOS). Statistical data were collected over 33 years and included daily rainfall amount and daily temperature data. Most of these data were collected from the Ministry of Agriculture and Development and the Vietnam Ministry of Natural Resources and Environment, as shown in Table 1. This model integrates the hydrological and basin geomorphology models assisted by GIS technology . Applying this method, the research focuses on identifying the factors that cause a flash flood in the basin, including soil properties, vegetation cover, basin slope, river density and cumulative flow, and, in comparison with statistics, is used to classify the potentiality of flash-flood generation of each information layer. Geographic information system is used for weighing and integrating spatial elements with the ratings and flow at each pixel of the raster-form map. This method is qualitative and subjective in nature but is suitable for the conditions of small and mediumsized river basins, where there is a lack of meteorological and hydrological stations . [20] [21]

Formulation of Space Model in Early Flash Flood Warning
The threshold for flash-flood generation in serving early warnings is very important; however, it is hard to determine in an accurate manner . Thresholds of flash-flood generation vary widely, ranging from 100 mm/h to 220 mm/d, depending on the basin surface. Most flash-flood basins have an average slope of more than 30% and forest cover of less than 10% of the basin's surface . where Fr is the early warning map of flash-flood risks; f is the maximum rainfall forecast; and p is the potential flood risk map (FFPI is the flash flood potential index).

Researchers of flash floods in
In reference to some published research , it is possible to evaluate the layers of basin surface associated with flash-flood risks in the following tables (Tables 2 and 3).  The results of the evaluation of information layers are shown in Figure 3. The AHP method was adopted to calculate the ratings for each layer . The results are shown in Table 4. The CR reliability of the matrix is determined by the CR common index. If the value of the CR index is less than or equal to 0.1, then the consistency between the factors in the comparison matrix is guaranteed. For the eight factors included in this research, CR = CI/RI = 0.08/1.41 = 0.056 (an index value <0.1 indicates that reliability is warranted). The method for calculating the indicators is given in the documents of Saaty .

Formulation of the Thuan Chau Flash Flood Risk Map
The potential flash flood risk map is formulated by the following function [4,21,23]: where FFPI is the potential flash flood risk, Wi refers to ratings of the factor (i), Xi refers to the factor (i) and n is the number of factors (1 to n-information layers mentioned in Section 3.3).
The result obtained is the flash-flood risk map with different numerical values (Figure 4). In its original form, it has not yet featured an early warning map of a flash-flood risk.  signal, the Statistical Product and Services Solutions (SPSS) software can analyze and draw a signal acquisition characteristic curve (ROC) to determine the reliability of the system. Each point on the curve is the coordinates corresponding to the actual signal frequency on the vertical axis and the theoretical signal frequency on the horizontal axis ( Figure 6).   (Figure 6), the ROC curve in Figure 5 for AUC = 0.86 indicates that the accuracy is relatively good.
At the present, there are many traditional meteorology stations to measure accumulative rainfall for the province. Additionally, AgriMedia (Hanoi, Vietnam) have set up more than 100 iMETOS stations, of which three iMETOS stations have been chosen for analyzing and setting the threshold. Therefore, we integrated these between auto-rainfall stations and traditional meteorology stations

Formulation of the Flash Flood Warning Map for Thuan Chau District
for analyzing.
The solar-powered automatic weather station system developed for model 2 consists of three iMETOS stations which are able to connect online in a "two-way" manner with the Meteoblue Global Meteorological Center of Switzerland. By applying Equation (3) Figure 7A and Figure 7B show the results of flash-flood forecast processing by rainfall in the province. In locations where rainfall exceeds the threshold, flash floods will occur (Figure 7c). The rainfall forecast information processing for each iMETOS weather station system and integration with risk maps for flash flood early warning is done by webGIS. Accordingly, information is transferred to the website to provide flash flood warning information to users . The generalized model and key features of the system are shown in Figure 8; this figure shows the completed resolutions for an early warning system for that study site. Input data, such as daily weather data (daily rainfall, daily temperature) and management documents, together with the hardware, have been analyzed, accessed, stored and displayed by disaster-management software. Using webGIS tools, information that is very useful for the decision will be displayed on the user interface. The residents can receive early-warning alerts via SMS message, streaming video and as electronic documents.  The software was developed using the open source programming language Python, Personal Home Page (PHP) and the PostgreSQL/PostGIS database . The software was simply organized using the key functions: Environmental Systems Research Institute (ESRI)-standardized database management, spatial interpolation of rainfall and integration of information layers to formulate a flash flood forecast map and the map was exported in the form of data and metrics. From the software, information is transferred to the website to provide information online and export messages to users. With a shortage of or limited survey data, the application of GIS with the approach of accessing basins facilitates the formulation of a flash flood risk map for mountainous areas.
In natural disaster management, a flash flood warning system can be a single integrated piece of software or can also include software and webpages operating in different phases (Figure 9a, 9b). However, they should meet three basic functions: quick processing of forecast rainfall data, rapid provision and communication about possible locations of disasters for concerned people in message form. The development of an early-warning system should follow both technology and application directions . At present, the model has been tested and aligned to produce forecasts close to reality.
In this study, the basin parameters were processed using the MCA model, through which the entire basin area was evaluated by pixel and the results map is shown in detail, meeting the requirements of the district-level research scale and commune-level scale. The accuracy of theory is 0.86, which indicates that the accuracy is relatively good. The iMETOS automatic meteorological station system and webGIS are useful in decision-making regarding early warning flood alerts.
The system has been adopted for early warnings of flash floods in Thuan Chau district for disaster management in Son La Province.