With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

  • A rapid nowcasting prediction method of urban flood based on data-driven and real-time monitoring.

  • The deduction model accurately estimates the global water depth.

  • The proposed urban flooding nowcasting model was observed to outperform the traditional machine learning model to predict.

With the impact of global climate change, extreme weather events are becoming more and more frequent (Güneralp et al. 2015; Li & Willems 2020). Meanwhile, the increase in urban impervious surface area due to rapid global urbanization will lead to greater flood risk, especially in developing countries (Ding et al. 2022; Wang et al. 2022). Rapidly predicting urban flooding is a critical part of providing decision-makers with enough time to take action and thus minimize damage (Hou et al. 2021; Yan et al. 2021).

To better predict the flooding scenario, a hydraulic model based on physical methods is used extensively for simulating the urban flood (Chang et al. 2021). The most commonly used software, such as InfoWorks ICM (Innovyze 2019), LISFLOOD-FP (Bates & De Roo 2000) and HEC-RAS (de Arruda Gomes et al. 2021), is based on the shallow water equations (SWEs) and its simplified form (Guidolin et al. 2016). However, solving SWEs at high spatial resolution is very complex and requires significant computational costs (Zhao et al. 2020; Buttinger-Kreuzhuber et al. 2022). Therefore, different prediction methods have been proposed for rapid and accurate urban flood prediction.

Machine learning methods have emerged in recent years as a surrogate model for urban flooding prediction (Mosavi et al. 2018). It provides an efficient and accurate prediction approach that does not need to consider complex physical processes such as nonlinear fluid motion (Kabir et al. 2020; Chu et al. 2020). Recently, these methods have been extensively used in the prediction of urban flood and risk assessment (Madayala et al. 2022; Youssef et al. 2022; Zahura & Goodall 2022). However, these models are usually based on hydraulic model data or historical data and can only provide static predictions of urban flooding conditions (Wu et al. 2020; Zhou et al. 2021; Zhou 2022). Thus, understanding the dynamics of flooding processes and the associated variable effects on flood zone objects (e.g., buildings) can provide valuable information for better management of flood disasters (Gao et al. 2021).

Moreover, the data-driven method also has shortcomings in the dynamic prediction of urban flooding. Changes in actual conditions will impact the forecast accuracy and make it difficult to develop a dynamic nowcasting prediction (Mancini et al. 2022). Therefore, in order to develop a highly accurate data-driven model, effective model calibration based on real-time monitored data is hence essential (Fattoruso et al. 2015). Nevertheless, the arrangement of monitoring sensors requires a lot of human and material resources, and it is not practical to arrange a large number of them (Banik et al. 2015; Yazdi 2018). Thus, it is quite important to realize the dynamic prediction of urban flooding based on real-time monitoring using a small number of monitoring sensors.

The present work aims to develop a method based on data-driven and real-time monitoring for rapid nowcasting prediction of urban flooding. The main components are as follows: (i) determining the location and number of monitoring sensors, and developing a deduction method for the water level of the whole drainage system based on the monitoring sensors and (ii) proposing a data-driven model and real-time monitoring-based method for rapidly nowcasting prediction of urban flooding.

An urban flooding nowcasting prediction method was developed to enhance the dynamic nowcasting of urban flooding based on a data-driven model and real-time monitoring data. The method consists of three modules, the data sources, real-time water level deduced in the whole system through machine learning (ML) algorithm and urban flooding rapidly nowcasting through a data-driven method.

As shown in Figure 1, the proposed model consists of three modules: data sources (establish the dataset by hydraulic model), real-time monitoring (select monitor sites and deduce urban global water level), and urban flooding prediction.
Figure 1

Flowchart for construction of urban flooding prediction with real-time data (UFP-RD) methods.

Figure 1

Flowchart for construction of urban flooding prediction with real-time data (UFP-RD) methods.

