A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States

2015 ◽  
Vol 187 (4) ◽  
Author(s):  
Ehsan Olyaie ◽  
Hossein Banejad ◽  
Kwok-Wing Chau ◽  
Assefa M. Melesse
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
River Systems

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 

scholarly journals Evaluation of different types of artificial intelligence methods to model the suspended sediment load in Tigris River

2018 ◽  
Vol 162 ◽  
pp. 03003 ◽  
Author(s):  
Mustafa Al-Mukhtar
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Suspended Sediment Load ◽  
Inference System ◽  
Tigris River ◽  
Artificial Neural Network Ann

Modeling of suspended sediment load in rivers has a major role in a proper management of water resources. Artificial intelligence has been identified as an efficient way to model the complex nonlinear hydrological relationship. In this study, Adaptive Neuro Fuzzy Inference System (ANFIS), in addition to two different kinds of Artificial Neural Network (ANN) i.e. feedforward and radial basis networks were used and compared to model the suspended sediment load (SSL) in Tigris River-Baghdad using the streamflow discharge as input. To this end, an intermittent data of SSL and streamflow were collected over the period 1962-1981 from Sarai station in Baghdad. 70 % of these data was used to calibrate (train) the networks and the remaining 30% for the validation (test). The coefficient of determination (R2), root mean square error (RMSE), and Nash and Sutcliffe model efficiency coefficient (NSE) were used to judge whether the observed and modelled data belong to the same distribution. Results revealed that the ANFIS model outperform the other methods. R2, RMSE, and NSE of ANFIS during the calibration phase were equal to 0.58, 75617, and 0.58, respectively and during the validation were 0.72, 27944, and 0.59, respectively. Therefore, ANFIS approach is recommended to estimate the river suspended sediment load.

scholarly journals Estimation of Suspended Sediment Load Using Artificial Intelligence-Based Ensemble Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Vahid Nourani ◽  
Huseyin Gokcekus ◽  
Gebre Gelete
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Ensemble Methods ◽  
Performance Measure ◽  
Combination Method ◽  
Suspended Sediment Load ◽  
Anfis Model ◽  
Ensemble Models

Suspended sediment modeling is an important subject for decision-makers at the catchment level. Accurate and reliable modeling of suspended sediment load (SSL) is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence- (AI-) based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI-based models, that is, support vector machine (SVM), adaptive neurofuzzy inference system (ANFIS), feed-forward neural network (FFNN), and one conventional multilinear regression (MLR) modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble (NNE), ANFIS ensemble (AE), weighted average ensemble (WAE), and simple average ensemble (SAE), were developed by combining the outputs of the four single models to improve their predictive performance. The study used two-year (2016-2017) discharge and SSL data for training and verification of the applied models. Determination coefficient (DC) and root mean square error (RMSE) were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI-based ensemble in particular provide excellent performance in SSL estimation.

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