Method of Hydrological Time Series Recovery Using Neural Network Technologies

Abstract

n this work we applied machine learning methods in the field of hydrological measurements to recover (to predict) missing or damaged data. Field measurements of temperature field that were taken as a typical example, were carried out on the Sea of Japan shelf (the Peter the Great Bay) in October 2021 using a vertical moored thermostring. Recording of one of its sensors was partly deleted in manual way. To recover the missing part, we used the method of averaging the readings of the nearest sensors, as well as one-dimensional and two-dimensional Long Short-Term Memory (LSTM) neural network models. The results were compared with real data; recovery quality was assessed using the Root Mean Square Error (RMSE) of the prediction. The study showed that two-dimensional LSTM provides more accurate data recovering, with a minimum RMSE of 0.014 ± 0.006. The simulation results prove that multidimensional recovering can significantly increase the length of the predicted series while maintaining acceptable accuracy and compensate for the error accumulation effect that is observed with one-dimensional recovering. We believe that multidimensional data recovery method based on the LSTM neural network is very promising for non-stationary time series.