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Data sources

A hydraulic model was constructed using InfoWorks ICM software to simulate the urban drainage system and flooding scenario, which includes the conduit drainage model and the urban surface flood model. The rainfall events with precipitation greater than 25 mm in 24 h were statistically analyzed from historical rainfall data as rainfall events that may lead to urban flooding (Zhou et al. 2019). The selected historical rainfall events were used as the input of the established hydraulic model, and the model simulated 1,200 different scenarios by adjusting the initial level of drainage node, water level of rivers, hydraulic structures and control facilities and soon in this study. Then, the datasets were obtained based on the simulation results of the hydraulic model, which was used for the UFP-RD model construction subsequently.

Real-time monitoring

Optimizing the arrangement of monitoring sites is an important part of real-time monitoring, the essence of which is to obtain as much information of the urban drainage system as possible from a limited number of monitoring sites. Thus, we adopt the Principal Component Analysis (PCA) method to simplify the data dimensions for monitoring site optimization. The water level time series data of drainage nodes and surface nodes in the datasets were used as the input to the PCA algorithm to extract the main eigenvectors, and the dimension reduction results were obtained in this study. The specific theories and calculation process are shown in the Text S1 of Supplementary material. The results of PCA are first ranked by the eigenvalue, and each principal component corresponds to the point of maximum load as the monitoring site. Then, select the top-ranked monitoring sites according to the number of required monitoring points. Because the water levels at each node of the drainage system are not completely isolated but interact with each other, especially the water levels at the neighboring points are strongly correlated due to the hydraulic connections. Thus, the vast majority of the water level information of the drainage system can be obtained from the selected monitoring points by ML modeling techniques.

Subsequently, we adopt ML techniques to quantify the cross-scale correlation in local-global water levels. The eXtreme Gradient Boosting (XGBoost) model, proposed by Chen & Guestrin (2016), is a scalable tree boosting model and is an improved model derived from the Gradient Boosting Decision Tree (GBDT). XGBoost effectively avoids overfitting and accelerates convergence by performing a second-order Taylor expansion on the loss function and adding a regular term. The basic idea of the algorithm is to continuously add trees and perform feature splitting, learning a new function with each added tree, fitting the residuals of the previous round's prediction with each round's prediction, and predicting the sample score based on the characteristics of the sample. XGBoost is selected as the ML algorithm in this study due to its fast, efficient, accurate and fault-tolerant compared with other ML methods, and it has been widely used for the prediction of the urban flood (Liao et al. 2023; Wang et al. 2023).

In this study, the min-max normalization method was used for the data preprocessing to eliminate adverse effects due to odd sample data. Then, the dataset from the hydraulic model is divided into two sub-datasets: one is the training set (1,080 scenarios) and another is the testing set (120 scenarios), corresponding to a total of 90 and 10% of the dataset, respectively. The training set is used for data samples for model fitting, and a gradient descent of the training error is performed during the training process to learn trainable weight parameters. The testing set is used to evaluate the generalization ability of the final model (Ahmed et al. 2019). The k-fold cross-validation method was used for developing the XGBoost model, in which the k value was set as 10. The input of the XGBoost model is the water level of monitoring sites and simulated precipitation events from the train set, and the output of the ML learning model is the water level of urban flooding except for monitoring sites. Moreover, the learning rate of the XGBoost model was 0.1, and the maximum depth and number of trees were 8 and 500, respectively. The specific details were shown in Text S2 of Supplementary material. Finally, the local-global deduction (LGD) model was developed based on the PCA and XGBoost for the real-time water level deduction.

