Water Level Data Preprocessing Method Based on Savitzky-Golay Filter

DOI

10.2991/msbda-19.2019.44How to use a DOI?

Keywords

Savitzky-Golay filter, Water level data, Time series, LSTM

Abstract

Using historical water level data to establish a model to estimate the future water level is a common means of water level prediction. In this type of method, the accuracy of water level prediction is closely related to the quality of historical water level data. However, due to measurement accuracy and system deviations, water level data collected by real-time water level observatories often have abnormal or erroneous data. Aiming at this situation, this paper studies the water level data preprocessing method based on Savitzky-Golay filter. This method removed the noise of historical water level data and smoothed the data, which could better establish the water level prediction model. This paper used the water level prediction model established by LSTM to verify the water level data preprocessing method. The results showed that this method can improve the accuracy of the prediction model.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoxiong Wang
AU  - Mingyang Pan
AU  - Chunxiao Xia
PY  - 2019/08
DA  - 2019/08
TI  - Water Level Data Preprocessing Method Based on Savitzky-Golay Filter
BT  - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019)
PB  - Atlantis Press
SP  - 288
EP  - 294
SN  - 2352-538X
UR  - https://doi.org/10.2991/msbda-19.2019.44
DO  - 10.2991/msbda-19.2019.44
ID  - Wang2019/08
ER  -