Abstract
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.
Highlights
Sediment yield is defined as the total measured sediment load outflow from a watershed at a point of reference in a specified period
The results showed that the ability of Artificial Neural Network (ANN) model for predicting sediment was better than other regression approaches due to their abilities in capturing nonlinear system among variables and used more than one input variable. [5] developed two different ANN methods to simulate relationship of suspended sediment load with river flow and precipitation by using hydro meteorological data
3.1 Structure characteristics of ANN The number of neurons in the hidden layer was determined using the trial and error procedure to reach the best characteristics of optimal structure of ANN that were used in this research
Summary
Sediment yield is defined as the total measured sediment load outflow from a watershed at a point of reference in a specified period. Transportation, and deposition of soil materials induce the sediment outflow from the watershed by rainfall and runoff. The most reliable method in guessing sediment quantity is the use of its measured records, but sediment sampling is very complex and requires high experience because of its considerable fluctuation within the river section and user-unfriendly measurement tools. These constraints led to low frequency of sediment observation especially in the remote regions
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