Urban flooding risk prediction

Figure 2 illustrates the schematic of the proposed urban flooding prediction with real-time data (UFP-RD) model used for urban flooding risk prediction. The observed water level values at monitoring sites for the current time are used in the UFP-RD model to predict urban flooding during the prediction step. Different from the commonly used ML models for urban flood prediction, the method in this study was not designed to predict flooding for the entire region, but rather to predict a time series of water levels at the selected monitoring sites, and then the prediction results were as the input of LGD model (Section 2.2) to obtain the urban flooding prediction results. Specifically, another XGBoost model is adopted to develop a time series model of water level at monitoring sites in this study, where the input of the model is accurate precipitation prediction and monitoring water level data for the corresponding time (Fang et al. 2021). Similarly, we used the k-fold cross-validation method was used for developing this XGBoost model, in which the k value was set as 10, the learning rate of the XGBoost model was 0.003, and the maximum depth and number of trees were 8 and 200, respectively. The autoregression training method was used for developing the time series XGBoost model, and the specific details were shown in Text S3 of Supplementary material. The implemented models were run on a personal computer with an AMD Ryzen 7 5700G and 32 GB random access memory, with a NVIDIA RTX 3060Ti 8 GB graphic processing unit (GPU).
Figure 2

The schematic of the UFP-RD model prediction process.

Figure 2

The schematic of the UFP-RD model prediction process.

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Furthermore, the predicted water levels are simply converted to a probabilistic flood risk map. For the drainage system of the conduit component. The risk can be expressed as:
formula
(1)
where is the flood risk of ith manhole in this rainfall event, is the predicted water level of the most unfavorable moment in the th manhole, is the elevation of the bottom in the ith manhole, is the elevation of the ground level in the ith manhole. For the urban ground segment, according to the Code for Design of Outdoor Wastewater Engineering GB50014-2021 (Ministry of Housing and Urban-Rural Development of the People's Republic of China 2021) and Technical Code for Urban Flooding Prevention and Control GB51222-2017 (Ministry of Housing and Urban-Rural Development of the People's Republic of China 2017), a 2 cm depth of water on an asphalt pavement is not considered a flooding event, whereas an average depth of water on a road section great than or equal to 15 cm is considered to be a flooding event. Thus we used 2 and 15 cm as the zoning values for flooding risk calculations, and the risk can be expressed as:
formula
(2)
where is the flood risk of ith ground point in this rainfall event, is the predicted water level of the most unfavorable moment in the ith ground point, is the elevation of the ground level in the ith ground point.

Model evaluation

To evaluate the performance of our proposed model, we compared the UFP-RD with the traditional ML algorithm (XGBoost model) in urban flooding risk prediction. Model performances were evaluated with four indicators: Maximum Error (MAXE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R2). The specific calculation was shown in Equations (3)–(6).
formula
(3)
formula
(4)
formula
(5)
formula
(6)
where is the measured value, is the predicted value, is the average value of , m is the number of samples. The MAXE, MAE and RMSE are all closer to 0 indicating better model results. The R2 value is between 0 and 1, the closer to 1 means the better the fit is (Chu et al. 2020).
Apart from the evaluation indicators, the probability indicators including Cumulative Distribution Function (CDF) Consistency Histogram (CCH) and Consistency Deviation (CD) were used to evaluate the consistency of the two frequency distribution histograms (Chen et al. 2020), which was slightly modified to make the evaluation of CCH more objective in this study. CCH is a histogram used to visualize the differences or associations between two groups. By displaying the distribution of the two groups and their cumulative frequencies at different percentiles, it provides an intuitive understanding of the overall data distribution. However, there is a high degree of subjectivity in the evaluation of CCH, so CD values are proposed to evaluate a CCH more objectively. The figure of CCH is a bar chart consisting of the same number of columns as the corresponding CDF and the value of the th column in the figure was defined as:
formula
(7)
where is the frequency value of the predicted value in the column representing the th CDF, is the frequency value of the measured value in the column representing the th CDF, n is the number of columns in the CDF. When the model works very well, the CDF figure of the predicted value and measured value are exactly the same. At this point, the values of each column in the CCH are uniformly equal to . Due to the strong subjective arbitrariness of CCH, Chen et al. proposed CD to more objectively evaluate CCH (Chen et al. 2020):
formula
(8)
where n is the number of columns in the CCH, is the value of the th column. The CD value is between 0 and 1, and the closer to 0 indicates the better stability of the model.

