Quantum Computing and Bayesian Optimization-Inspired Multilayer Perceptron Approach for Suspended Sediment Concentration Estimates at Rivers

Suspended Sediment Concentration (SSC) is an indicator of a river system's quality and has several ecological impacts on aquatic life. Increased levels of SSC typically reduce the transparency of the water, thereby reducing photosynthetic activity. Moreover, it has an adverse effect on aquatic organisms due to impediments to respiration and altered habitat conditions. Additionally, contaminants, including heavy metals and organic pollutants, can be carried by suspended sediments as they attach to them. Thus, understanding and prediction of SSC became an active research topic in hydrologic science for better analyzing the sources and variations of SSC . The current research aims to estimate and predict SSC levels using measured data from the flow station 07373420 located on the Mississippi River in West Feliciana Parish in the Gulf of Mexico operated by the US Geological Survey. Over 29 years (1992 to 2021) of monthly flow and SSC data were obtained and used in this research. The SSC values were estimated using three machine learning-based modeling frameworks: Multilayer Perceptron (MLP), hybrid Quantum-Inspired Algorithms - Multilayer Perceptron (QIS-MLP), and Bayesian Optimization-Multilayer Perceptron (BO-MLP). The MLP models the complex, nonlinear relationship between independent and dependent variables using an artificial neural network. Each neuron receives the weighted inputs from the previous layer and processes them through the activation function, allowing the MLP to detect nonlinear relationships among the various input variables. With BO, the optimal hyperparameters of the MLP model (number of hidden layers, number of neurons, and learning rate) were tuned automatically. BO-MLP selects promising configurations through iterative evaluations of the acquisition function, optimally exploring the hyperparameter search space for the MLP model. The QIS-MLP utilizes an improved global search strategy combined with enhanced ability to conduct local searches by virtue of its advanced quantum optimization algorithm. To evaluate the performance of each model, the standard statistical measures of model performance were utilized, including Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Correlation Coefficient (CC). Among all tested input combinations, the QIS-MLP with three lagged SSC values and three lagged discharge values represents the best performance with the highest degree of accuracy and greatest potential for generalization of SSC values (RMSE = 37.34, NSE = 0.418, KGE = 0.537, and CC = 0.7). As a result, the QIS-MLP achieved superior output through optimization of functions and reduction in error rates. Based on the results, QIS-MLP were found to demonstrate superior accuracy and reliability of monthly SSC estimates and, therefore, considered an excellent candidate for intelligent modeling of complex hydrological systems.

Keywords: Hydrological Modeling, Quantum-Inspired Algorithms, Suspended Sediment Concentration