Parameterization and application of storm surge/tide modeling using a genetic algorithm for typhoon periods

You, Sung Hyup; Lee, Yong Hee; Lee, Woo Jeong

doi:10.1007/s00376-011-0113-9

Cited by 4 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genetic Algorithm (GA) is a method of searching for optimal solutions by simulating natural evolutionary processes. Genetic algorithms are robust to model complexity and non-linearity, thus providing a more flexible and straightforward parameter estimation method [111]. Genetic Programming (GP) is a branch of genetic algorithms specializing in symbolic regression that can find both the functional form of a model and the numerical coefficients.…”

Section: Genetic Algorithms and Genetic Programmingmentioning

confidence: 99%

“…For the optimization of the model's parameters, though some progress has been made in the earth system sciences [25], few studies were conducted to investigate the capability of ML models in improving parameterization schemes of storm surge models. The only study we can find is You et al [111] applied GA to optimize four parameters, including the bottom drag coefficient, the background horizontal diffusivity, Smagorinski's horizontal viscosity, and the sea level pressure scaling of the 2D storm surge model. It was shown that the model accuracies in terms of RMSE were improved by 30% (mean) and 54% (median) over the default case, owing to the preeminent nonlinear fitting ability of ML.…”

Section: Ml-aided Optimization Of Numerical Model Parameterizationmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Application of Machine Learning in Storm Surge Problems

Qin,

Su,

Chu

et al. 2023

JMSE

View full text Add to dashboard Cite

The rise of machine learning (ML) has significantly advanced the field of coastal oceanography. This review aims to examine the existing deficiencies in numerical predictions of storm surges and the effort that has been made to improve the predictive accuracy through the application of ML. The readers are guided through the steps required to implement ML algorithms, from the first step of formulating problems to data collection and determination of input features to model selection, development and evaluation. Additionally, the review explores the application of hybrid methods, which combine the bilateral advantages of data-driven methods and physics-based models. Furthermore, the strengths and limitations of ML methods in predicting storm surges are thoroughly discussed, and research gaps are identified. Finally, we outline a vision toward a trustworthy and reliable storm surge forecasting system by introducing novel physics-informed ML techniques. We are meant to provide a primer for beginners and experts in coastal ocean sciences who share a keen interest in ML methodologies in the context of storm surge problems.

show abstract

Section: Genetic Algorithms and Genetic Programmingmentioning

confidence: 99%

Section: Ml-aided Optimization Of Numerical Model Parameterizationmentioning

confidence: 99%

A Review of Application of Machine Learning in Storm Surge Problems

Qin,

Su,

Chu

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…In 2004, Alvarez et al 22 successfully constructed a prediction model for the Ligurian Sea SST and sea level anomalies using the GA algorithm. You et al 23 used a GA to optimize the parameters of a two-dimensional storm surge calculation model, thereby improving the sea level prediction results. Wang et al 24 used a GA to optimize the parameters of a wavelet neural network for non-astronomical tide forecasting.…”

Section: Introductionmentioning

confidence: 99%

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Li,

Zhou,

Wang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change.

show abstract

Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques

Sahoo

Bhaskaran

2019

Soft Comput

View full text Add to dashboard Cite

Parameterization and application of storm surge/tide modeling using a genetic algorithm for typhoon periods

Cited by 4 publications

References 14 publications

A Review of Application of Machine Learning in Storm Surge Problems

A Review of Application of Machine Learning in Storm Surge Problems

An improved sparrow search algorithm and CNN-BiLSTM neural network for predicting sea level height

Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques

Contact Info

Product

Resources

About