Study area

J city is located in the Hangjiahu plain, in the north of Zhejiang Province in China. It is one of the commercial centers in the Yangtze River Delta region. J city has a dense network of urban rivers, with an average annual rainfall of up to 1,168 mm. To control urban flooding, sluice gates and pumping stations were constructed in urban low-lying flood-prone areas, and some scattered small urban polders have been separated in the city. The SGT polder was selected as the case study area, as shown in Figure 3. The SGT polder contains 473 manholes and 22,992 surface nodes for a total of 23,465 points. The SGT polder is mainly for commercial and residential land using, and the four main types of land use are 35% for roofs, 28% for urban green areas, 22% for tarmacadam and 10% for brick. The region has an area of approximately m2 with an average altitude of 3.5 m (Yellow Sea elevation datum of China) and is served by a separate sewer system. The drainage system has a design return period of less than one year, and thus SGT is highly prone to urban flooding when the disaster-caused rainstorm comes.
Figure 3

Schematic diagram of the location of the study area.

Figure 3

Schematic diagram of the location of the study area.

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Data collection and hydraulic model calibration

Meteorological data were taken from historical meteorological monitoring data, which was provided by WheatA agro weather big data system. In order to obtain real-time observed data, sensors were installed at the corresponding sites according to the results of the optimal arrangement of monitoring sites. The installation position of these sensors is shown in Figure 3. Three water level meters (HOBO U20L-01) were installed at different locations along the river. Two rain gauges (L99-YL, China) were also installed to record the precipitation event. Finally, six flow-level meters (Isco 2150) were installed at optimized nodes of the drainage system as monitor sites.

The river pump gate operation data was obtained from the management department, and the model was calibrated with these data and actual measurement data from the sensors. In several rainfall events for validation, the R2 of the hydraulic model at each flow meter reached 0.92–0.98. This indicates that the developed hydraulic model possesses acceptable accuracy, and hence it can accurately simulate the hydrodynamic process of the study area.

Real-time monitoring of urban drainage system

The number of principal components and the corresponding total variance explained are shown in Figure 4. The results show that 99.3% of the variance in the water level can be explained by 14 principal components. The percentage of variance explanation indicates the degree to which the total variance of the sample can be explained by the predictor variables (Gewers et al. 2022). This means that the water level in almost all drainage systems in the SGT area can be calculated if we get the water level in the 14 manholes. Meanwhile, it can also be seen from Figure 4 that the increase in the number of principal components was not effective in improving the variance explained when the number of principal components exceeds 14. In other words, it cannot improve the accuracy of the urban global water level. In addition, according to Kaiser's rule, only the principal components with eigenvalues greater than one have great significance in PCA (Gewers et al. 2022). These principal components (eigenvalue >1) are also the same as those 14 principal components described above. The results present the point corresponding to the maximum load of each principal component for selection (Table S1, Supplementary material). For the balance of economy and accuracy, six monitoring sensors (95.3% explanation rate) were installed in the case study area with two as validation (Figure 3).
Figure 4

Variation of eigenvalues (a) and the corresponding explained total variance (b) with different numbers of principal components.

Figure 4

Variation of eigenvalues (a) and the corresponding explained total variance (b) with different numbers of principal components.

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The results for validation of the LGD model and hydraulic model simulation were performed using ten rainfall events of the test set (Table S2, Supplementary material). As demonstrated in Table 1, the average R2 values of the surface node and manhole reach 0.909 and 0.899, respectively. In addition, the average MAE and RMSE values between the two models are relatively low, are 0.101 and 0.144 (urban surface), and 0.124 and 0.162 (conduit system), respectively. These results revealed a strong fit between the LGD model and the hydraulic model, indicating the reliable accuracy of the results deduced by the LGD model. Figure 5 presents the most unfavorable flooding scenario under a disaster-causing rainfall event. Through comparing the results of the LGD model and hydraulic model, it reveals that both of the two models simulate flood extents similarly and are also consistent with the recorded flood spot in the SGT polder. Thus, it shows the reasonableness of the results deduced by the LGD model.
Table 1

