scholarly journals Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul

Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 187
Author(s):  
Yong-Man Won ◽  
Jung-Hwan Lee ◽  
Hyeon-Tae Moon ◽  
Young-Il Moon
Keyword(s):  
Deep Learning ◽  
Flood Risk ◽  
Flood Forecasting ◽  
Flood Damage ◽  
Urban Stream ◽  
Urban Flood ◽  
Model Driven ◽  
Forecasting And Warning ◽  
Rainfall Runoff Model ◽  
Pump Stations

Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R2 value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R2 value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics.

scholarly journals Flood Stage Forecasting at the Gurye-Gyo Station in Sumjin River Using LSTM-Based Deep Learning Models

2021 ◽  
Vol 21 (3) ◽  
pp. 193-201
Author(s):  
Jaewon Jung ◽  
Hyelim Mo ◽  
Junhyeong Lee ◽  
Younghoon Yoo ◽  
Hung Soo Kim
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Warning System ◽  
Flood Forecasting ◽  
Flood Damage ◽  
Support Vector ◽  
Forecasting Models ◽  
Physically Based ◽  
Forecasting And Warning ◽  
Storm Rainfall

Instances of flood damage caused by extreme storm rainfall due to climate change and variability have been showing an increasing trend. Particularly, a flood forecasting and warning system has been recognized as an important nonstructural measure for flood damage reduction, including loss of life. Flood forecasting and warning have been performed by the forecasts of flood discharge and flood stage using the physically based rainfall-runoff models. However, recently, studies involving the application of a machine learning-based flood forecasting models, which addresses the limitations of extant physically based flood stage forecasting models, have been performed. We may require various case studies to determine more accurate methods. Therefore, this study performed the real-time forecasting of the river water level or stage at the Gurye station of the Sumjin river with lead times of 1, 3, and 6 h by applying a long short-term memory (LSTM)-based deep learning model. In addition, the applicability of the LSTM model was evaluated by comparing the results with those from widely used models based on support vector machine and multilayer perceptron. Consequently, we noted that the LSTM model exhibited a relatively better forecasting performance. Therefore, the applicability of the LSTM model should be extensively studied for flood forecasting applications.

scholarly journals Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
Author(s):  
Song ◽  
Park ◽  
Lee ◽  
Park ◽  
Song
Keyword(s):  
Decision Making ◽  
Water Level ◽  
Real Time ◽  
Lead Time ◽  
Flash Flood ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Forecasting And Warning ◽  
Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

scholarly journals Investigation of satellite rainfall-driven rainfall–runoff model using deep learning approaches in two different catchments in India

2021 ◽  
Author(s):  
Pavan Kumar Yeditha ◽  
Maheswaran Rathinasamy ◽  
Sai Sumanth Neelamsetty ◽  
Biswa Bhattacharya ◽  
Ankit Agarwal
Keyword(s):  
Deep Learning ◽  
River Basin ◽  
Flood Forecasting ◽  
Precipitation Data ◽  
Rainfall Runoff ◽  
Data Set ◽  
Satellite Precipitation ◽  
Satellite Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

scholarly journals Review article: A review and critical analysis of the efforts towards urban flood risk management in the Lagos region of Nigeria

2016 ◽  
Vol 16 (2) ◽  
pp. 349-369 ◽  
Author(s):  
U. C. Nkwunonwo ◽  
M. Whitworth ◽  
B. Baily
Keyword(s):  
Risk Management ◽  
Flood Risk ◽  
Risk Mitigation ◽  
Flood Hazard ◽  
Flood Management ◽  
Flood Damage ◽  
Flood Risk Management ◽  
Management Policy ◽  
Urban Flooding ◽  
Urban Flood

