Neural Network and Harmonic Analysis for Recovering Missing Extreme Water-Level Data during Hurricanes in Florida

2009 ◽  
Vol 252 ◽  
pp. 417-426 ◽  
Author(s):  
Wenrui Huang ◽  
Sudong Xu
Keyword(s):  
Neural Network ◽  
Harmonic Analysis ◽  
Water Level ◽  
Extreme Water Level ◽  
Level Data ◽  
Water Level Data

Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

2022 ◽  
pp. 1077-1097
Author(s):  
Nguyen Quang Dat ◽  
Ngoc Anh Nguyen Thi ◽  
Vijender Kumar Solanki ◽  
Ngo Le An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Water Level ◽  
Project Work ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Input Variables ◽  
Network Approaches ◽  
Short Time

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

scholarly journals Application of artificial neural network for predicting water levels in Hooghly estuary, India

2020 ◽  
Vol 3 (1) ◽  
pp. 401-415 ◽  
Author(s):  
Kalyan Kumar Bhar ◽  
Susmita Bakshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Tidal Cycle ◽  
Water Levels ◽  
Hooghly Estuary ◽  
Tidal Water ◽  
Level Data ◽  
Water Level Data ◽  
Artificial Neural

Abstract Hydrodynamic models for morphodynamic studies in estuaries require continuous tidal water level data as boundary conditions. However, for the Hooghly estuary in India, measurement of continuous tidal water elevation data at the most downstream point is a very difficult task because of the remote location and the confluence with the deep sea. The tidal water level data at this station are measured for a half tidal cycle which is not useful for hydrodynamic modeling. However, at other upstream stations, tide water level data are measured continuously. Accordingly, in this study, an attempt is made to generate continuous tidal water level data at the remote station, using the data of the neighboring stations as input to an artificial neural network (ANN) model. A three-layered feed-forward backpropagation (FFBP) network with two hidden layers is selected and five different combinations of input vectors are used. Simulated water level data obtained from each model are compared with the observed data graphically as well as by estimating the standard error parameters. The best model suitable for prediction of continuous tidal elevation during any time of the tidal cycle and applicable throughout the year is then identified. It is found that tidal data from the nearest neighboring station are more suitable for training.

Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

2020 ◽  
Vol 10 (3) ◽  
pp. 1-19
Author(s):  
Nguyen Quang Dat ◽  
Ngoc Anh Nguyen Thi ◽  
Vijender Kumar Solanki ◽  
Ngo Le An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Water Level ◽  
Project Work ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Input Variables ◽  
Network Approaches ◽  
Short Time

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

Estimation of Missing Water-Level Data for the Everglades Depth Estimation Network (EDEN)

2009 ◽  
Author(s):  
Paul Conrads ◽  
Matthew D. Petkewich
Keyword(s):  
Water Level ◽  
Depth Estimation ◽  
Level Data ◽  
Water Level Data

Water-level data for wells in and near burial ground 4, Oak Ridge National Laboratory, Tennessee, 1975-1979

1981 ◽  
Author(s):  
D.A. Webster ◽  
J.S. Beatty ◽  
Pamela K. Benjamin ◽  
W.M. Tranum
Keyword(s):  
Water Level ◽  
National Laboratory ◽  
Burial Ground ◽  
Oak Ridge ◽  
Level Data ◽  
Water Level Data ◽  
Oak Ridge National Laboratory

Tsunami height forecast from water-level data

2002 ◽  
Author(s):  
Yong Wei ◽  
Kwok Fai Cheung ◽  
G.D. Curtis ◽  
C. McCreety
Keyword(s):  
Water Level ◽  
Tsunami Height ◽  
Level Data ◽  
Water Level Data
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