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Predicting discharge using a low complexity machine learning model
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DOI：10.1016/j.compag.2015.09.012.png)
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摘要
En 中文
This paper reports on the validation of a simplified discharge prediction model that is suitable for implementation on a resourced constrained system such as a wireless sensor network, which will allow their operation to become more proactive rather than reactive. The data-driven model, utilising an M5 decision tree modelling technique, is validated using a 12-month training data set derived from published measured data. Daily runoff and drainage is predicted, and the results are compared with existing data-driven models developed in this domain. Results for the model give an R-2 of 0.82 and Root Relative Mean Square Error (RRMSE) of 35.9%. 80% of the residuals for the predicted test values fall within a 2 mm discharge depth/day error range. The main significance is that the proposed model gives comparable results with fewer samples and simpler parameters when compared to previous published research, which offers the potential for implementation in resource constrained monitoring and control systems. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Wireless sensor networks
Discharge prediction
Environmental modelling
Machine learning
M5 trees
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期刊
IF:
8.9
论文数:
1.0W
被引数:
4.9W
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