scholarly journals A Study on the Improvement of Flood Forecasting Techniques in Urban Areas by Considering Rainfall Intensity and Duration

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1883 ◽  
Author(s):  
Choo ◽  
Jo ◽  
Yun ◽  
Lee
Keyword(s):  
Water Level ◽  
Urban Areas ◽  
Prediction Method ◽  
Flood Forecasting ◽  
Flood Damage ◽  
Rainfall Data ◽  
Runoff Simulation ◽  
Flood Prediction ◽  
Prediction Techniques ◽  
Impervious Areas

Frequent localized torrential rains, excessive population density in urban areas, and increased impervious areas have led to massive flood damage that has been causing overloading of drainage systems (watersheds, reservoirs, drainage pump sites, etc.). Flood concerns are raised around the world in the events of rain. Flood forecasting, a typical nonstructural measure, was developed to help prevent repetitive flood damage. However, it is difficult to apply flood prediction techniques using training processes because training needs to be applied at every usage. Other techniques that use predicted rainfall data are also not appropriate for small watershed, such as single drainage area. Thus, in this paper, a flood prediction method is proposed by improving four criteria (50% water level, 70% water level, 100% water level, and first flooding of water pipes) in an attempt to reduce flooding in urban areas. The four criteria nodes are generated using a rainfall runoff simulation with synthetic rainfall at various durations. When applying real-time rainfall data, these nodes have the advantage of simple application. The improved flood nomograph made in this way is expected to help predict and prepare for rainstorms that can potentially cause flood damage.

Accuracy Validation of Flood Forecasting Method Based on Time Series Analysis Using Observed Water Level and Rainfall Data

2020 ◽  
Author(s):  
Naoki Koyama ◽  
Tadashi Yamada
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Water Level ◽  
First Grade ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Flood Prediction ◽  
Series Analysis ◽  
Level Data ◽  
Water Level Data

<p>The aim of this paper is to verify the accuracy of the real-time flood prediction model, using the time-series analysis. Forecast information of water level is important information that encourages residents to evacuate. Generally, flood forecasting is conducted by using runoff analysis. However, in developing countries, there are not enough hydrological data in a basin. Therefore, this study assumes where poor hydrologic data basin and evaluates it through reproducibility and prediction by using time series analysis which statistical model with the water level data and rainfall data. The model is applied to the one catchment of the upper Tone River basin, one of the first grade river in Japan. This method is possible to reproduce hydrograph, if the observation stations exist several points in the basin. And using the estimated parameters from past flood events, we can apply this method to predict the water level until the flood concentration time which the reference point and observation station. And until this time, the peak water level can be predicted with the accuracy of several 10cm. Prediction can be performed using only water level data, but by adding rainfall data, prediction can be performed for a longer time.</p>

scholarly journals Evaluation of the Uncertainty of Flash Flood Prediction Using the RRI Model in Mountainous Rivers

10.29007/n72w ◽  
2018 ◽  
Author(s):  
Yosuke Nakamura ◽  
Koji Ikeuchi ◽  
Shiori Abe ◽  
Toshio Koike ◽  
Shinji Egashira
Keyword(s):  
Water Level ◽  
Lead Time ◽  
Hydrological Model ◽  
Flash Flood ◽  
Flood Damage ◽  
Water Levels ◽  
Uncertainty Distribution ◽  
Flood Prediction ◽  
The Mean ◽  
Level 2

In recent years, flood damage caused by flash floods in mountainous rivers has been frequently reported in Japan. In order to ensure a sufficient lead time for safe evacuation, it is necessary to predict river water levels in real time utilizing a hydrological model. In this study, we conducted flood prediction using the RRI model and rainfall forecasted for the next 6 hours in the Kagetsu River basin (136.1 km2) in July 2017, evaluated the uncertainty regarding the prediction, and illustrated the results using a box-plot. The evaluation found that the mean error of the forecasted water level was approximately - 0.3 m in the prediction for the initial 3 hours and -0.97 m at the 6th hour. Also, the study investigated the possibility of correcting water levels forecasted by clarifying an uncertainty distribution. As a result, the water level forecasted was found to be underestimated because it was predicted to rise as high as Warning Level 2, while the water level forecasted with bias correction was predicted to reach Warning Level 4. Moreover, the lead time was estimated to prolong by 2 hours. Overall, the study suggested that flood forecasting can be improved by considering the uncertainty involved in prediction.

