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

Abstract

Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management. Despite 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 accurate. In 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) algorithm. The instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River, Iran are used as a case study. In 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, respectively. In 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 evaluated. The 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 study. Given that the use of the SRC model is common, the proposed model in this study can increase the efficiency of this regression model.