Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition

Chen, Yen‐Chang; Yeh, Hui‐Chung; Kao, Sung‐Shuo; Wei, Chiang; Su, Pei-Yi

doi:10.3390/hydrology10020047

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…By comparing the decomposition effects of EMD, EEMD, and CEEMD, it was found that the CEEMD model not only overcame the error caused by EMD mode aliasing but also significantly reduced the reconstruction error of EEMD by adding complementary white noise sequences, resulting in the best decomposition effect. This is also an important reason why some recent studies on hydrology 41 and water environment 23 have recommended the use of CEEMD method for data decomposition.…”

Section: Discussionmentioning

confidence: 99%

A novel RF-CEEMD-LSTM model for predicting water pollution

Ruan,

Cui,

Song

et al. 2023

Sci Rep

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Accurate water pollution prediction is an important basis for water environment prevention and control. The uncertainty of input variables and the nonstationary and nonlinear characteristics of water pollution series hinder the accuracy and reliability of water pollution prediction. This study proposed a novel water pollution prediction model (RF-CEEMD-LSTM) to improve the performance of water pollution prediction by combining advantages of the random forest (RF) and Long short-term memory (LSTM) models and Complementary ensemble empirical mode decomposition (CEEMD). The experimental results based on measured data show that the proposed RF-CEEMD-LSTM model can accurately predict water pollution trends, with a mean ab-solute percentage error (MAPE) of less than 8%. The RMSE of the RF-CEEMD-LSTM model is reduced by 62.6%, 39.9%, and 15.5% compared to those of the LSTM, RF-LSTM, and CEEMD-LSTM models, respectively, proving that the proposed method has good advantages in predicting non-linear and nonstationary water pollution sequences. The driving force analysis results showed that TN has the most significant impact on water pollution prediction. The research results could provide references for identifying and explaining water pollution variables and improving water pollution prediction method.

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Section: Discussionmentioning

confidence: 99%

A novel RF-CEEMD-LSTM model for predicting water pollution

Ruan,

Cui,

Song

et al. 2023

Sci Rep

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show abstract

“…The results showed that the GEP model presents better performance compared with the ANN and Muskingum models for multiple inflows system. Chen et al [25] developed a new model applying ensemble empirical mode decomposition (EEMD) and stepwise regression for water level forecasting in a tidal river. Only water level data were used in the proposed model, and it was found that the model is simple and highly accurate.…”

Section: Other ML Methodsmentioning

confidence: 99%

Review of Machine Learning Methods for River Flood Routing

Li,

Jun

2024

Water

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River flood routing computes changes in the shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models, require a high quality and quantity of input data, such as measured hydrologic time series, geometric data, hydraulic structures, and hydrological parameters. Unlike physically based models, machine learning algorithms, which are data-driven models, do not require much knowledge about underlying physical processes and can identify complex nonlinearity between inputs and outputs. Due to their higher performance, lower complexity, and low computation cost, researchers introduced novel machine learning methods as a single application or hybrid application to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing.

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“…Yen-Chang Chen et al [80] Ensemble Empirical Mode Decomposition (EEMD) and a stepwise regression model was introduced to forecast the water level of tidal river sources…”

Section: Author Proposed Methods Advantage Limitationsmentioning

confidence: 99%

A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models

Bhansali,

Narasimhulu,

Pérez de Prado

et al. 2023

Energies

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Today, methodologies based on learning models are utilized to generate precise conversion techniques for renewable sources. The methods based on Computational Intelligence (CI) are considered an effective way to generate renewable instruments. The energy-related complexities of developing such methods are dependent on the vastness of the data sets and number of parameters needed to be covered, both of which need to be carefully examined. The most recent and significant researchers in the field of learning-based approaches for renewable challenges are addressed in this article. There are several different Deep Learning (DL) and Machine Learning (ML) approaches that are utilized in solar, wind, hydro, and tidal energy sources. A new taxonomy is formed in the process of evaluating the effectiveness of the strategies that are described in the literature. This survey evaluates the advantages and the drawbacks of the existing methodologies and helps to find an effective approach to overcome the issues in the existing methods. In this study, various methods based on energy conversion systems in renewable source of energies like solar, wind, hydro power, and tidal energies are evaluated using ML and DL approaches.

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Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition

Cited by 6 publications

References 31 publications

A novel RF-CEEMD-LSTM model for predicting water pollution

A novel RF-CEEMD-LSTM model for predicting water pollution

Review of Machine Learning Methods for River Flood Routing

A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models

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