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.

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.

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

2021 ◽  
Vol 12 (3) ◽  
pp. 2427-2435
Author(s):  
Adib Mashuri Et.al
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Water Level ◽  
Time Scale ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Daily Time

This study focused on chaotic analysis of water level data in different elevations located in the highland and lowland areas. This research was conducted considering the uncertain water level caused by the river flow from highland to lowland areas. The analysis was conducted using the data collected from the four area stations along Pahang River on different time scales which were hourly and daily time series data. The resulted findings were relevant to be used by the local authorities in water resource management in these areas. Two methods were used for the analysis process which included Cao method and phase space plot. Both methods are based on phase space reconstruction that is referring to reconstruction of one dimensional data (water level data) to d-dimensional phase space in order to determine the dynamics of the system. The combination of parameters  and d is required in phase space reconstruction. Results showed that (i) the combination of phase space reconstruction’s parameters gave a higher value of parameters by using hourly time scale compared to daily time scale for different elevation; (ii) different elevation gave impact on the values of phase space reconstructions’ parameters; (iii) chaotic dynamics existed using Cao method and phase space plot for different elevation and time scale. Hence, water level data with different time scale from different elevation in Pahang River can be used in the development of prediction model based on chaos approach.

scholarly journals Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 998 ◽  
Author(s):  
Md Khan ◽  
Nur Muhammad ◽  
Ahmed El-Shafie
Keyword(s):  
Neural Network ◽  
River Basin ◽  
Water Resource Management ◽  
Correlation Coefficients ◽  
Peninsular Malaysia ◽  
Drought Forecasting ◽  
Level Data ◽  
Water Level Data ◽  
Langat River ◽  
Ann Models

Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management.

scholarly journals Stochastic modeling of Lake Van water level time series with jumps and multiple trends

2013 ◽  
Vol 10 (2) ◽  
pp. 2353-2371 ◽  
Author(s):  
H. Aksoy ◽  
N. E. Unal ◽  
E. Eris ◽  
M. I. Yuce
Keyword(s):  
Time Series ◽  
Water Level ◽  
Eastern Anatolia ◽  
Lake Van ◽  
Data Set ◽  
Lake Water Level ◽  
Trend Model ◽  
Level Data ◽  
Water Level Data ◽  
Surrounding Areas

Abstract. In 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey has risen up about 2 m. Analysis of the hydrometeorological shows that change in the water level is related to the water budget of the lake. In this study, a stochastic model is generated using the measured monthly water level data of the lake. The model is derived after removal of trend and periodicity in the data set. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. For the multiple-trend, the time series is first divided into homogeneous segments by means of SEGMENTER, segmentation software. Four segments are found meaningful practically each fitted with a trend line. Two models considering mono- and multiple-trend time series are developed. The multiple-trend model is found better for planning future development in surrounding areas of the lake.

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 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.

scholarly journals USING TIME SERIES OF SENTINEL-1 IMAGES TO PRODUCE DRY BATHYMETRY OF RIVERS

2020 ◽  
Vol V-3-2020 ◽  
pp. 387-394 ◽  
Author(s):  
P. Maillard
Keyword(s):  
Time Series ◽  
Water Level ◽  
Cross Section ◽  
Cross Sections ◽  
Discharge Data ◽  
River Bank ◽  
Manual Intervention ◽  
Optical Images ◽  
Level Data ◽  
Water Level Data

Abstract. A method is presented to produce river cross section profiles from a time series of Sentinel-1 images paired with water level data. Four programs are presented that generate river width data and cross section profiles from SAR or optical images. The programs generate a river bank and island width database with minimum manual intervention. Water level data from in situ stations are interpolated to match the width data and create elevation points from which the cross section profiles are produced. The method is fully described and tested on the São Francisco River in Brazil. The width data are plotted against discharge data to compare their progression. Over 1700 cross sections were produced and classified by their shape. Potential and limitations are presented.

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