ABSTRACT The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.

中文翻译：

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使用人工智能进行月悬浮泥沙负荷预测：一种新的随机子空间方法的测试

摘要 将一种新的人工智能方法随机子空间 (RS) 预测河流悬浮泥沙负荷的预测能力与常用方法进行了比较：随机森林 (RF) 和使用径向分布的两种支持向量机 (SVM) 模型。基函数核 (SVM-RBF) 和归一化多项式核 (SVM-NPK)。使用伊朗哈拉兹河的河流流量、降雨量和河流水位数据，结果显示：(a) RS 模型提供了比 SVM-RBF (NSE = 0.80)、SVM-NPK ( NSE = 0.78) 和 RF (NSE = 0.68)，分别对应很好、良好、满意和不满意的负荷预测精度；(b) RBF 核优于 NPK 核；(c) 在 SVM 模型中，预测能力对 gamma 和 epsilon 最敏感，树的最大深度和 RF 模型中的特征数量、分类器类型、树的数量和 RS 模型中的子空间大小；(d) 悬浮泥沙负荷与河流流量最密切相关（PCC = 0.76）。总体而言，结果表明 RS 模型在数据贫乏的流域中具有巨大的潜力，例如这里研究的流域，仅根据河流流量、降雨深度和河流水位的月度记录，就可以对悬浮荷载进行强有力的预测。