MATEC Web Conf.
Volume 162, 2018The 3rd International Conference on Buildings, Construction and Environmental Engineering, BCEE3-2017
|Number of page(s)||8|
|Section||Water Resources Engineering and Geomatics|
|Published online||07 May 2018|
Derivation of suspended sediment data for Al-Adhiam watershed-Iraq using artificial neural network model
Building and Construction Engineering Department, University of Technology, Baghdad, Iraq
2 Ministry of Water Resources, Baghdad, Iraq
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.