Evaluating the Performance of Machine Learning Models for Predicting Suspended Sediment Load (case study: Taleghan watershed, Iran)

Accurate prediction of suspended sediment load (SSL) in rivers is crucial for sustainable water resources management.Sediment, as a significant factor in degrading aquatic ecosystems, reducing the lifespan of hydraulic structures, and deteriorating water quality, has been a major concern.Given the complexity of hydrological processes and the interplay of various factors affecting sediment production and transport, accurate modeling of this phenomenon has always been challenging.In recent years, advancements in machine learning techniques have enabled the development of highly accurate predictive models.This study aims to enhance the understanding of sediment dynamics in watersheds and provide more precise tools for SSL prediction by applying machine learning models to daily SSL forecasting in the Taleghan watershed.The Taleghan watershed, characterized by its mountainous topography and specific hydrological conditions, was selected for this study due to its significance in supplying water to the Tehran region.Accurate SSL prediction in this watershed is of paramount importance. 2-Material and MethodsFor this study, hydrological data including discharge, precipitation, snow index, and suspended sediment load were collected from the Gelink Taleghan hydrometric station over the period 2000 to 2018.The collected data, after undergoing quality checks and outlier removal, were prepared for training and testing machine learning models.Data preprocessing involved standardization, normalization, and outlier removal to enhance model accuracy.Principal Component Analysis (PCA) was employed to reduce data dimensionality and select the most significant input variables for the model.This method aids in identifying new linear combinations of original variables that explain the maximum variance in the data.By using PCA, the number of input variables in the model can be reduced, thereby decreasing model training time and complexity.Six machine learning models, including xgbTree, Cubist, qrnn, Ctree, Cforest, and LASSO, were utilized in this study.These models were chosen due to their ability to model nonlinear and complex relationships between variables.To evaluate model performance, various statistical metrics such as Root Mean Squared Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) were employed.Additionally, time series plots, scatter plots, and Taylor diagrams were used for visual assessment of model performance. 3-ResultsResults indicated that xgbTree, Cubist, and qrnn models outperformed other models in predicting SSL.These models effectively simulated daily variations in SSL.Overall, the xgbTree model demonstrated the best