scholarly journals ESTIMATION OF SUSPENDED SEDIMENT LOAD BY ARTIFICIAL NEURAL NETWORK

2019 ◽  
Vol 9 (4) ◽  
pp. 665-670
Author(s):  
Ş.Y. Kumcu ◽  
◽  
A.E. Tumer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Artificial Neural

scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

scholarly journals Monthly Suspended Sediment Load Estimation using Artificial Neural Network and Traditional Models in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 613-618
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Sediment Load ◽  
Water Discharge ◽  
Tectonic Activity ◽  
Precipitation Seasonality ◽  
Artificial Neural

The measurement of sediment yield is essential for getting the information of the mass balance between sea and land. It is difficult to directly measure the suspended sediment because it takes more time and money. One of the most common pollutants in the aquatic environment is suspended sediments. The sediment loads in rivers are controlled by variables like canal slope, basin volume, precipitation seasonality and tectonic activity. Water discharge and water level are the major controlling factor for estimate the sediment load in the Krishna River. Artificial neural network (ANN) is used for sediment yield modeling in the Krishna River basin, India. The comparative results show that the ANN is the easiest model for the suspended sediment yield estimates and provides a satisfactory prediction for very high, medium and low values. It is also noted that the Multiple Linear Regressions (MLR) model predicted an many number of negative sediment outputs at lower values. This is entirely unreality because the suspended sediment result can not be negative in nature. The ANN is provided better results than traditional models. The proposed ANN model will be helpful where the sediment measures are not available.

Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm

2020 ◽  
Vol 27 (30) ◽  
pp. 38094-38116 ◽  
Author(s):  
Fatemeh Barzegari Banadkooki ◽  
Mohammad Ehteram ◽  
Ali Najah Ahmed ◽  
Fang Yenn Teo ◽  
Mahboube Ebrahimi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
Optimization Algorithm ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Load Prediction ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Lion Optimization Algorithm

scholarly journals Derivation of suspended sediment data for Al-Adhiam watershed-Iraq using artificial neural network model

2018 ◽  
Vol 162 ◽  
pp. 03014
Author(s):  
Mahmoud Saleh Al-Khafaji ◽  
Mustafa Al-Mukhtar ◽  
Ahmed Saud Mohena
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Data Sampling ◽  
Ann Model ◽  
Measured Flow ◽  
Sediment Data ◽  
Target Data

The mean part of river sediments is suspended sediment load, its prediction and simulation has important significance to manage the water resources and environments. In Iraq, most researchers avoid to fighting in sediment researches when related with hydrological models spatially with that need enough observed sediment data for calibration and validation because the sediment data very limitation or scars. The aim of this study is employing the Artificial Neural Network (ANN) model to estimate the suspended sediment load of Al-Adhaim watershed in Iraq from available measured sediment data, identify the suitable pattern of input and target data sampling and obtaining the best nonlinear equation between the river discharge and suspended sediment load. To this end, the ANN model was training and tested with the available sediment data, which was for water year (1983-1984). Two modes were applied for input and target data sampling each mode has two cases, where in the first mode the time series data sampling was used with flow as an input for case one while flow and average precipitation in case two with used suspended sediment as a target variable. For second mode the supervise data sampling was used with the same input and target division in first mode. The performance of the model was evaluated by using Coefficient of determination (R2) and the Nash- Sutcliffe efficiency (NS) and standardization of root mean square error (RSR), the statistical analysis model testing for Al-Adhiam watershed showed satisfactory agreement between observed and estimated daily values for Mode2- Case2. R2, NS and RSR of the testing period were 0.99 and 0.8and 0.2 respectively. The result shows that the conducted ANN model can be used with the best net as a predictor for sediment yield in this watershed. The model was used to predict daily sediment load data for period from 1Oct. 1984 to 31Spt 1985. The predicted daily sediment data was plotted against daily measured flow. The correlation between predicted sediment and measured flow was in good agreement with R2 =0.89 and the best relation was polynomial equation from second degree.

scholarly journals Correction to: Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm

2020 ◽  
Vol 27 (30) ◽  
pp. 38117-38119
Author(s):  
Fatemeh Barzegari Banadkooki ◽  
Mohammad Ehteram ◽  
Ali Najah Ahmed ◽  
Fang Yenn Teo ◽  
Mahboube Ebrahimi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
Optimization Algorithm ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Load Prediction ◽  
Ant Lion Optimization ◽  
Ant Lion ◽  
Lion Optimization Algorithm
Sign in / Sign up

Export Citation Format

Share Document