Abstract. Urban flooding has been and will continue to be a significant problem for many cities across the developed and developing world. Crucial to the amelioration of the effects of these floods is the need to formulate a sound flood management policy, which is driven by knowledge of the frequency and magnitude of impacts of these floods. Within the area of flood research, attempts are being made to gain a better understanding of the causes, impacts, and pattern of urban flooding. According to the United Nations office for disaster reduction (UNISDR), flood risk is conceptualized on the basis of three integral components which are frequently adopted during flood damage estimation. These components are: probability of flood hazard, the level of exposure, and vulnerabilities of elements at risk. Reducing the severity of each of these components is the objective of flood risk management under the UNISDR guideline and idea of “living with floods”. On the basis of this framework, the present research reviews flood risk within the Lagos area of Nigeria over the period 1968–2012. During this period, floods have caused harm to millions of people physically, emotionally, and economically. Arguably over this period the efforts of stakeholders to address the challenges appear to have been limited by, amongst other things, a lack of reliable data, a lack of awareness amongst the population affected, and a lack of knowledge of flood risk mitigation. It is the aim of this research to assess the current understanding of flood risk and management in Lagos and to offer recommendations towards future guidance.

scholarly journals A comparative flood damage and risk impact assessment of land use changes

2021 ◽  
Author(s):  
Karen Gabriels ◽  
Patrick Willems ◽  
Jos Van Orshoven
Keyword(s):  
Land Use ◽  
Flood Risk ◽  
Risk Mitigation ◽  
Land Use Changes ◽  
Damage Model ◽  
Flood Damage ◽  
Infiltration Capacity ◽  
Forest Patches ◽  
Land Use Types ◽  
Rainfall Runoff Model

Abstract. Sustainable flood risk management encompasses the implementation of nature-based solutions to mitigate flood risk. These measures include the establishment of land use types with a high (e.g. forest patches) or low (e.g. sealed surfaces) water retention and infiltration capacity at strategic locations in the catchment. This paper presents an approach for assessing the relative impact of such land use changes on economic flood damages and associated risk. This spatially explicit approach integrates a reference situation, a flood damage model and a rainfall-runoff model, considering runoff re-infiltration and propagation, to determine relative flood risk mitigation or increment related to the implementation of land use change scenarios. The applicability of the framework is illustrated for a 4800 ha undulating catchment in the region of Flanders, Belgium by assessing afforestation of 187.5 ha (3.9 %), located mainly in the valleys, and sealing of 187.5 ha, situated mainly at higher elevations. These scenarios result in a risk reduction of 57 % (100 856 €) for the afforestation scenario and a risk increment of

scholarly journals Integrated Flood Forecasting and Warning System against Flash Rainfall in the Small-Scaled Urban Stream

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 971
Author(s):  
Jung Hwan Lee ◽  
Gi Moon Yuk ◽  
Hyeon Tae Moon ◽  
Young-Il Moon
Keyword(s):  
Water Level ◽  
Urban Areas ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Lead Times ◽  
Short Term ◽  
Inland River ◽  
Prediction Module ◽  
Forecasting And Warning

The flood forecasting and warning system enable an advanced warning of flash floods and inundation depths for disseminating alarms in urban areas. Therefore, in this study, we developed an integrated flood forecasting and warning system combined inland-river that systematized technology to quantify flood risk and flood forecasting in urban areas. LSTM was used to predict the stream depth in the short-term inundation prediction. Moreover, rainfall prediction by radar data, a rainfall-runoff model combined inland-river by coupled SWMM and HEC-RAS, automatic simplification module of drainage networks, automatic calibration module of SWMM parameter by Dynamically Dimensioned Search (DDS) algorithm, and 2-dimension inundation database were used in very short-term inundation prediction to warn and convey the flood-related data and information to communities. The proposed system presented better forecasting results compared to the Seoul integrated disaster prevention system. It can provide an accurate water level for 30 min to 90 min lead times in the short-term inundation prediction module. And the very short-term inundation prediction module can provide water level across a stream for 10 min to 60 min lead times using forecasting rainfall by radar as well as inundation risk areas. In conclusion, the proposed modules were expected to be useful to support inundation forecasting and warning systems.

scholarly journals Risk Type Analysis of Building on Urban Flood Damage

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2505
Author(s):  
Kiyong Park ◽  
Sang-Hyun Choi ◽  
Insang Yu
Keyword(s):  
Flood Risk ◽  
Urban Areas ◽  
Flood Damage ◽  
Vulnerability Analysis ◽  
Precipitation Pattern ◽  
City Development ◽  
Urban Flood ◽  
Risk Class ◽  
Exposure Analysis ◽  
Building Type

