scholarly journals Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 96
Author(s):  
Wei-Ting Chao ◽  
Chih-Chieh Young
Keyword(s):  
Neural Network ◽  
Storm Surge ◽  
Hybrid Model ◽  
Lead Time ◽  
Hybrid Approach ◽  
Coastal Region ◽  
Storm Surges ◽  
Search Space ◽  
Observation Data ◽  
Local Pressure

Storm surges are one of the most devastating coastal disasters. Numerous efforts have continuously been made to achieve better prediction of storm surge variation. In this paper, we propose a parametric cyclone and neural network hybrid model for accurate, long lead-time storm surge prediction. The model was applied to the northeastern coastal region of Taiwan, i.e., Longdong station. A total of 14 historical typhoon events were used for model training and validation, and the results and questions associated with this hybrid approach carefully discussed. Overall, the proposed method reduced the complexity of network structure while retaining the important typhoon indicators. In particular, local pressure and winds estimated from the storm parameters through physically-based parametric cyclone models allow for inferring the possible future influence of a typhoon, unlike the simple collection and direct usage of observation data from local stations in earlier works. Meanwhile, the error-tolerance capability of the neural network alleviated some discrepancy in the model inputs and enabled good surge prediction. Further, the proposed method showed better and faster convergence thanks to the retention of storm information and the reduced dimensions of the search space. The hybrid model presented excellent performance or maintained reasonable capability for short lead-time and long lead-time storm surge prediction. Compared with the pure neural network model under the same network dimensions, the present model demonstrated great improvement in accuracy as the prediction lead time increased to 8 h, e.g., 33–40% (13–21%) and 32–37% (18–29%) RMSE and CE, respectively, in the training/validation phase.

scholarly journals Improving prediction accuracy of river discharge time series using a Wavelet-NAR artificial neural network

2012 ◽  
Vol 14 (4) ◽  
pp. 974-991 ◽  
Author(s):  
Shouke Wei ◽  
Depeng Zuo ◽  
Jinxi Song
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
River Discharge ◽  
Prediction Accuracy ◽  
Hybrid Approach ◽  
Series Data ◽  
Discharge Time ◽  
Artificial Neural

This study developed a wavelet transformation and nonlinear autoregressive (NAR) artificial neural network (ANN) hybrid modeling approach to improve the prediction accuracy of river discharge time series. Daubechies 5 discrete wavelet was employed to decompose the time series data into subseries with low and high frequency, and these subseries were then used instead of the original data series as the input vectors for the designed NAR network (NARN) with the Bayesian regularization (BR) optimization algorithm. The proposed hybrid approach was applied to make multi-step-ahead predictions of monthly river discharge series in the Weihe River in China. The prediction results of this hybrid model were compared with those of signal NARNs and the traditional Wavelet-Artificial Neural Network hybrid approach (WNN). The comparison results revealed that the proposed hybrid model could significantly increase the prediction accuracy and prediction period of the river discharge time series in the current case study.

Using Neural Networks Forecast the Economic Losses Caused by Storm Surge

2013 ◽  
Vol 798-799 ◽  
pp. 987-991
Author(s):  
Ling Di Zhao ◽  
Ming Ye Yang ◽  
Chun Peng Bian ◽  
Qing Hao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Storm Surge ◽  
Bp Neural Network ◽  
Storm Surges ◽  
Economic Loss ◽  
Entropy Method ◽  
Economic Conditions ◽  
Economic Losses ◽  
Grade Prediction

In order to make up for the lack of natural grade warning, we sought a new method for judging the losses of storm surges. Firstly apply entropy method etc to grade storm surges into 4 levels (mild, moderate, heavy and extra heavy) according to economic loss indices in Zhejiang Province. Then develop BP neural network to forecast losses with the selected indicators of natural, social and economic conditions. Comparing forecast grades with the actual value, we found the accuracy of grade prediction is 80%. It shown the grading results and predicting method are reliable and could be used for the grades of economic losses forecast of storm surges in future.

scholarly journals Numerical experiments of storm winds, surges, and waves on the southern coast of Korea during Typhoon Sanba: the role of revising wind force

