| dc.description.abstract | Land subsidence is an irreversible geological phenomenon that has had a significant
impact on human safety around the world, and Taiwan is no exception. Therefore,
predicting land subsidence and implementing early prevention measures have become
important issues. In the early stages, land subsidence prediction in Taiwan focused
mainly on numerical research and simulations. Compared to numerical simulations,
artificial intelligence methods do not require estimated hydrological parameters and
pumping data. They can directly model the given dataset. However, other studies that
apply artificial intelligence methods for prediction have been hindered by insufficient
recorded data, making it challenging to achieve effective predictions. With the
increasing availability of recorded data and advancements in artificial intelligence
technology, more promising results can be achieved in this field. This study utilized
the Kriging interpolation to establish a dataset for the entire Yunlin County using
Global Navigation Satellite System (GNSS) automatic reference station data, as well
as station-based datasets. This study utilized the Kriging method to establish a dataset
for the entire Yunlin County using Global Navigation Satellite System (GNSS)
automatic reference station data, as well as station-based datasets. By using seven
features, including groundwater level, humidity, temperature, rainfall, sunshine hours,
land use, and geological composition, the model was able to effectively describe the
real subsidence levels in the Yunlin County-wide land subsidence sensitivity test. The
evaluation indicators were as follows: R
2
(Coefficient of Determination) – 0.954, Cor
(Pearson Correlation Coefficient) – 0.979, MSE (Mean Square Error) – 2.20E-05 (unit:
square meter). Regarding the prediction of specific GNSS stations, the model showed
the most stable results when using an 8-week training dataset to predict subsidence for
the next 8 weeks. The combined model yielded the following results: R2 – 0.221, Cor– 0.519, and RMSE (Root Mean Square Error) – 0.00207(unit: meter). | en_US |