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 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.

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 

Suspended sediment load prediction of river systems: GEP approach

2012 ◽  
Vol 6 (9) ◽  
pp. 3469-3480 ◽  
Author(s):  
H. Md. Azamathulla ◽  
Yong Chong Cuan ◽  
Aminuddin Ab. Ghani ◽  
Chun Kiat Chang
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Load Prediction ◽  
River Systems
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