Development of Deep Learning Models to Improve the Accuracy of Water Levels Time Series Prediction through Multivariate Hydrological Data

Abstract

Since predicting rapidly fluctuating water levels is very important in water resource engineering, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to evaluate water-level-prediction accuracy at Hangang Bridge Station in Han River, South Korea, where seasonal fluctuations were large and rapidly changing water levels were observed. The hydrological data input to each model were collected from the Water Resources Management Information System (WAMIS) at the Hangang Bridge Station, and the meteorological data were provided by the Seoul Observatory of the Meteorological Administration. For high-accuracy high-water-level prediction, the correlation between water level and collected hydrological and meteorological data was analyzed and input into the models to determine the priority of the data to be trained. Multivariate input data were created by combining daily flow rate (DFR), daily vapor pressure (DVP), daily dew-point temperature (DDPT), and 1-hour-max precipitation (1HP) data, which are highly correlated with the water level. It was possible to predict improved high water levels through the training of multivariate input data of LSTM and GRU. In the prediction of water-level data with rapid temporal fluctuations in the Hangang Bridge Station, the accuracy of GRU’s predicted water-level data was much better in most multivariate training than that of LSTM. When multivariate training data with a large correlation with the water level were used by the GRU, the prediction results with higher accuracy (R2=0.7480−0.8318; NSE= 0.7524−0.7965; MRPE= 0.0807−0.0895) were obtained than those of water-level prediction results by univariate training.

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

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Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model

“…They found that the LSTM-based model showed high accuracy both in the prediction of smooth streamflow in the dry season and rapidly fluctuant streamflow in the rainy season, outperforming traditional neural networks. Kidoo et al [16] created a multivariations input GRU model for the accurate prediction of water level by selecting meteorological data related to water level height at Hangang Bridge Station. Recent research has tried to propose some hybrid models to improve the accuracy and generalization of a model, such as LSTM-ALO [17], LSTM-GA [18], LSTM-INFO [19] and so on.…”

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

“…Tese models' features can receive a substantial quantity of data and can be applied to numerous climatic parameters and other hydrological boundary factors [8]. According to the published literature survey, multiple AI models for water level modelling have been built, including artifcial neural network (ANN) [9], adaptive neuro-fuzzy inference (ANFIS) [10], support vector machine (SVM) [11], and random forests (RFs) [12]. Te advantages and disadvantages of the models mentioned above (main ML models) are covered according to diferent topics of hydrology felds, such as drought [13] and water quality [14].…”