2014 ◽  
Vol 14 (12) ◽  
pp. 3279-3295 ◽  
Author(s):  
J. J. Yoon ◽  
J. S. Shim ◽  
K. S. Park ◽  
J. C. Lee
Keyword(s):  
Storm Surge ◽  
Numerical Experiments ◽  
Complex Geometry ◽  
Numerical Models ◽  
Three Dimensional ◽  
Coastal Region ◽  
Storm Surges ◽  
Ocean Model ◽  
Wind Force ◽  
Study Results

Abstract. The southern coastal area of Korea has often been damaged by storm surges and waves due to the repeated approach of strong typhoons every year. The integrated model system is applied to simulate typhoon-induced winds, storm surges, and surface waves in this region during Typhoon Sanba in 2012. The TC96 planetary boundary layer wind model is used for atmospheric forcing and is modified to incorporate the effect of the land's roughness on the typhoon wind. Numerical experiments are carried out to investigate the effects of land-dissipated wind on storm surges and waves using the three-dimensional, unstructured grid, Finite Volume Coastal Ocean Model (FVCOM), which includes integrated storm surge and wave models with highly refined grid resolutions along the coastal region of complex geometry and topography. Compared to the measured data, the numerical models have successfully simulated storm winds, surges, and waves. Better agreement between the simulated and measured storm winds has been found when considering the effect of wind dissipation by land roughness. In addition, this modified wind force leads to clearly improved results in storm surge simulations, whereas the wave results have shown only slight improvement. The study results indicate that the effect of land dissipation on wind force plays a significant role in the improvement of water level modeling inside coastal areas.

scholarly journals Numerical experiments of storm winds, surges, and waves on the southern coast of Korea during Typhoon Sanba: the role of revising wind force

2014 ◽  
Vol 2 (8) ◽  
pp. 5315-5360
Author(s):  
J. J. Yoon ◽  
J. S. Shim ◽  
K. S. Park ◽  
J. C. Lee
Keyword(s):  
Storm Surge ◽  
Numerical Experiments ◽  
Complex Geometry ◽  
Numerical Models ◽  
Three Dimensional ◽  
Coastal Region ◽  
Storm Surges ◽  
Ocean Model ◽  
Wind Force ◽  
Study Results

Abstract. The southern coastal area of Korea has often been damaged by storm surges and waves, due to the repeated approach of strong typhoons every year. The integrated model system is applied to simulate typhoon-induced winds, storm surges, and surface waves in this region during Typhoon Sanba in 2012. The TC96 (planetary boundary layer model) wind model is used for atmospheric forcing and is modified to incorporate the effect of the land's roughness on the typhoon wind. Numerical experiments are carried out to investigate the effects of land-dissipated wind on storm surges and waves using a three dimensional, unstructured grid, Finite Volume Coastal Ocean Model (FVCOM), which includes integrated storm surge and wave models with highly refined grid resolutions along the coastal region of complex geometry and topography. Compared to the measured data, the numerical models have successfully simulated storm winds, surges, and waves. Better agreement between the simulated and measured storm winds has been found when considering the effect of wind dissipation by land roughness. In addition, this modified wind force leads to clearly improved results in storm surge simulations, whereas the wave results have shown only slight improvement. The study results indicate that the effect of land dissipation on wind force plays a significant role in the improvement of water level modeling inside coastal areas.

scholarly journals Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning

2021 ◽  
Author(s):  
Jun-Whan Lee ◽  
Jennifer Irish ◽  
Michelle Bensi ◽  
Doug Marcy
Keyword(s):  
Time Series ◽  
Tropical Cyclone ◽  
Storm Surge ◽  
Coastal Region ◽  
Surrogate Modeling ◽  
Storm Surges ◽  
High Fidelity ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Modeling Approaches

Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally e cient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.

scholarly journals Effect of Tides and Storm Surges on Storm Waves at the Northern Coastal Areas of Vietnam

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Nguyen Ba Thuy
Keyword(s):  
Storm Surge ◽  
Wave Height ◽  
Operating Time ◽  
Storm Surges ◽  
Wave Radiation ◽  
Observation Data ◽  
Ocean Engineering ◽  
Super Typhoon ◽  
Storm Waves ◽  
Storm Wave

