Water table depth prediction based on Deep learning models in Electrical
Power Transmission Lines area
Abstract
Background: Predicting water table depth in Electrical Power
Transmission Lines area presents great importance and helps the decision
makers do the safety analysis during the project. The present study
predicts the water table depth with observed weather data and hydrologic
data. Method: The study first compared the results of LSTM, GRU,
LSTM-S2S, and FFNN models in daily data simulation. Moreover, two
scenarios (S1 and S2) were set to identify the effect of the water
component on water table depth simulation. In addition, in order to
analyze how data time scale influences the model simulation results, the
monthly scale data was simulated by LSTM, GRU, and LSTM-S2S models.
Result: The result indicated that LSTM-S2S was the best model for
predicting daily water table depth among the four models. By contrast,
FFNN performed the worst. LSTM and GRU model performed equally well both
in daily data and monthly data simulation. S1 performed better than S2
in the water table depth simulation. The average daily performance of R2
and NSE was both higher than that in the monthly results with LSTM, GRU,
and LSTM-S2S models. Conclusion: As a result, the method in the present
study can be used to simulate the water table depth in the future in
Electrical Power Transmission Lines area.