Prediction of karst spring water level based on the time series analysis method

GUO Yi; QIN Dajun; WANG Feng; GAN Fupin; YAN Baikun

Volume 40 Issue 4

Aug. 2021

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GUO Yi, QIN Dajun, WANG Feng, GAN Fupin, YAN Baikun. Prediction of karst spring water level based on the time series analysis method[J]. CARSOLOGICA SINICA, 2021, 40(4): 689-697.

Citation:

GUO Yi, QIN Dajun, WANG Feng, GAN Fupin, YAN Baikun. Prediction of karst spring water level based on the time series analysis method[J]. CARSOLOGICA SINICA, 2021, 40(4): 689-697.

GUO Yi, QIN Dajun, WANG Feng, GAN Fupin, YAN Baikun. Prediction of karst spring water level based on the time series analysis method[J]. CARSOLOGICA SINICA, 2021, 40(4): 689-697.

Citation:

GUO Yi, QIN Dajun, WANG Feng, GAN Fupin, YAN Baikun. Prediction of karst spring water level based on the time series analysis method[J]. CARSOLOGICA SINICA, 2021, 40(4): 689-697.

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Prediction of karst spring water level based on the time series analysis method

1.
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources/Institute of Geology and Geophysics，Chinese Academy of Sciences
2.
Institute of Geology and Geophysics，Chinese Academy of Sciences
3.
Chinese Academy of Environmental Planning
4.
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources

Publish Date: 2021-08-25

Abstract

Abstract

Karst springs in Jinan City are the main water supply source，thus，it is of great significance to find out the dynamic law of spring water and reasonably and scientifically forecast the spring water level for the development，utilization and protection of karst water resources. In this paper，firstly，the daily water level data of Baotu Spring and Heihu Spring from May 2，2012 to October 31，2018 are decomposed into trend terms，periodic terms and random terms by applying with time series analysis method， the dynamic variation law of spring water level was analyzed and the water level forecast model was established. The results show that there is no obvious trend change of spring water level dynamics at this stage，however， under the influence of precipitation，there are two major periods of dynamic variation of spring water level ，perennial change（3.2 years）and seasonal change. At the same time，due to the influence of various irregular interference factors，the dynamic variation of spring water level presents a random term. Secondly，the prediction accuracy of the forecast model is verified by the daily spring water level data from November 1，2018 to August 24，2020，and the results show that the model runs reasonably and has good prediction effect， with certain practical value. Finally，the spring water level（ from August 25，2020 to October 31，2022） is forecasted by above model，which provides a basis for the development and management of local karst water resources.
- time series analysis method,
- water level forecast,
- karst spring

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References(29)

References

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