Simulating and Predicting of Hydrological Time Series Based on TensorFlow Deep Learning

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About the Journal In Memory of Professor Jerzy Radecki Ownership and Management Statement Editorial Office Editorial Board Scope Impact Factor Indexed by Journal Impact Factor contributing items Publication Frequency Revenue Sources & Business Model Contact Subscription

Current issue

Online first

Instructions for Authors Frequently Asked Questions

Editorial policies Publication Policy Overview Peer Review Policy Research Ethics and Malpractice Policy Plagiarism Policy Conflict of Interest Policy Open Access Policy Instructions for Reviewers Generative AI and AI-Assisted Technologies Policy Advertising Policy Direct Marketing & Manuscript Solicitation Digital Archiving and Preservation Policy CrossMark, Correction, and Retraction Policy

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ORIGINAL RESEARCH

Simulating and Predicting of Hydrological Time Series Based on TensorFlow Deep Learning

Jinbo Qin ¹

,

Ji Liang ¹

,

Tao Chen ²

,

Xiaohui Lei ³

,

Aiqing Kang ³

1

Huazhong University of Science and Technology,Wuhan, China

2

Electric Power Research Institute, Jilin Electric Power Co., Changcun, China

3

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin and China Institute of Water Resources and Hydropower Research, Beijing, China

Submission date: 2017-11-15

Final revision date: 2017-12-13

Acceptance date: 2017-12-23

Online publication date: 2018-09-19

Publication date: 2018-12-20

Corresponding author

Xiaohui Lei

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin & China Institute of Water Resources and Hydropower Research

Pol. J. Environ. Stud. 2019;28(2):795-802

DOI: https://doi.org/10.15244/pjoes/81557

KEYWORDS

hydrological time series

TOPICS

ABSTRACT

Hydrological time series refers to the observation time point and the observed time value. The simulation and prediction of hydrological time series will greatly improve the predictability of hydrological time series, which is of great significance for hydrological forecasting. TensorFlow, the second generation of artificial intelligence learning system in Google, has been favored by a large number of researchers by virtue of its high flexibility, portability, multi-language support, and performance optimization. However, the application of deep learning in hydrology is less. Based on the TensorFlow framework, the AR model and the LSTM model are constructed in Python language. The hydrological time series is used as the input object, and the model is deeply studied and trained to simulate and predict the hydrological time series. The effect of the model was tested by fitting degrees and other indexes. The fitting degree of the AR model is 0.9551, and the fitting degree of the LSTM model is 0.8012, which shows the feasibility of the model for predicting the hydrological time series, and puts forward the solution for the limitation of the existing analysis results.

CONFLICT OF INTEREST

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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