ABSTRACT

In the current paper, the efficiency of three new standalone data-mining algorithms [M5 Prime (M5P), Random Forest (RF), M5Rule (M5R)] and six novel hybrid algorithms of bagging (BA-M5P, BA-RF and BA-M5R) and Attribute Selected Classifier (ASC-M5P, ASC-RF and ASC-M5R) for streamflow prediction were assessed and compared with an autoregressive integrated moving average (ARIMA) model as a benchmark. The models used precipitation (P) and streamflow (Q) data from the period 1979–2012 for training and validation (70% and 30% of data, respectively). Different input combinations were prepared using both P and Q with different lag times. The best input combination proved to be that in which all of the the data were used (i.e. R and Q – with lag times). Overall, employing Q with different lag times proved to be more effective than using only P as input for streamflow prediction. Although all models showed very good predictive power, BA-M5P outperformed the other models.

摘要

在当前论文中，三种新的独立数据挖掘算法 [M5 Prime (M5P)、随机森林 (RF)、M5Rule (M5R)] 和六种新的装袋混合算法（BA-M5P、BA-RF 和 BA）的效率-M5R) 和属性选择分类器 (ASC-M5P、ASC-RF 和 ASC-M5R) 进行了评估，并与作为基准的自回归综合移动平均 (ARIMA) 模型进行了比较。这些模型使用1979-2012 年期间的降水 ( P ) 和流量 ( Q ) 数据进行训练和验证（分别为 70% 和 30% 的数据）。使用具有不同滞后时间的P和Q准备不同的输入组合。最佳输入组合被证明是使用所有数据的组合（即R和Q -滞后时间）。总的来说，使用具有不同滞后时间的Q被证明比仅使用P作为流量预测的输入更有效。尽管所有模型都显示出非常好的预测能力，但 BA-M5P 的表现优于其他模型。