scholarly journals Scenario Analysis of Sluice Gate Operations for Evaluating Inland Flood Damage

2021 ◽  
Vol 16 (3) ◽  
pp. 429-436
Author(s):  
Hiromichi Muroi ◽  
◽  
Kensuke Mine ◽  
Yoshiki Eguchi
Keyword(s):  
Water Level ◽  
Urban Areas ◽  
High Water ◽  
Flood Damage ◽  
Sluice Gate ◽  
Flood Simulation ◽  
Gate Operation ◽  
Sewerage System ◽  
Actual Damage ◽  
Great Damage

Typhoon Hagibis, which hit Japan directly on October 12, 2019, caused great damage, including the flooding of rivers, across various parts of Japan. The Tama River, which flows north of Kawasaki City, also experienced flooding which exceeded the designed high water level; although it did not cause fluvial flooding, river water flowed into the urban areas through the sewerage system, causing unprecedented inundation damage. This damage was reproduced with the inland flood simulation model. Furthermore, we performed simulations in which the water level, precipitation, and sluice gate operation of the Tama River differed from actual conditions, and compared them with the actual damage. Based on these results, we examined methods for reducing inundation damage, such as improving the operation method of sluice gates, and confirmed their effects.

scholarly journals Flood Discharge Prediction Based on Remote-Sensed Spatiotemporal Features Fusion and Graph Attention

2021 ◽  
Vol 13 (24) ◽  
pp. 5023
Author(s):  
Chen Chen ◽  
Dingbin Luan ◽  
Song Zhao ◽  
Zhan Liao ◽  
Yang Zhou ◽  
...  
Keyword(s):  
Flood Control ◽  
Short Term Memory ◽  
Feature Fusion ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Distributed Model ◽  
Human Beings ◽  
Flood Prediction ◽  
Lumped Model

Floods have brought a great threat to the life and property of human beings. Under the premise of strengthening flood control engineering measures and following the strategic thinking of sustainable development, many achievements have been made in flood forecasting recently. However, due to the complexity of the traditional lumped model and distributed model, the hydrologic parameter calibration process is full of difficulties, leading to a long development cycle of a reasonable hydrologic prediction model. Even for modern data-driven models, the spatial distribution characteristics of the rainfall data are also not fully mined. Based on this situation, this paper abstracts the rainfall data into the graph structure data, uses remote sensing images to extract the elevation information, introduces the graph attention mechanism to extract the spatial characteristics of rainfall, and employs long-term and short-term memory (LSTM) network to fuse the spatial and temporal characteristics for flood prediction. Through well-designed experiments, the forecasting effect of flood peak value and flood arrival time is verified. Furthermore, compared with the LSTM model and BIGRU model without spatial feature extraction, the advantages of spatiotemporal feature fusion are highlighted. The specific performance is that the RMSE (the root means square error) and R2(coefficient of determination) of the GA-RNN model have been significantly improved. Finally, we conduct experiments on the observed ten rainfall events in the history of the target watershed. According to the hydrological prediction specifications, the model can be evaluated as a Class B flood forecasting model.

scholarly journals Overwhelming Flow of Water using Machine Learning Techniques

2019 ◽  
Vol 9 (2S3) ◽  
pp. 593-601
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Water Level ◽  
Flood Forecasting ◽  
Ultrasonic Sensor ◽  
Rainfall Data ◽  
Support Vector ◽  
Artificial Neural Network Ann

