scholarly journals Conjunction of ANN and threshold based wavelet de-noising approach for forecasting suspended sediment load

2013 ◽  
Vol 3 (1) ◽  
pp. 09-25
Author(s):  
Vahid Nourani ◽  
Aida Yahyavi Rahimi ◽  
Farzad Hassan Nejad
Keyword(s):  
Time Series ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Data Driven ◽  
Suspended Sediment Load ◽  
Potomac River ◽  
Denoising Method ◽  
Raw Data ◽  
Daily Streamflow

Information on suspended sediment load (SSL) is fundamental for numerous water resources management and environmental protection projects. This phenomenon has the inherent complexity due to a large number of vague parameters and existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity turns into a barrier to get accurate prediction by conventional linear methods. On the other hand, the extent of the noise on hydrological data reduces the performance of data-driven models like Artificial Neural Networks (ANNs). Although ANNs could capture the complex nonlinear relationship between input and output parameters, being data-driven method positioned it in a state of need to preprocessed data. In this paper, the application of ANN approach focusing on wavelet- based denoising method for modeling daily streamflow-sediment relationship was proposed. The daily streamflow and SSL data observed at outlet of the Potomac River in USA were used as the case study. Achieving this purpose, Daubechies (db) was used as mother wavelet to decompose both streamflow and sediment time series into detailed and approximation subseries. Decomposition at level ten via db3 and at level eight via db5 were examined for streamflow and SSL time series, respectively. At first, the appropriate input combination with raw data to estimate current SSL was determined and re-imposed to ANN with denoised data.  The comparison of results reveals that in term of determination coefficient, the obtained result by denoised data was improved up to 23.2% with raged to use noisy, raw data and this exhibits that denoised data can be employed productively in ANN-based daily SSL forecasting.

A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia

2021 ◽  
Author(s):  
Marwah Sattar Hanoon ◽  
Alharazi Abdulhadi Abdullatif B ◽  
Ali Najah Ahmed ◽  
Arif Razzaq ◽  
Ahmed H. Birima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Learning Approaches ◽  
Suspended Sediment Load ◽  
River Systems

scholarly journals Prediction of Suspended Sediment Load Using Data-Driven Models

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2060 ◽  
Author(s):  
Adnan ◽  
Liang ◽  
El-Shafie ◽  
Zounemat-Kermani ◽  
Kisi
Keyword(s):  
Suspended Sediment ◽  
Fuzzy Inference System ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Resource Planning ◽  
Multivariate Adaptive Regression Splines ◽  
Suspended Sediment Load ◽  
Inference System ◽  
Daily Streamflow ◽  
Neural Fuzzy

Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China—Guangyuan and Beibei—were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The data period covers 01/04/2007–12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

Numerical modelling of hydro-morphodynamic channel processes at decadal timescales using optimization methods: an application to the Dijle River, Belgium

2021 ◽  
Author(s):  
Sardar Ateeq-Ur-Rehman ◽  
Nils Broothaerts ◽  
Ward Swinnen ◽  
Gert Verstraeten
Keyword(s):  
Time Series ◽  
Boundary Conditions ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Morphological Changes ◽  
Computational Cost ◽  
River Channel ◽  
Suspended Sediment Load ◽  
Rating Curves

