Appraisal of data-driven techniques for predicting short-term streamflow in tropical catchment

Autor: Kai Lun Yeoh, How Tion Puay, Rozi Abdullah, Teh Sabariah Abd Manan
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Water Science and Technology, Vol 88, Iss 1, Pp 75-91 (2023)
Druh dokumentu: article
ISSN: 0273-1223
1996-9732
DOI: 10.2166/wst.2023.193
Popis: Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash–Sutcliffe efficiency (NSE): 0.599–0.962) outperforms the MLR model (NSE: 0.584–0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models. HIGHLIGHTS The novel short-term streamflow prediction was performed using RF and MLR models.; The performance of the data-driven models varies with input combinations and model algorithms.; The RF model captured the nonlinearity in the time series of streamflow.; The RF model has better accuracy than the MLR model.; The prediction accuracy of the data-driven models decreases as the lead-time length increases.;
Databáze: Directory of Open Access Journals
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