Abstract
Accurate estimation of water level dynamics in lakes at daily or hourly time-scales is important for the ecosystem and formulation of water resources policies. In this study, lake level dynamics of Sumu Barun Jaran are simulated and predicted at hourly time scale using Deep Learning (DL) model. Two mature machine learning methods, namely Multiple Linear Regression (MLR) and Artificial Neural Network (ANN), are also adopted for the comparison purpose. The result shows that the DL model preforms the best on three criteria, following by the three-layered Back-Propagation ANN model and MLR model.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wen, J., Han, PF., Zhou, Z., Wang, XS. (2018). Lake-Level Prediction Leveraging Deep Neural Network. In: Wang, L., Qiu, T., Zhao, W. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-78078-8_3
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DOI: https://doi.org/10.1007/978-3-319-78078-8_3
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