Suspended sediment has significant effects on reservoir storage capacity, the operation of hydraulic structures and river morphology. Hence, modeling suspended sediment loads (SSL) in rivers contributes to various water resource management and river engineering. An evaluation of stand-alone data mining models (i.e., reduced error pruning tree (REPT), M5P and instance-based learning (IBK)) and hybrid models, (i.e., bagging-M5P, random committee-REPT (RC-REPT) and random subspace-REPT (RS-REPT)) for predicting SSL resulting from glacial melting at an Andean catchment in Chile has been conducted in this study. The best input combinations are constructed based on Pearson correlation coefficient (PCC) of hourly SSL time series data with water discharge (Q), water temperature (T) and electrical conductivity (C) for different time lags. Seventy percent of the available data (one year of hourly data) is used to calibrate the models (dataset training) and the remaining 30% is used for model evaluation (dataset testing). The performances of the models are evaluated using several quantitative and graphical criteria, including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), percentage of bias (PBIAS), the ratio of RMSE to the standard deviation of observation (RSR), a Taylor diagram and a boxplot. All the models performed well in predicting SSL. However, the Friedman and Wilcoxon signed rank tests revealed that predicted SSL significantly differed for different models except between IBK (or M5P) and REPT. The hybrid models performed better than individual models. The bagging-M5P had the best predictive capability while the REPT had the poorest.

中文翻译：

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使用数据挖掘模型量化每小时悬浮沉积物负荷：智利安第斯冰川流域的案例研究

悬浮泥沙对水库蓄水能力、水工建筑物的运行和河流形态有显着影响。因此，模拟河流中的悬浮泥沙负荷 (SSL) 有助于各种水资源管理和河流工程。独立数据挖掘模型（即减少错误修剪树（REPT）、M5P 和基于实例的学习（IBK））和混合模型（即bagging-M5P、随机委员会-REPT（RC-REPT））的评估和随机子空间-REPT (RS-REPT)) 用于预测智利安第斯流域冰川融化导致的 SSL。最佳输入组合是基于每小时 SSL 时间序列数据的 Pearson 相关系数 (PCC) 构建的，其中包含不同时间滞后的排水量 (Q)、水温 (T) 和电导率 (C)。70% 的可用数据（一年的每小时数据）用于校准模型（数据集训练），其余 30% 用于模型评估（数据集测试）。使用多种定量和图形标准评估模型的性能，包括决定系数 (R2)、均方根误差 (RMSE)、平均绝对误差 (MAE)、纳什-萨特克利夫效率 (NSE)、偏差百分比 (PBIAS) )、RMSE 与观测标准差 (RSR) 的比率、泰勒图和箱线图。所有模型在预测 SSL 方面都表现良好。然而，弗里德曼和威尔科克森符号秩检验表明，除了 IBK（或 M5P）和 REPT 之外，不同模型的预测 SSL 显着不同。混合模型的性能优于单个模型。