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Suspended sediment load prediction using non-dominated sorting genetic algorithm Ⅱ

International Soil and Water Conservation Research 2019年第2期7卷 119-129页

作者： mahmoudreza tabatabaei Amin Salehpour Jam Seyed Ahmad Hosseini Soil Conservation and Watershed Management Research Institute Agricultural ResearchEducation and Extension Organization(AREEO)TehranIran

Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed *** the common use of the conventional model of the sediment rating curve (SRC) and the methods proposed to correct it,the results of this model are still not sufficiently *** this study,in order to increase the efficiency of SRC model,a multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) *** instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River,Iran are used as a case *** the first part of the study,using self-organizing map (SOM),an unsupervised artificial neural network,the data were clustered and classified as two homogeneous groups as 70% and 30% for use in calibration and evaluation of SRC models,*** the second part of the study,two different groups of SRC model comprised of conventional SRC models and optimized models (single and multi-objective optimization algorithms) were extracted from calibration data set and their performance was *** comparative analysis of the results revealed that the optimal SRC model achieved through NSGA-Ⅱ algorithm was superior to the SRC models in the daily SSL estimation for the data used in this *** that the use of the SRC model is common,the proposed model in this study can increase the efficiency of this regression model.

关键词： Clustering Neural network Non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) Sediment rating curve Self-organizing map

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