Application of surrogate models in estimation of storm surge:A comparative assessment

Kajbaf, Azin Al; Bensi, Michelle

doi:10.1016/j.asoc.2020.106184

Cited by 44 publications

(22 citation statements)

References 41 publications

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“…However, results indicated an underestimation by the ANN model of the peak storm surge values. The underprediction of storm surge by ANNs has also been shown in other studies (Al Kajbaf & Bensi, 2020; Hashemi et al., 2016; Sahoo & Bhaskaran, 2019). Errors were seen to decrease (increase) in proportion to a decrease (increase) in the target response (Al Kajbaf & Bensi, 2020).…”

Section: Discussionsupporting

confidence: 79%

“…The underprediction of storm surge by ANNs has also been shown in other studies (Al Kajbaf & Bensi, 2020; Hashemi et al., 2016; Sahoo & Bhaskaran, 2019). Errors were seen to decrease (increase) in proportion to a decrease (increase) in the target response (Al Kajbaf & Bensi, 2020). Several reasons are proposed here to explain this behavior.…”

Section: Discussionsupporting

confidence: 79%

“…ML methods have shown to be suitable for this type of application, achieving higher accuracy than the traditional forecasting methods mentioned previously (Mosavi et al., 2018). Studies have compared the use of these different methods for flood forecasting (Al Kajbaf & Bensi, 2020; Hashemi et al., 2016) and have highlighted the consistency in performance of the different models.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of an Artificial Neural Network for Storm Surge Forecasting

et al. 2021

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Accurate and timely storm surge forecasts are essential during tropical cyclone events in order to assess the magnitude and location of the impacts. Coupled ocean‐atmosphere dynamical models provide accurate measures of storm surge but remain too computationally expensive to run for real‐time forecasting purposes. Therefore, it is common to utilize a parametric vortex model, implemented within a hydrodynamic model, which decreases computational time at the expense of forecast accuracy. Recently, data‐driven neural networks are being implemented as an alternative due to their combined efficiency and high accuracy. This work seeks to examine how an artificial neural network (ANN) can be used to make accurate storm surge predictions, and explores the added value of using a recurrent neural network (RNN). In particular, it is concerned with determining the parameters needed to successfully implement a neural network model for the Mid‐Atlantic Bight region. The neural network models were trained with modeled data resulting from coupling of the Hybrid Weather Research and Forecasting cyclone model (HWCM) and the Advanced Circulation Model. An ensemble of synthetic, but physically plausible, cyclones were simulated using the HWCM and used as input for the hydrodynamic model. Tests of the ANN were conducted to investigate the optimal lead‐time configuration of the input data and the neural network architecture needed to minimize storm surge forecast errors. Results highlight the accuracy of the ANN in forecasting moderate storm surge levels, while indicating a deficiency in capturing the magnitude of the peak values, which is improved in the implementation of the RNN.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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Implementation of an Artificial Neural Network for Storm Surge Forecasting

et al. 2021

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“…Typically, the performance and accuracy of the trained emulators are assessed through aggregated error metrics such as root mean squared error (RMSE) and correlation coefficient (R). Al Kajbaf and Bensi [21] showed that aggregated error metrics give incomplete measures of the performance, and the accuracy of the models must be assessed beyond these metrics.…”

Section: Introductionmentioning

confidence: 99%

Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR

Alipour

Leal

2021

Water

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Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results; however, they are computationally costly and therefore unsuitable for many real time applications and uncertainty analysis that requires a large number of model realizations. Therefore, the present study aims to (i) develop emulators based on SVR and ANN as an alternative for predicting the 100-year flood water level, (ii) improve the performance of the emulators through dimensionality reduction techniques, and (iii) assess the required training sample size to develop an accurate emulator. Our results indicate that SVR based emulator is a fast and reliable alternative that can predict the water level accurately. Moreover, the performance of the models can improve by identifying the most influencing input variables and eliminating redundant inputs from the training process. The findings in this study suggest that the training data size equal to 70% (or more) of data results in reliable and accurate predictions.

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“…A large proportion of research indicates that the ensemble of surrogates can provide more accurate and robust results. Therefore, successful engineering applications using ensembles of surrogates are continuously reported [21][22][23][24][25][26]. Hamza and Saitou [21] presented a multi-scenario co-evolutionary genetic algorithm (MSCGA) for vehicle structural crashworthiness via an ensemble of surrogates.…”

Section: Introductionmentioning

confidence: 99%

Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model

Pan

2020

Mathematics

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Surrogate modeling techniques are widely used to replace the computationally expensive black-box functions in engineering. As a combination of individual surrogate models, an ensemble of surrogates is preferred due to its strong robustness. However, how to select the best quantity and variety of surrogates for an ensemble has always been a challenging task. In this work, five popular surrogate modeling techniques including polynomial response surface (PRS), radial basis functions (RBF), kriging (KRG), Gaussian process (GP) and linear shepard (SHEP) are considered as the basic surrogate models, resulting in twenty-six ensemble models by using a previously presented weights selection method. The best ensemble model is expected to be found by comparative studies on prediction accuracy and robustness. By testing eight mathematical problems and two engineering examples, we found that: (1) in general, using as many accurate surrogates as possible to construct ensemble models will improve the prediction performance and (2) ensemble models can be used as an insurance rather than offering significant improvements. Moreover, the ensemble of three surrogates PRS, RBF and KRG is preferred based on the prediction performance. The results provide engineering practitioners with guidance on the superior choice of the quantity and variety of surrogates for an ensemble.

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Application of surrogate models in estimation of storm surge:A comparative assessment

Cited by 44 publications

References 41 publications

Implementation of an Artificial Neural Network for Storm Surge Forecasting

Implementation of an Artificial Neural Network for Storm Surge Forecasting

Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR

Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model

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