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

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.

Download Full-text

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

Atmosphere ◽

10.3390/atmos11090971 ◽

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.

Download Full-text

Flood Forecasting and Warning System using Real-Time Hydrologic Observed Data from the Jungnang Stream Basin

Journal of Korea Water Resources Association ◽

10.3741/jkwra.2010.43.1.51 ◽

2010 ◽

Vol 43 (1) ◽

pp. 51-65 ◽

Cited By ~ 4

Author(s):

Jong-Tae Lee ◽

Kyung-A Seo ◽

Sung-Chul Hur

Keyword(s):

Real Time ◽

Warning System ◽

Flood Forecasting ◽

Forecasting And Warning

Download Full-text

Development and Testing of a Low-cost Water Level Sensor and a Mobile based Real-time Flood Warning System for the Flash Flood Prone North Eastern part of Bangladesh

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910999200614000455 ◽

2020 ◽

Vol 10 ◽

Author(s):

Nova Ahmed ◽

Md. Sirajul Islam ◽

Sifat Kalam ◽

Farzana Islam ◽

Nabila Chowdhury ◽

...

Keyword(s):

Water Level ◽

Real Time ◽

Flash Flood ◽

Low Cost ◽

Warning System ◽

Outdoor Environment ◽

Flood Warning ◽

North Eastern ◽

Flood Warning System ◽

North Eastern Part

Background: The North-Eastern part of Bangladesh is suffering from flash flood very frequently, causing colossal damage to life and properties, especially the vast croplands. A distributed sensing system can monitor the water level on a continuous basis to warn people near the riverbank beforehand and reduce the damage largely. However, the required communication infrastructure is not available in most of the remote rural areas in a developing country like Bangladesh. Objective: This study intends to develop a low-cost sensor based warning system, customizing to the Bangladesh context. Method: The system utilizes a low-cost ultrasound based sensor device, a lightweight mobile phone based server, low-cost IoT sensing nodes, and a central server for continuous monitoring of river stage data along with the provision of storage and long-term data analytics. Results: A flash flood warning system developed afterward with the sensors, mobile-based server, and appropriate webbased interfaces. The device was tested for some environmental conditions in the lab and deployed it later in the outdoor conditions for short-term periods. Conclusion: Overall, the warning system performed well in the lab as well as the outdoor environment, with the ability to detect water level at reasonable accuracy and transmit data to the server in real time. Some minor shortcomings still noted with the scope for improvements, which are in the way to improve further.

Download Full-text

<i>Anticipatory defence for agricultural drought forecasting and warning system using a real time water-level</i>

10.13031/aim.201900965 ◽

2019 ◽

Author(s):

Jong-Won Do ◽

Bo-Sung Koh ◽

Kwang-Ya Lee ◽

Tae-Hyun Ha ◽

Seung-Yong Lee ◽

...

Keyword(s):

Water Level ◽

Real Time ◽

Warning System ◽

Agricultural Drought ◽

Drought Forecasting ◽

Forecasting And Warning

Download Full-text

Application of self-organising maps and multi-layer perceptron-artificial neural networks for streamflow and water level forecasting in data-poor catchments: the case of the Lower Shire floodplain, Malawi

Hydrology Research ◽

10.2166/nh.2014.168 ◽

2014 ◽

Vol 45 (6) ◽

pp. 838-854 ◽

Cited By ~ 11

Author(s):

F. D. Mwale ◽

A. J. Adeloye ◽

R. Rustum

Keyword(s):

Neural Networks ◽

Developing Countries ◽

Artificial Neural Networks ◽

Water Level ◽

Lead Time ◽

Flood Forecasting ◽

Multi Layer Perceptron ◽

Raw Data ◽

Artificial Neural ◽

Forecasting And Warning

With a paradigm shift from flood protection to flood risk management that emphasises learning to live with the floods, flood forecasting and warning have received more attention in recent times. However, for developing countries, the lack of adequate and good quality data to support traditional hydrological modelling for flood forecasting and warning poses a big challenge. While there has been increasing attention worldwide towards data-driven models, their application in developing countries has been limited. A combination of self-organising maps (SOM) and multi-layer perceptron artificial neural networks (MLP-ANN) is applied to the Lower Shire floodplain of Malawi for flow and water level forecasting. The SOM was used to extract features from the raw data, which then formed the basis of infilling the gap-riddled data to provide more complete and much longer records that enhanced predictions. The MLP-ANN was used for the forecasting, using alternately the SOM features and the infilled raw data. Very satisfactory forecasts were obtained with the latter for up to 2-day lead time, with both the Nash–Sutcliffe index and coefficient of correlation being in excess of 0.9. When SOM features were used, however, the lead time for very satisfactory forecasts increased to 5 days.

Download Full-text