Results for validation of the LGD model and hydraulic model

MetricsUrban surface
Conduit system
AverageMaximumMinimumAverageMaximumMinimum
MAXE 0.164 0.239 0.109 0.218 0.373 0.122 
MAE 0.101 0.192 0.051 0.124 0.239 0.064 
RMSE 0.144 0.252 0.074 0.162 0.286 0.088 
R2 0.909 0.952 0.852 0.899 0.945 0.836 
MetricsUrban surface
Conduit system
AverageMaximumMinimumAverageMaximumMinimum
MAXE 0.164 0.239 0.109 0.218 0.373 0.122 
MAE 0.101 0.192 0.051 0.124 0.239 0.064 
RMSE 0.144 0.252 0.074 0.162 0.286 0.088 
R2 0.909 0.952 0.852 0.899 0.945 0.836 
Figure 5

The most unfavorable moment of urban floods: (a) urban surface simulated by LGD model and (b) urban surface simulated by hydraulic model.

Figure 5

The most unfavorable moment of urban floods: (a) urban surface simulated by LGD model and (b) urban surface simulated by hydraulic model.

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Meanwhile, we compared the conduit system water level in a non-disaster-causing rainfall events. The most unfavorable moment of the drainage network is shown in Figure 6. Table 1 shows the average values of MAXE, MAE, and RMSE of the conduit system were 0.218, 0.124 and 0.162, respectively, while R2 is 0.899. The results were similar to the urban surface.
Figure 6

The most unfavorable moment of the drainage network: (a) conduit simulated by LGD model and (b) conduit simulated by hydraulic model.

Figure 6

The most unfavorable moment of the drainage network: (a) conduit simulated by LGD model and (b) conduit simulated by hydraulic model.

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To quantitatively assess the performance of the LGD model and hydraulic model in representing the probability distribution of flooding results, the CDF consistency histogram is provided in Figure 7. On each subplot, the reference dashed line (0.1) of a perfectly flattened histogram. Figure 7 clearly illustrates that the CCH column of the result of the water level in conduit system is high at the 0.3–0.4 quartile compared to the reference line. This indicates that the frequency of water level simulated values at this quantile is high compared to the test values and there is an overestimation. Similarly, there is an underestimation at the 0.5–0.9 quantile. In addition, the CCH column of the values at the urban surface almost coincides with the reference line, indicates that LGD model is more effective in calculating the water level at urban surface. The CD values of conduit system and urban surface are 0.272 and 0.013, respectively. The closer the CD value is to 0, the better the stability of the model. Thus, these results reveal that the urban surface is more stable compared to conduit system when predicting water level, which is also consistent with the results of the RMSE (Table 1).
Figure 7

Cumulative distribution function (CDF) consistency histogram of the LGD model.

Figure 7

Cumulative distribution function (CDF) consistency histogram of the LGD model.

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Urban flooding risk prediction

Figures 8 and 9 depict the predicted results of urban flooding and conduit system risk based on the UFP-RD model and XGBoost model for a disaster-causing rainfall event. The spatial distribution of urban flooding risk points is generally similar for the UFP-RD model and XGBoost model, but the specific flooding risk (water depth) was different, as shown by the zones identified in the yellow boxes. Moreover, there were some similar results were also found in conduit system (Figure 9).
Figure 8

Urban flood risk prediction results: (a) UFP-RD model and (b) XGBoost model.

Figure 8

Urban flood risk prediction results: (a) UFP-RD model and (b) XGBoost model.

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Figure 9

Conduit system risk prediction results: (a) UFP-RD model and (b) XGBoost model.

Figure 9

Conduit system risk prediction results: (a) UFP-RD model and (b) XGBoost model.

