Enhancing Accuracy of Sediment Total Load Prediction Using Evolutionary Algorithms (Case Study: Gotoorchay River)

آب و خاک

Article 1, Volume 29, Issue 6, January 2015, Pages 1416-1426   

Document Type: Research Article

DOI: 10.22067/jsw.v29i6.26891

Authors

K. Roshangar^* ; M.T. Aalami; F. Vojoudi Mehrabani

University of Tabriz

Abstract

Introduction: Exact prediction of transported sediment rate by rivers in water resources projects is of utmost importance. Basically erosion and sediment transport process is one of the most complexes hydrodynamic. Although different studies have been developed on the application of intelligent models based on neural, they are not widely used because of lacking explicitness and complexity governing on choosing and architecting of proper network. In this study, a Genetic expression programming model (as an important branches of evolutionary algorithems) for predicting of sediment load is selected and investigated as an intelligent approach along with other known classical and imperical methods such as Larsen´s equation, Engelund-Hansen´s equation and Bagnold´s equation.
Materials and Methods: In this study, in order to improve explicit prediction of sediment load of Gotoorchay, located in Aras catchment, Northwestern Iran latitude: 38°24´33.3˝ and longitude: 44°46´13.2˝), genetic programming (GP) and Genetic Algorithm (GA) were applied. Moreover, the semi-empirical models for predicting of total sediment load and rating curve have been used. Finally all the methods were compared and the best ones were introduced. Two statistical measures were used to compare the performance of the different models, namely root mean square error (RMSE) and determination coefficient (DC). RMSE and DC indicate the discrepancy between the observed and computed values.
Results and Discussions: The statistical characteristics results obtained from the analysis of genetic programming method for both selected model groups indicated that the model 4 including the only discharge of the river, relative to other studied models had the highest DC and the least RMSE in the testing stage (DC= 0.907, RMSE= 0.067). Although there were several parameters applied in other models, these models were complicated and had weak results of prediction. Our results showed that the model 9, with the most DC and the least RMSE (DC=0.694, RMSE= 0.081), had the relative advantage to the other none dimensional models. Finally it is clear that the model 6 had more predicting capability rather than the model 9, so among all the models, model 6 was the best referring model for estimation of sediment load of the Ghotoorchay river.
Conclusion: It was observed that the model including only the discharge of the Ghotoorchay river the best model for estimation of sediment load and it was applied for comparing all the other sediment predicting models such as some classic methods that includes Larsen´s equation, Engelund-Hansen´s equation and Bagnold´s equation and optimized rating curve. Among all methods, it was concluded that the genetic programming was superior to other methods in predicting sediment load of the mentioned river. Therefore, genetic programming that is a branch of evolutionary algorithms, with high prediction capability was offered as a powerful tool for optimizing and explicit predicting of total sediment load of the Ghotoorchay River.

Keywords

Genetic programming; Optimization of Prediction; Rating Curve; Sediment load

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