Short-term water level prediction of Hongze Lake by Prophet-LSTM combined model based on LAE

Du, Nannan; Xue-chun, Liang

doi:10.1109/ichceswidr54323.2021.9656315

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…The Prophet algorithm was applied to each of the five Hunnicutt stations. Twenty percent of the data for each station was reserved for testing and forecasting purposes, following similar hydrology machine learning methodologies [42][43][44].…”

Section: Prophet Algorithmmentioning

confidence: 99%

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Bolick

Post

Naser

et al. 2023

Water

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Flooding in urban streams can occur suddenly and cause major environmental and infrastructure destruction. Due to the high amounts of impervious surfaces in urban watersheds, runoff from precipitation events can cause a rapid increase in stream water levels, leading to flooding. With increasing urbanization, it is critical to understand how urban stream channels will respond to precipitation events to prevent catastrophic flooding. This study uses the Prophet time series machine learning algorithm to forecast hourly changes in water level in an urban stream, Hunnicutt Creek, Clemson, South Carolina (SC), USA. Machine learning was highly accurate in predicting changes in water level for five locations along the stream with R2 values greater than 0.9. Yet, it can be challenging to understand how these water level prediction values will translate to water volume in the stream channel. Therefore, this study collected terrestrial Light Detection and Ranging (LiDAR) data for Hunnicutt Creek to model these areas in 3D to illustrate how the predicted changes in water levels correspond to changes in water levels in the stream channel. The predicted water levels were also used to calculate upstream flood volumes to provide further context for how small changes in the water level correspond to changes in the stream channel. Overall, the methodology determined that the areas of Hunnicutt Creek with more urban impacts experience larger rises in stream levels and greater volumes of upstream water during storm events. Together, this innovative methodology combining machine learning, terrestrial LiDAR, 3D modeling, and volume calculations provides new techniques to understand flood-prone areas in urban stream environments.

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Section: Prophet Algorithmmentioning

confidence: 99%

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Bolick

Post

Naser

et al. 2023

Water

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“…Zhang et al (Zhang et al 2021) designed CNNLSTM, a deep learning hybrid model based on the Convolutional Neural Network (CNN) and LSTM models, to predict downstream water levels. Du and Liang (Du and Liang 2021) created an ensemble LSTM and Prophet model which was shown to outperform any of the single models used in the ensemble. Le et al (Le et al 2021) added an attention mechanism (Xu et al 2015;Chorowski et al 2015) to an encoderdecoder architecture to solve the hydro prediction problem.…”

Section: Related Workmentioning

confidence: 99%

An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks

Xu²,

Anastasiu

2023

AAAI

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Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction. Diverse traditional and deep learning models have been applied to discover the nonlinear relationships and recognize the complex patterns in these types of data. However, existing methods usually ignore the negative influence of imbalanced data, or severe events, on model training. Moreover, methods are usually evaluated on a small number of generally well-behaved time series, which does not show their ability to generalize. To tackle these issues, we propose a novel probability-enhanced neural network model, called NEC+, which concurrently learns extreme and normal prediction functions and a way to choose among them via selective back propagation. We evaluate the proposed model on the difficult 3-day ahead hourly water level prediction task applied to 9 reservoirs in California. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines and exhibits superior generalization ability on data with diverse distributions.

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“…The SARIMA model was then used to predict the trends and seasonal terms of sea level variations, while the LSTM was used to predict the random terms. Du et al [11] proposed a combination model for short-term water level forecasting based on the Prophet model and a long short-term memory (LSTM) network. The Prophet and LSTM models were initially constructed separately, and their results were combined by adopting different weights based on the least absolute error (LAE).…”

Section: Introductionmentioning

confidence: 99%

WLP-VBL: A Robust Lightweight Model for Water Level Prediction

Yi,

Huang,

Pan

et al. 2023

Electronics

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Accurate and reliable water level prediction plays a crucial role in the optimal management of water resources and reservoir scheduling. Water level data have the characteristics of volatility and temporality; a single water level prediction model can only be applied to specific hydrological conditions and reservoirs. Therefore, in this paper, we present a robust lightweight model for water level prediction, namely WLP-VBL, by using a combination of VMD, BA, and LSTM. The proposed WLP-VBL model consists of three steps: first, the water level dataset is decomposed by EMD to obtain a number of decomposition layers K, and then VMD is used to decompose the original water level dataset into K intrinsic modal functions (IMFs) to produce a clearer signal. Next, the IMF data are sent to an LSTM neural network optimized by BA for prediction, and finally each component is superimposed to obtain the predicted value. In order to evaluate the effectiveness of the model, experiments were carried out on water level data for the Gan River. The results indicate that: (1) Compared with state-of-the art methods, e.g., LSTM, VMD-LSTM, and EMD-LSTM, WLP-VBL exhibited the best performance. The MSE and MAE of WLP-VBL decreased by 69.6~74.7% and 45~98.5%, respectively. (2) The proposed model showed stronger robustness for water level prediction, and was able to handle highly volatile and noisy data.

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Short-term water level prediction of Hongze Lake by Prophet-LSTM combined model based on LAE

Cited by 5 publications

References 3 publications

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks

WLP-VBL: A Robust Lightweight Model for Water Level Prediction

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