Abstract
Massive amount of water level data has been collected by using Internet of Things (IoT) techniques in the Yangtze River and other rivers. In this paper, utilizing these data to construct deep neural network models for water level prediction is focused. To achieve higher accuracy, both the factors of time and locations of data collection sensors are considered to perform prediction. And the network structures of gated recurrent unit (GRU) and convolutional neural network (CNN) are combined to build a CNN-GRU model in which the GRU part learns the changing trend of water level, and the CNN part learns the spatial correlation among water level data observed from adjacent water stations. The CNN-GRU model that using data from multiple locations to predict the water level of the middle location has higher accuracy than the model only based on GRU and other state-of-the-art methods including autoregressive integrated moving average model (ARIMA), wavelet-based artificial neural network (WANN) and long-short term memory model (LSTM), because of its ability to decrease the affections of abnormal value and data randomness of a single water station to some extent. The results are verified on an experiment dataset that including 30-year observed data of water level at several collection stations in the Yangtze River. For forecasting the 8-o'clock water levels of future 5 days, accuracy of the CNN-GRU model is better than that of ARIMA, WANN and LSTM models with three evaluation factors including Nash-Sutcliffe efficiency coefficient (NSE), average relative error (MRE) and root mean square error (RMSE).
Highlights
The water level of the inland waterway is an important factor in guiding the navigation of vessels and their reasonable loading
Behzad et al used the support vector machine (SVM) and artificial neural network (ANN) to predict groundwater level in different weathers and periods, and the results showed that SVM has more advantages than ANN in medium and long-term water level prediction, especially in the case of a small amount of data [1]
Wang et al proposed an EEMD-autoregressive integrated moving average model (ARIMA) model coupling the ARIMA and ensemble empirical mode decomposition (EEMD) to forecast annual runoff time series, and the results showed that EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting [6]
Summary
The water level of the inland waterway is an important factor in guiding the navigation of vessels and their reasonable loading. M. Pan et al.: Water Level Prediction Model Based on GRU and CNN function for intelligent prediction of the daily water level in the Yangtze River, and more accurate forecasts were obtained the improvement is regarded as moderate [2]. Lee et al proposed a short-term water level prediction model by combining neural networks and genetic algorithms (GA) for 15 water level locations in four major rivers in Korea, and the experimental results showed that the model has strong accuracy and adaptability [7]. Anh et al proposed a wavelet-artificial neural network (WAANN) model to addresses daily water level forecasting with short time, in which wavelet analysis (WA) was used to remove highfrequency random noise of time series data and ANN was used to make the short-term prediction, the results of WAANN of water level forecasting showed better performance than ANN [15].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