Close modal
To further demonstrate the performance of the proposed model in an intuitive way, we compared the predicted results of the UFP-RD model and XGBoost model with the observed water level real data at two monitoring sites that were not used for model training. Figure 10 shows the water level variation of UFP-RD model, XGBoost model, and observed data over 24 h. The results indicate that the UFP-RD model can predict the water level more accurately compared to the XGBoost model in both two monitoring sites. The specific evaluation indicator values are shown in Table 2. In two monitoring sites, the R2 for the XGBoost model was 0.933 and 0.914, whereas the R2 for the UFP-RD model was higher, with 0.973 and 0.962, respectively. In addition, other evaluation indicator values also indicate that the proposed UFP-RD model performs high accuracy during the dynamic prediction process. On the one hand, we simulated the results of different scenarios through the hydraulic model as the dataset to develop the XGBoost model. However, the hydraulic model is only a simplification of the real situation, there are some differences with the real scenario, which is the reason for the difference between the XGBoost model output and the monitoring data. On the other hand, the XGBoost model predicts the flooding situation for the entire area directly based on inputs such as rainfall. It tries to establish a mathematical model that predicts water levels based on these input features. While the XGBoost model can provide predictions by learning complex relationships between the data, it does not utilize water level data from actual monitoring points as inputs, which may limit its accuracy in certain situations. Our proposed UFP-RD mode is more accurate because it combines the information of real monitoring data by learning the mapping relationship between monitoring point data and other point water level data, and obtaining the results by deducting using real monitoring point data.
Table 2

Specific results of the model with real-time monitoring and without real-time monitoring

MetricsUFP-RD
XGBoost
Point 1Point 2Point 1Point 2
MAXE 0.053 0.132 0.127 0.212 
MAE 0.015 0.062 0.062 0.107 
RMSE 0.042 0.081 0.103 0.142 
R2 0.973 0.962 0.933 0.914 
MetricsUFP-RD
XGBoost
Point 1Point 2Point 1Point 2
MAXE 0.053 0.132 0.127 0.212 
MAE 0.015 0.062 0.062 0.107 
RMSE 0.042 0.081 0.103 0.142 
R2 0.973 0.962 0.933 0.914 
Figure 10

Comparison of the UFP-RD model and XGBoost model: (a) point 1 and (b) point 2.

Figure 10

Comparison of the UFP-RD model and XGBoost model: (a) point 1 and (b) point 2.

Close modal

It is clear from the comparison analysis above that this paper's urban surface part lacks a comparison between simulation results and actual flooding depth. This is because monitoring data for this area is difficult to obtain.

The UFP-RD model improved the prediction accuracy compared to the traditional ML model because the method considers adopting the real-time observed data to eliminate the cumulative error at each time step. Moreover, the UFP-RD model retains the advantages of high computational efficiency and short time consumption of traditional ML model, and the total calculation time of 0.29 s (CPU) and 0.25 s (GPU) (Figure S3, Supplementary material). Thus, the proposed model can be well used for nowcasting prediction of urban flooding scenarios.

This study develops a rapid nowcasting prediction method for urban flooding risk based on data-driven and real-time monitoring. Our approach provides an LGD model based on an ML algorithm that enables quantifying the cross-scale relationship in local-global water levels. The following conclusions were obtained from a case study in the SGT polder of J City:

  • (1) Through a case study in SGT Polder of J City. The LGD model can deduce the urban global water level based on a small number of monitoring sites. The average values of MAE and RMSE for the urban surface and conduit system in the LGD model were as low as 0.101 and 0.144, 0.124 and 0.162, respectively. Meanwhile, the LGD model was more stable compared to the conduit system when deducing water levels in urban surface.

  • (2) This study implements a nowcasting prediction method of urban flooding scenarios based on data-driven and real-time monitoring data, which is named the UFP-RD model. The proposed UFP-RD model has higher accuracy than the traditional ML algorithm in predicting the water depth of urban floods, and it retains the advantages of high computational efficiency. It can provide important technical reference for the early warning and control of urban flooding disasters.

This project was supported by the Basic Public Welfare Research Program of Zhejiang Province (ZJWZ24E090002).

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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