Time-Series Prediction of Streamflows of Malaysian Rivers Using Data-Driven Techniques
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Volume 146, Issue 7
Abstract
A reliable and continuous streamflow simulation capability is essential for systematic management of water resource systems. Thus, predicting streamflow is important for water management and flood control. This study evaluated the effectiveness of a few data-driven procedures, such as the least squares support vector machine (LS-SVM), M5P tree, and random forest (RF) algorithm for estimating streamflows of the Bernam and Tualang rivers of Malaysia. Three standard statistical measures, i.e., correlation coefficient (CE), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the performance of the developed model. The performance of RF-based models was found to be higher than that of LS-SVM and M5P-based models with respect to predicting streamflow for both the rivers.
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Data Availability Statement
All the data, models, and code generated during this study are available from the corresponding author on request. The streamflow data for both the rivers and partial code for LS-SVM, RF, and M5P models will be available on request.
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Journal of Irrigation and Drainage Engineering
Volume 146 • Issue 7 • July 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jul 3, 2019
Accepted: Nov 18, 2019
Published online: Apr 24, 2020
Published in print: Jul 1, 2020
Discussion open until: Sep 24, 2020
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