Climate change caused by global warming has resulted in an increase in average temperature and changes in precipitation pattern and intensity. Consequently, this has led to an increase in localized heavy rain which intensifies the uncertainty of the development of urban areas. To minimize flood damage in an urban area, this study aims to analyze the flood risk effect on buildings by ranking the risk of flood damage for each building type and sorting the long-term land use plan and the building type that requires particular consideration. To evaluate the flood risk of each building type, vulnerability analysis and exposure analysis were conducted in five regions of the Ulsan City. The vulnerability analysis includes determination of each building type by using the building elements which are sensitive to flood damage. In terms of the exposure analysis, environmental factors were applied to analyze the flood depth. The mapping based on the results from two analyses provided the basis for classifying the flood risk into five classes (green, yellowish green, yellow, orange, red). The results were provided in the urban spatial form for each building type. This analysis shows that the district near the Taehwa river is the area with the highest risk class buildings (red and orange class buildings). Notably, this area plays a pivotal functional role in administrating the Ulsan City and has a high density of buildings. This phenomenon is explained by city development which is centered around the lowland; however, given the high value of property, the potential risk is proven to be high.

Flood risk modelling based on tangible and intangible urban flood damage quantification

2010 ◽  
Vol 62 (1) ◽  
pp. 189-195 ◽  
Author(s):  
J. A. E. ten Veldhuis ◽  
F. H. L. R. Clemens
Keyword(s):  
Flood Risk ◽  
Urban Areas ◽  
Human Health Risk ◽  
Flood Damage ◽  
Call Centre ◽  
Damage Functions ◽  
Urban Flood ◽  
Risk Modelling ◽  
Traffic Disturbance ◽  
Risk Curves

The usual way to quantify flood damage is by application stage-damage functions. Urban flood incidents in flat areas mostly result in intangible damages like traffic disturbance and inconvenience for pedestrians caused by pools at building entrances, on sidewalks and parking spaces. Stage-damage functions are not well suited to quantify damage for these floods. This paper presents an alternative method to quantify flood damage that uses data from a municipal call centre. The data cover a period of 10 years and contain detailed information on consequences of urban flood incidents. Call data are linked to individual flood incidents and then assigned to specific damage classes. The results are used to draw risk curves for a range of flood incidents of increasing damage severity. Risk curves for aggregated groups of damage classes show that total flood risk related to traffic disturbance is larger than risk of damage to private properties, which in turn is larger than flood risk related to human health. Risk curves for detailed damage classes show how distinctions can be made between flood risks related to many types of occupational use in urban areas. This information can be used to support prioritisation of actions for flood risk reduction. Since call data directly convey how citizens are affected by urban flood incidents, they provide valuable information that complements flood risk analysis based on hydraulic models.

Application of scenario technique in flood risk management

2007 ◽  
Vol 56 (4) ◽  
pp. 87-95 ◽  
Author(s):  
A. Winterscheid
Keyword(s):  
Risk Management ◽  
Flood Risk ◽  
Flood Control ◽  
Flood Damage ◽  
Flood Risk Management ◽  
Policy Framework ◽  
Limited Perspective ◽  
Integrated Flood Risk Management ◽  
Scenario Technique ◽  
Management Cycle

It is now commonly accepted that the management of flood risks has to be fulfilled within an integrated framework. About two decades ago flood risk was managed from a limited perspective predominantly by means of structural measures aimed at flood control. In contrast integrated flood risk management incorporates the complete management cycle consisting of the phases prevention, protection and preparedness. In theory it is a well described concept. In the stage of implementation, however, there is often a lack of support although a consistent policy framework exists. Consequently, the degree of implementation must be rated as inadequate in many cases. In particular this refers to the elements which focus on preparedness and prevention. The study to which this paper refers emphasises the means and potentials of scenario technique to foster the implementation of potentially appropriate measures and new societal arrangements when applied in the framework of integrated flood risk management. A literature review is carried out to reveal the state-of-the-art and the specific problem framework within which scenario technique is generally being applied. Subsequently, it is demonstrated that scenario technique is transferable to a policy making process in flood risk management that is integrated, sustainable and interactive. The study concludes with a recommendation for three applications in which the implementation of measures of flood damage prevention and preparedness is supported by scenario technique.

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