A NEW FLOOD PREDICTION METHOD WITH DATA ASSIMILATION TECHNIQUE FOR WATER-LEVEL DATA; DYNAMIC INTERPOLATION AND EXTRAPOLATION METHOD FOR FLOOD PREDICTION

With the aim of accurately predicting river water levels a few hours ahead in the event of a flood, we created a river water level prediction model consisting of a runoff model, a channel model, and data assimilation technique. We also developed a cascade assimilation method that allows us to calculate assimilations of water levels observed at multiple points using particle filters in real-time. As a result of applying the river water level prediction model to Arakawa Basin using the assimilation technique, it was confirmed that reproductive simulations that produce results very similar to the observed results could be achieved, and that we would be able to predict river water levels less affected by the predicted amount of rainfall.

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

10.5194/egusphere-egu2020-12604 ◽

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>

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Flood Potential Prediction System Based on Water Level Image

10.31227/osf.io/pz6jv ◽

2017 ◽

Author(s):

Indra Riyanto ◽

Akhmad Musafa

Keyword(s):

Water Level ◽

Warning System ◽

Target Area ◽

Prediction System ◽

Flood Prediction ◽

Affected Area ◽

Level Data ◽

Water Level Data ◽

Elevation Data ◽

System User

In this research, a Flood Potential Prediction System is designed based on Water level Data obtained by image processing. The system process the output data from water level image into tables and then displayed as flood prediction image with a GIS program. The system processes the data in real time to provide public users the accurate flood area prediction. By using this system, user can predict the area which will be affected when the water level rises. Having this information allows the user to inform the peoples that live near the affected area to evacuate or at least to prepare for the upcoming flood. This kind of early warning system not only will save the life of people who live near the affected area but also save their valuables from the flood disaster. The area is segmented by ground elevation at 0.5 m intervals and water level is recorded at 10 cm intervals. The resulting area obtained from elevation data is considered as the boundary of the maximum extent of the flood. The plot of target area shows that an increase of 50 cm in river level can result in doubled area that possibly flooded, while increase of 1 meter of water level shows that the potential area grow fourfold than in normal condition.

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CNN-BiLSTM water level prediction method with attention mechanism

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012032 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012032

Author(s):

Qingqing Nie ◽

Dingsheng Wan ◽

Rui Wang

Keyword(s):

Time Series ◽

Water Level ◽

Time Series Data ◽

Prediction Method ◽

Attention Mechanism ◽

Series Data ◽

Single Model ◽

Level Data ◽

Water Level Data ◽

Water Level Prediction

Abstract Hydrological time series data is stochastic and complex, and the importance of its historical features is different. A single model is difficult to overcome its own limitations when dealing with hydrological time series prediction problems, and the prediction accuracy of a single model can be further improved. According to the characteristics of hydrological time series data, a CNN-BiLSTM water level prediction method with attention mechanism is proposed. In this paper, CNN extracts the spatial characteristics of water level data and BiLSTM learns the time period characteristics by combining the past and future sequence information, attention mechanism is introduced to focus the salient features in the sequence. Taking the hourly water level data of Pinghe basin in China as experimental basis, experimental result shows that this method is more accuracy than Support Vector Machine (SVM), Temporal Convolutional Neural network (TCN), and Bidirectional Long Short-Term Memory network (BiLSTM) model.

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