Floods are rare and dangerous disaster in minimum duration, which have the most destructive impact within urban and rural areas. This research in flood prediction models contributed to risk reduction, to prevent the loss of human life, and reduce the property of damage in floods. This study implements the automated machine learning models, using the Support Vector Machine (SVM) and Artificial Neural Network (ANN). The rainfall data and various meteorological parameter which include temperature data are used in this study. Concurrent daily records of inflow and discharge are taken into consideration to calculate the water level to quantify the importance of the lake flow. It aims to discovering accurate and efficient for the flood forecasting model. This paper attempts to forecast flood by modelling water level, temperature and rainfall data in the region of Korattur lake, Chennai, India. In this study, ultrasonic sensor used to capture the measurement of water level to predict from ultrasonic waves and input of same implemented in BPNN and Support Vector Machine (SVM) were used for flood forecasting. The water level flow is deducted in this research using ultrasonic sensor, proves the best efficient models applied for flood forecasting. This study can be used as a predicting the flood by choosing the proper Machine Learning (ML) algorithm such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithm for showing higher accuracy. To get more accurate result of the models, three standard statistical performance evaluation parameters, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination ( ) were used to analyse the performance of the model developed. As a result, the proposed model proves the most efficiency and accuracy for predicting the flood forecasting

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.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals A Study on Urban Inundation Using SWMM in Busan, Korea, Using Existing Dams and Artificial Underground Waterways

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1708
Author(s):  
Yeon-Moon Choo ◽  
Sang-Bo Sim ◽  
Yeon-Woong Choe
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Flood Damage ◽  
Virtual Model ◽  
Sustainable Cities ◽  
Annual Average ◽  
New Methods ◽  
Average Rainfall ◽  
Reduction Methods ◽  
Urbanized Areas

The annual average rainfall in Busan area is increasing, causing frequent flooding of Busan’s Suyeong and Oncheon rivers. Due to the increase in urbanized areas and climate change, it is difficult to reduce flood damage. Therefore, new methods are needed to reduce urban inundation. This study models the effects of three flood reduction methods involving Oncheon River, Suyeong River, and the Hoedong Dam, which is situated on the Suyeong. Using EPA-SWMM, a virtual model of the dam and the rivers was created, then modified with changes to the dam’s height, the installation of a floodgate on the dam, and the creation of an underground waterway to carry excess flow from the Oncheon to the Hoedong Dam. The results of this study show that increasing the height of the dam by 3 m, 4 m, or 6 m led to a 27%, 37%, and 48% reduction in flooding, respectively, on the Suyeong River. It was also found that installing a floodgate of 10 × 4 m, 15 × 4 m, or 20 × 4 min the dam would result in a flood reduction of 2.7% and 2.9%, respectively. Furthermore, the construction of the underground waterway could lead to an expected 25% flood reduction in the Oncheon River. Measures such as these offer the potential to protect the lives and property of citizens in densely populated urban areas and develop sustainable cities and communities. Therefore, the modifications to the dam and the underground waterway proposed in this study are considered to be useful.

scholarly journals Geophysical surveys integrated with rainfall data analysis for the study of soil piping phenomena occurred in a densely urbanized area in eastern Sicily

2021 ◽  
Author(s):  
Graziano Patti ◽  
Sabrina Grassi ◽  
Gabriele Morreale ◽  
Mauro Corrao ◽  
Sebastiano Imposa
Keyword(s):  
Data Analysis ◽  
Land Use Planning ◽  
Urban Areas ◽  
Rainfall Data ◽  
Soil Subsidence ◽  
Hydrogeological Characteristics ◽  
Geophysical Surveys ◽  
Soil Piping ◽  
Definition Of ◽  
Surface Collapse

AbstractThe occurrence of strong and abrupt rainfall, together with a wrong land use planning and an uncontrolled urban development, can constitute a risk for infrastructure and population. The water flow in the subsoil, under certain conditions, may cause underground cavities formation. This phenomena known as soil piping can evolve and generate the surface collapse. It is clear that such phenomena in densely urbanized areas represent an unpredictable and consistent risk factor, which can interfere with social activities. In this study a multidisciplinary approach aimed to obtain useful information for the mitigation of the risks associated with the occurrence of soil piping phenomena in urban areas has been developed. This approach is aimed at defining the causes of sudden soil subsidence events, as well as the definition of the extension and possible evolution of these instability areas. The information obtained from rainfall data analysis, together with a study of the morphological, geological and hydrogeological characteristics, have allowed us to evaluate the causes that have led to the formation of soil pipes. Furthermore, performance of 3D electrical resistivity surveys in the area affected by the instability have allowed us to estimate their extension in the subsoil and identifying the presence of further areas susceptible to instability.

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