Abstract
A data-driven relationship between sediment and discharge of a river is among the most erratic relationships in river engineering due to the existence of an inevitable scatter in sediment rating curves. Recently, Multigene Genetic Programming (MGGP), as a machine learning (ML) method, has been proposed to develop data-driven models for various phenomena in the field of hydrology and water resource engineering. The present study explores the capability of MGGP-based models to develop daily sediment ratings of two gauging sites with 30-year sediment-discharge data, which was utilized previously in the literature. The results obtained by MGGP were compared with those achieved by an empirical model and Artificial Neural Network (ANN). The coefficients of the empirical model were calibrated using linear and nonlinear regression models (Generalized Reduced Gradient (GRG) and the Modified Honey Bee Mating Optimization (MHBMO) algorithm). According to the comparative analysis, the mean absolute error (MAE) at the two gauging stations reduced from 516.54 to 519.23 obtained by nonlinear regression to 447.26 and 504.23 achieved by MGGP, respectively. Similarly, all other performance indices indicated the suitability and accuracy of MGGP in developing sediment ratings. Therefore, it was demonstrated that ML-based models, particularly MGGP-based models, outperformed the empirical models for estimating sediment loads.
Highlights
Climate change and the surge in water demands have created a need for better strategies in river engineering [1, 2]
Based on the performance of Multigene Genetic Programming (MGGP) in previous applications and the need for developing sediment ratings with higher accuracy, the present study aims to investigate the capability of MGGP to model the sediment ratings and compare the performance of MGGP models with other prevalent techniques. e parameters of the empirical model of sediment ratings were calibrated by linear regression and two different optimization algorithms (the Modified Honey Bee Mating Optimization (MHBMO) algorithm and Generalized Reduced Gradient (GRG))
The present work explores the possibility of improving the estimates of sediment loads by using two machine learning (ML) models (ANN and MGGP) over the commonly used sediment rating equation (equation (1))
Summary
Climate change and the surge in water demands have created a need for better strategies in river engineering [1, 2]. In this regard, a close-to-reality estimation of sediment loads transported by a river plays a vital role in developing river management strategies [3]. Knowledge of sediment transport is important for various river engineering applications, including channel restoration, reservoir sedimentation [4], soil loss [5], predicting bed roughness coefficients [6], water quality analysis [7], and design of hydraulic structures. Accounting for the complexity of sediment-discharge relationships, researchers have sought artificial intelligence (AI) and machine learning (ML) methods as an alternative to the simple conventional sediment rating
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