Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM: A Case Study in Fuzhou City, China

Liu, Yu; Wang, Hao; Feng, Wenwen; Huang, Haocheng

doi:10.3390/ijerph18179287

Cited by 23 publications

(8 citation statements)

References 37 publications

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“…For example, Liu Yu et al [4] proposed a real-time rolling prediction system of urban river water levels in order to reduce the risk of flooding in urban rivers. Liu Yu et al [5] proposed a long short-term memory (LSTM)-based method for real-time rolling prediction of short-term water levels in inner and outer urban rivers, which was verified in a case study in Fuzhou City, China. Berkhahn et al [6] presented an artificial neural network-based model to predict the maximum water levels during a flash flood event.…”

Section: Introductionmentioning

confidence: 90%

Real-Time Water Level Prediction in Open Channel Water Transfer Projects Based on Time Series Similarity

Zhou

Zhang

et al. 2022

Water

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Changes in the opening of gates in open channel water transfer projects will cause fluctuations in the water level and flow of adjacent open channels and thus bring great challenges for real-time water level prediction. In this paper, a novel slope-similar shape method is proposed for real-time water level prediction when the change of gate opening at the next moment is known. The water level data points of three consecutive moments constitute the query. The slope similarity is used to find the historical water level datasets with similar change trend to the query, and then the best slope similarity dataset is determined according to the similarity index and the gate opening change. The water level difference of the next moment of the best similar data point is the water level difference of the predicted moment, and thus the water level at the next moment can be obtained. A case study is performed with the Middle Route of the South-to-North Water Diversion Project of China. The results show that 87.5% of datasets with a water level variation of less than 0.06 m have an error less than 0.03 m, 71.4% of which have an error less than 0.02 m. In conclusion, the proposed method is feasible, effective, and interpretable, and the study provides valuable insights into the development of scheduling schemes.

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Section: Introductionmentioning

confidence: 90%

Real-Time Water Level Prediction in Open Channel Water Transfer Projects Based on Time Series Similarity

Zhou

Zhang

et al. 2022

Water

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“…Nevertheless, there was a study to predict river flow rates with high accuracy by providing guidelines for input training data [4]. In addition, real-time groundwater-level prediction [29], extreme water-level prediction by highest tide [30,31], and real-time urban-river-flood-level prediction, using various DNN models, have been effectively performed recently [32]. However, until recently, there were not many studies to improve the accuracy of predicting water levels with very high temporal variability.…”

Section: Introductionmentioning

confidence: 99%

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

Park

Jung

Seong

et al. 2022

Water

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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.

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“…In recent years, wavelet analysis has received a great deal of attention among engineers and mathematicians [9]. In contrast to Fourier analysis, wavelet analysis has two different mathematical theoretical bases, one is the wavelet integral transform, a convolution operation on some elementary wavelet function, and the other is the wavelet level, which uses a single wavelet function and represents it by adding a binary expansion and a translation of the integral [10].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Regulation Model of Physical Fitness Training Intensity Based on Wavelet Recursive Fuzzy Neural Network

Shen

Yang

2022

Computational Intelligence and Neuroscience

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It has been widely used in signal processing, image processing, speech recognition and synthesis, pattern recognition, machine vision, machinery fault diagnosis and monitoring, and other scientific and technological fields and has achieved great results. The application potential in nonlinear system identification is increasing. According to the theory of “overload recovery” and “functional reserve”, the mathematical model of “load-fitness state” is established to understand the adaptation characteristics and individual characteristics of athletes to sports training. The model is used to simulate the values and time required to reach the maximum fitness state for four types of precompetition reduction plans and to provide a reference for the development of precompetition training plans. The data required for parameter estimation were the actual training data of six outstanding basketball athletes (mean age 18.2 ± 0.75, mean training years 4.6 ± 0.49). And the coaches’ training plan was not intervened during the test. In order to further reduce the biaxial synchronization error of the sports platform and improve the stability of the system, the wavelet transformation capable of time-varying signal analysis and the recursive structure with dynamic capability were combined with the fuzzy neural network, and the learning ability of the neural network was used to learn and adjust the scaling and translation factors in the wavelet function, the mean and standard deviation in the fuzzy structure, and the connection weights between the layers, according to the biaxial synchronization. The simulation results show that the designed global sliding mode controller can improve the convergence speed of tracking error and ensure the single-axis tracking accuracy of the H-type motion platform compared with the traditional sliding mode controller, and the tracking accuracy and synchronization accuracy of the system can be further improved after adding the cross-coupled synchronization controller, but the improvement of synchronization control accuracy is not very satisfactory due to the fixed selection of the parameters of the cross-coupled controller. Further improvement is needed.

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Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM: A Case Study in Fuzhou City, China

Cited by 23 publications

References 37 publications

Real-Time Water Level Prediction in Open Channel Water Transfer Projects Based on Time Series Similarity

Real-Time Water Level Prediction in Open Channel Water Transfer Projects Based on Time Series Similarity

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

Real-Time Regulation Model of Physical Fitness Training Intensity Based on Wavelet Recursive Fuzzy Neural Network

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