Groundwater and gravity modelling using recurrent neural networks
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2021
Författare
Våge, Magnus
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Artificial intelligence has gained a lot of interest in recent years due to its impressive
performance on a variety of prediction and classification tasks. These results are in
large part a result of the increase in computational capacity and the availability of
large data sets.
Here we apply artificial intelligence on the problem of predicting local variations
of gravity due to local hydrological effects at the Onsala Space Observatory. Hydrological
effects imply redistribution of mass in close proximity to the measuring
equipment, and therefore contributes to the variations seen in the local strength of
gravity. The objective of this thesis is to model groundwater and residual gravity
levels based on data from a normal weather station, giving us the ability to construct
useful simulated groundwater and residual gravity data measurements without the
actual equipment in place. The modelling is performed using recurrent neural networks
containing LSTM units, and with some convolutional preprocessing of the
input data. The resulting models perform very well for predicting groundwater levels,
reaching an RMSE between observed and predicted values of 0.046 m over the
course of 2019 which is comparable to other results using neural networks to predict
hourly groundwater levels ([2], [3]).
The neural networks initially performed quite poorly when predicting residual gravity.
However, by utilizing our good results from the groundwater predictions, we
extended our available data set from about 3 to almost 8 years. The best model
trained on the 8 year data set achieved an RMSE of 5.961 nms−2 on the test set, a
more than 6-fold improvement over the networks trained on the 3 year data set.
The conclusion is that recurrent neural networks are suitable for modelling groundwater
levels and residual gravity, but their performance is highly dependent on the
amount of data available and the preprocessing applied to the data.
Beskrivning
Ämne/nyckelord
rnn, lstm, gravity, groundwater, seq2seq, cnn.