Dr. Pen-shan Hung Profile

Dr. Pen-shan Hung

at Feng Chia Univ

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Proceedings Article | 3 November 2008 Paper

Testing prediction models of land subsidence on GPS permanent station

Hung-Zi Chen, Pen-shan Hung, Chia-Chyang Chang

Proceedings Volume 7145, 71452O (2008) https://doi.org/10.1117/12.813080

KEYWORDS: Data modeling, Global Positioning System, Artificial neural networks, Error analysis, Mathematical modeling, Differential equations, Geodesy, Control systems, Civil engineering, Environmental hazards

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Due to the heavy withdrawal of underground water for cultivating fishery and industrial factories, the land subsidence occurred in southwestern Taiwan has resulted in environmental hazard and potential risk. In order to fully realise the subsidence characteristics and establish a subsidence prediction function for any possible application in the study area, the height variation was estimated and tested for the representative site of Pei-Kang (PKGM), using some selected models, i.e. the linear regression, grey theory and artificial neural network. Since different estimation models associate with different time spans of the data, a series of GPS-based vertical coordinates was categorised into two groups of data set, namely a long-term (52 weeks) data set and a short-term (5 weeks) data set, both collected at PKGM for around 10 years (from 1996 to 2005). Using short-term data set, the prediction errors showed that a linear regression model works slightly better than grey theory. Since the land subsidence is possibly related to various natural factors, such as time, stream flow rate, ground water elevation or underground water level, etc., this paper also investigates the factor identification based on the height predictions using a multi-variant type of regression model and artificial neural network model. It was found that the prediction models can present a 1 cm level of height prediction error. Moreover, the most dominating influence factor was tested to be the variable of time. An artificial neural network operated with the main factor of time is capable of working with the long-term GPS data set to effectively predicate a 1 cm level of height variation in a significant land subsidence area.

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