Quality, Reliability, Security and Robustness in Heterogeneous Systems. 13th International Conference, QShine 2017, Dalian, China, December 16 -17, 2017, Proceedings

Research Article

Lake-Level Prediction Leveraging Deep Neural Network

  • @INPROCEEDINGS{10.1007/978-3-319-78078-8_3,
        author={Jinfeng Wen and Peng-Fei Han and Zhangbing Zhou and Xu-Sheng Wang},
        title={Lake-Level Prediction Leveraging Deep Neural Network},
        proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 13th International Conference, QShine 2017, Dalian, China, December 16 -17, 2017, Proceedings},
        proceedings_a={QSHINE},
        year={2018},
        month={4},
        keywords={Lake level Sumu Barun Jaran Badain Jaran Desert Deep learning Artificial neural network},
        doi={10.1007/978-3-319-78078-8_3}
    }
    
  • Jinfeng Wen
    Peng-Fei Han
    Zhangbing Zhou
    Xu-Sheng Wang
    Year: 2018
    Lake-Level Prediction Leveraging Deep Neural Network
    QSHINE
    Springer
    DOI: 10.1007/978-3-319-78078-8_3
Jinfeng Wen1,*, Peng-Fei Han1, Zhangbing Zhou, Xu-Sheng Wang1
  • 1: China University of Geosciences (Beijing)
*Contact email: wenjinfeng127@163.com

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.