Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach
Journal: International Journal of Advanced Engineering Research and Science (Vol.4, No. 12)Publication Date: 2017-12-10
Authors : Bestami TaÅŸar Yunus Ziya Kaya Hakan Varçin Fatih ÜneÅŸ Mustafa Demirci;
Page : 79-84
Keywords : Suspended Sediment; Artificial Neural Networks; Sediment Rating Curves; M5 Tree; Estimation.;
Abstract
Suspended sediment estimation is important to the water resources management and water quality problem. In this article, artificial neural networks (ANN), M5tree (M5T) approaches and statistical approaches such as Multiple Linear Regression (MLR), Sediment Rating Curves (SRC) are used for estimation daily suspended sediment concentration from daily temperature of water and streamflow in river. These daily datas were measured at Iowa station in US. These prediction aproaches are compared to each other according to three statistical criteria, namely, mean square errors (MSE), mean absolute relative error (MAE) and correlation coefficient (R). When the results are compared ANN approach have better forecasts suspended sediment than the other estimation methods.
Other Latest Articles
- Petrophysical Evaluation and Reservoir Characterization of the Zubair Formation in the Luhais and Rachi oil fields, Southern Iraq
- Updating and Rendering Content on Objects in a three Dimensional Virtual Environment
- EFFECTS OF SELECTED MORDANTS ON THE APPLICATION OF NATURAL DYE EXTRACTED FROM THE PODS OF PITHECELLOBIUM JIRINGA SEEDS
- EFFECTS OF SELECTED MORDANTS ON THE APPLICATION OF NATURAL DYE EXTRACTED FROM THE PODS OF PITHECELLOBIUM JIRINGA SEEDS
- ASSESSING THE SATISFACTION RATE OF HOSPITALIZED MOTHERS AND NURSES WARDS IN AL-HADI HOSPITAL OF SHOUSHTAR AFTER THE ESTABLISHMENT OF A HEALTH CARE REFORM PLAN IN 2017
Last modified: 2017-12-30 01:52:33