A Review of Application of Machine Learning in Storm Surge Problems

Qin, Yue; Su, Changyu; Chu, Dongdong; Zhang, Jicai; Song, Jinbao

doi:10.3390/jmse11091729

Cited by 9 publications

(4 citation statements)

References 143 publications

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“…This integration, which may involve techniques like weighted averaging or model ensembling, endows the final meta-model with enhanced generalization capabilities. Meta-modeling achieves a balance between various aspects of the individual models by leveraging their strengths and mitigating their weaknesses, leading to more robust and comprehensive models ( Qin et al, 2023 ). This balance is derived from an in-depth understanding of each model’s performance and characteristics.…”

Section: Resultsmentioning

confidence: 99%

Enhanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques

Zhang,

Yang,

Wang

et al. 2024

Front. Chem.

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The component analysis of raw meal is critical to the quality of cement. In recent years, near-infrared (NIR) has been emerged as an innovative and efficient analytical method to determine the oxide content of cement raw meal. This study aims to utilize NIR spectroscopy combined with machine learning and chemometrics to improve the prediction of oxide content in cement raw meal. The Savitzky-Golay convolution smoothing method is applied to eliminate noise interference for the analysis of calcium carbonate (CaCO3), silicon dioxide (SiO2), aluminum oxide (Al2O3), and ferric oxide (Fe2O3) in cement raw materials. Different wavelength selection techniques are used to perform a comprehensive analysis of the model, comparing the performance of several wavelength selection techniques. The back-propagation neural network regression model based on particle swarm optimization algorithm was also applied to optimize the extracted and screened feature wavelengths, and the model prediction performance was checked and evaluated using Rp and RMSE. In conclusion, the results indicate that NIR spectroscopy in combination with ML and chemometrics has great potential to effectively improve the prediction performance of oxide content in raw materials and highlight the importance of modeling and wavelength selection techniques. By enabling more accurate and efficient determination of oxide content in raw materials, NIR spectroscopy coupled with meta-modeling has the potential to revolutionize quality assurance practices in cement manufacturing.

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Section: Resultsmentioning

confidence: 99%

Enhanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques

Zhang,

Yang,

Wang

et al. 2024

Front. Chem.

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“…A thorough and extensive literature review can be found in [1,71], where machine learning models are compared to traditional physically based models.…”

Section: Neural Network Ensemblementioning

confidence: 99%

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

Nezhad,

Barooni,

Velioglu Sogut

et al. 2023

JMSE

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This review paper focuses on the use of ensemble neural networks (ENN) in the development of storm surge flood models. Storm surges are a major concern in coastal regions, and accurate flood modeling is essential for effective disaster management. Neural network (NN) ensembles have shown great potential in improving the accuracy and reliability of such models. This paper presents an overview of the latest research on the application of NNs in storm surge flood modeling and covers the principles and concepts of ENNs, various ensemble architectures, the main challenges associated with NN ensemble algorithms, and their potential benefits in improving flood forecasting accuracy. The main part of this paper pertains to the techniques used to combine a mixed set of predictions from multiple NN models. The combination of these models can lead to improved accuracy, robustness, and generalization performance compared to using a single model. However, generating neural network ensembles also requires careful consideration of the trade-offs between model diversity, model complexity, and computational resources. The ensemble must balance these factors to achieve the best performance. The insights presented in this review paper are particularly relevant for researchers and practitioners working in coastal regions where accurate storm surge flood modeling is critical.

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“…For example, Fan et al [20] developed an artificial neural network-based method for calculating ship-bridge collision forces using numerical simulation data. Qin et al [21] argued that the rise of machine learning has greatly promoted the development of the oceanography field and has used Physics-informed neural networks to address storm surge issues. Kameshwar [22] predicted the collision response of bridge piles based on the response surface methodology.…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy and Fast Calculation Framework for Berthing Collision Force of Docks Based on Surrogate Models

Zeng,

Zhu,

Wang

et al. 2024

JMSE

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The accurate prediction of the collision force magnitude resulting from ship berthing on docks is crucial for the rationality and safety of dock structural design. This paper presents a novel framework for the calculation of berthing collision force for ships (CBCF), which integrates field data, finite element models, and surrogate models. Based on field data and finite element analysis, the framework constructs and compares four surrogate models with low sample requirements, ultimately selecting the optimal surrogate model for predicting collision force. Furthermore, a sensitivity analysis of the parameters is conducted based on the selected model, followed by a comparison with the various methods used for collision force prediction. The results illustrate the effectiveness of the proposed framework in replacing finite element models for the rapid and accurate prediction of collision force. Comparison with existing methods also underscores the advantages of the proposed framework, including low sample requirements, high calculation accuracy, and exceptional efficiency. In summary, this study not only introduces a novel and precise surrogate model framework for the swift prediction of berthing collision force, but it also offers valuable insights into the prevention of ship collision with wharf accidents and facilitates the rational and safe design of wharf structures.

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A Review of Application of Machine Learning in Storm Surge Problems

Cited by 9 publications

References 143 publications

Enhanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques

Enhanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques

Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review

High-Accuracy and Fast Calculation Framework for Berthing Collision Force of Docks Based on Surrogate Models

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