<p>Numerical hydro-morphodynamic models can simulate the impact of future changes in climate and land cover on river channel dynamics. Accurate predictions of the hydro-morphological changes within river channels require a realistic representation of controlling factors and boundary conditions (BC), such as the sediment load. This is, in particular, true where simulations are run over longer timescales and when sparse data on sediment load is available. Using sediment rating curves to reconstruct the missing sediment load data can lead to poor estimates of temporal variations in sediment load, and hence, erroneous predictions of channel morphodynamics. Furthermore, when simulating channel morphological changes at longer timescales, this comes at a high computational cost making it impossible to run various scenarios of changing boundary conditions to long river reaches with sufficient spatial detail.  Here, we apply different methods (morphological factors (MFs) and wavelet transform (WT)) to overcome these problems and to arrive at faster and more accurate predictions of long-term morphodynamic simulations.</p><p> </p><p>We modelled river channel bed level changes of the River Dijle (central Belgium) from 1969 to 1999. Detailed cross-sectional surveys every 20 to 25 m along the river axis were collected in 1969, 1999 and 2018. Since 1969, the river has been incised by about 2 m most probably as a response to land-use/land-cover changes and subsequent changes in discharge and sediment load.  Daily discharge and water level measurements are available for the entire period; however, daily suspended sediment load was only collected between 1998 and 2000. Therefore, WTs were coupled with artificial neural networks (WT-ANN) to calculate long-term sediment load BCs (1969-1999) from the short-term collected suspended sediment concentration samples. Sediment load predictions with sediment rating curves only obtain an R<sup>2</sup> of 0.115, whereas WT-ANN predictions of suspended sediment load data show an R<sup>2</sup> of 0.902.</p><p> </p><p>Using MFs the reference hydrograph was condensed with a factor of 10 and 20. WT is a mathematical tool that can convert time-domain signals into time-frequency domain signals by passing through low and high-level filters. Passing sediment load time series through these filters create another synthetic BCs containing the frequential and spatial information with half the original signal's temporal length. Thus we also compare the modelling performance using WT generated synthetic BCs with MFs. Similarly, 36x1 to 36x10 processors of an HPC was used to simulate 16 km river reach containing 3,33,305 mesh nodes (with 1.5 m mesh resolution).  Interestingly, with a significant reduction in computational cost, there was a mild difference (R<sup>2</sup>=0.802 using MFs 10 and R<sup>2</sup>=0.763 using MFs 20) in model performance without using MFs during initial trials. Surprisingly, generating a synthetic time series using WT did not perform well. Therefore, hydrograph compression using MFs is found the best option to reduce the computational cost, significantly. Although the computational time reduced from 30 days to only 3 days using MFs and more precise BCs calibrated model with R<sup>2</sup>=0.70, WT poor performance needs to be still investigated.</p>

scholarly journals Geomorphological factors influencing hysteresis patterns between suspended load and flow rate in Alpine rivers

2018 ◽  
Vol 40 ◽  
pp. 04004
Author(s):  
Clément Misset ◽  
Alain Recking ◽  
Cédric Legout ◽  
Alain Poirel ◽  
Marine Cazilhac
Keyword(s):  
Time Series ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Hysteresis Loops ◽  
Total Solid ◽  
Suspended Load ◽  
Suspended Sediment Transport ◽  
Suspended Sediment Load ◽  
Fine Sediments ◽  
Erosion Processes

Suspended sediment load represents a large part of total solid fluxes transported in most rivers. Thus, for hydropower plan management or for environmental issues, it is crucial to understand how these sediments are produced, stored and transported in a given catchment. Hysteresis loops in discharge-suspended load signals are commonly used to assess sediment sources and production processes but most of the time the shape of this relation is analyzed qualitatively on short time series or for few events. In this study we analyzed quantitatively 10 long time series of suspended sediment load of various alpine catchments. This method allows us to compare events and to assess to which extent fine sediments originate from hillslope erosion processes or from river bed remobilization. We found that watersheds with braided bed morphology are dominated by clockwise loops while those with narrower bed as step-pool morphology are dominated by counter-clockwise hysteresis or have no general trend. These results suggest that storage and remobilization of fine sediments within the bed could play a major role in suspended sediment transport in Alpine streams, especially in large braided rivers.

scholarly journals Effect of Sand Erosion on Turbine Components: A Case Study of Kali Gandaki “A” Hydroelectric Project (144 MW), Nepal

2015 ◽  
Vol 17 ◽  
pp. 24-33 ◽  
Author(s):  
Balendra Chhetry ◽  
Kumar Rana
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Hydroelectric Plant ◽  
Suspended Sediment Load ◽  
Sediment Erosion ◽  
Hydroelectric Project ◽  
Power Stations ◽  
Sand Erosion ◽  
High Sediment

In high sediment laden river projects or silt affected power stations, the frequency of repair and maintenance of underwater parts is comparatively higher which leads to increase the overall forced outages per year for repair The extent of the major maintenance will depend on the operating condition such as suspended sediment load passing through the turbine and how the machine was loaded during the operation. This paper illustrates the analysis of sediments, effect of sand erosion and maintenance of turbine of Kali Gandaki “A” Hydroelectric Plant (144 MW). The paper also describes the repair methods used for different turbine components to minimize the effects induced by sediment erosion. HYDRO Nepal JournalJournal of Water, Energy and EnvironmentIssue: 17, July 2015 

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