期刊
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
卷 66, 期 9, 页码 1457-1474出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02626667.2021.1928673
关键词
streamflow modelling; M5P; random forest; M5Rule; bagging; Attribute Selected Classifier; data mining; Taleghan catchment
资金
- Ferdowsi University of Mashhad, Mashhad, Iran
This paper assesses the efficiency of various data mining algorithms and hybrid models for streamflow prediction, with BA-M5P outperforming other models.
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.
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