Lake-Level Prediction Leveraging Deep Neural Network

  • Jinfeng Wen
  • Peng-Fei Han
  • Zhangbing Zhou
  • Xu-Sheng Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 234)


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.


Lake level Sumu Barun Jaran Badain Jaran Desert Deep learning Artificial neural network 


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.China University of Geosciences (Beijing)BeijingChina
  2. 2.Computer Science DepartmentTELECOM SudParisEvryFrance

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