| 摘要(英) |
Land subsidence (LS) occurs all over the world and has brought many disasters to people. Choushui River alluvial fan is also suffered from it. Thus, in order to mitigate the impact of LS, prediction of LS becomes an important goal. Previously, researches of LS prediction in taiwan were mainly focused on numerical model, while machine learning method was seldom applied. In comparison with numerical model, machine learning method don′t necessarily need hydrogeological parameters when building model, it can be directly built with given dataset. This study aims to establish a LS prediction model of Choushui River alluvial fan area. We discussed the time scale of data fed into the model, importance of features, performance of different models, influence radius of feature, and establishment of a model with a spatial resolution of 100 x 100 (in meters). This study uses Random Forest (RF) and Long Short-Term Memory (LSTM) of machine learning method to build the model. Several hydrogeological data is regarded as feature. Deep leveling pile and Global Navigation Satellite System data as LS data. In order to stabilize the data trend, the groundwater level and LS data are processed by first-order difference. According to the results of experiment one and two, RF model has the best performance when training and prediction time scale are 90 and 30 days respectively, with two measurements which are used to calculate the deviation between predict and true value, the Root Mean Square Error (RMSE) can be as low as 4.27 (mm) and Mean Absolute Error (MAE) is 2.97 (mm). The time scale result can be used to determine the dimension of the input features and output LS result of the model. Besides, the feature importance experiment shows that the groundwater level is the most important feature. Among the groundwater level feature, the first confined and second confined aquifers are the most important. |
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