Abstract: In this study, the effect of tides and storm surges on storm waves at the Northern coastal area of Vietnam is investigated by a coupled model of surge wave and tide (called: SuWAT). In particular, tide and storm surge are simulated by two-dimensional long wave equations taking into account the wave radiation stress, obtained from the SWAN model. The numerical was then applied to simulate storm waves and surges for typhoon Frankie (7/1996), Washi (7/2005) and Doksuri (9/2017). In the case of the super typhoon, the intensity of typhoon Washi is increased to level 16 (super typhoon level) but remains the same trajectory and operating time. The numerical results showed relatively well with observation data on storm surge and wave height. In general, the wave height is higher in the region near the coast and lower at offshore when considering the effect of tide and storm surge on storm wave. It also indicated that the effect of storm surge on storm wave is more significant than the tide. The results of the study are the basis for proposing to improve the wave forecasting technology in the study area. Keywords: Storm wave, tides, storm surge, super typhoon. References: [1] Đ. Đ. Chiến, N. B. Thủy, N.T. Sáo, T.H. Thái, S. Kim. Nghiên cứu tương tác sóng và nước dâng do bão bằng mô hình số trị, Tạp chí Khí tượng Thủy văn, 647 (2014) 19-24.[2] T.Q. Tiến, P.K. Ngọc. Kết nối mô hình SWAN với mô hình WAM thành hệ thống dự báo sóng biển cho vùng Vịnh Bắc Bộ, Tạp chí Khí tượng Thủy văn, 651 (2014) 21-26.[3] Y.Funakoshi, S.C.Hagen, P.Bacopoulos. Coupling of hydrodynamic and wave models: case study for Hurricane Floyd (1999) Hindcast, Journal of Waterway, Port, Coastal and Ocean Engineering, 134 (2008) 321 – 335.[4] S.Y. Kim, T. Yasuda, H. Mase. Wave set-up in the storm surge along open coasts during Typhoon Anita, Coastal Engineering, 57 (2010) 631-642.[5] X. Bertin, K. Li, A. Roland, and J.R. Bidlot. The contribution of short waves in storm surges: two recent examples in the central part of the bay of Biscay, Continental Shelf Research 96 (2015) 1-15.[6] H.Đ. Cường, N.B. Thủy, N.V. Hưởng, D.Đ. Tiến. Đánh giá nguy cơ bão và nước dâng do bão tại ven biển Việt Nam, Tạp chí khí tượng thủy văn, 684 (2018) 29-36.[7] Delf University of Technology. SWAN Cycle III Verion 40.31, User Guide. Delf, 2004.[8] N.B. Thủy, H.Đ. Cường, D.Đ. Tiến, Đ.Đ. Chiến, S.Kim. Đánh giá diễn biến nước biển dâng do bão số 3 năm 2014 và vấn đề dự báo, Tạp chí Khí tượng Thủy văn, 647 (2014).14-18.[9] N.B. Thuy, S. Kim, D.D. Chien, V.H. Dang, H.D. Cuong, C. Wettre and L. R. Hole. Assessment of Storm Surge along the Coast of Central Vietnam, Coastal researcher Journal, 33 (2017) 518-530.[10] V.H. Đăng, N.B. Thủy, Đ.Đ. Chiến, S. Kim. Nghiên cứu đánh giá định lượng các thành phần nước dâng trong bão bằng mô hình số trị, Tạp chí khoa học công nghệ biển. 17 (2017) 132-138.[11] T. Fujita. Pressure distribution within typhoon, Geophysical Magazine, 23 (1952). 437-451.

scholarly journals Predicting typhoon-induced storm surge tide with a two-dimensional hydrodynamic model and artificial neural network model

2012 ◽  
Vol 12 (12) ◽  
pp. 3799-3809 ◽  
Author(s):  
W.-B. Chen ◽  
W.-C. Liu ◽  
M.-H. Hsu
Keyword(s):  
Neural Network ◽  
Storm Surge ◽  
Hydrodynamic Model ◽  
East Coast ◽  
Storm Surges ◽  
Two Dimensional ◽  
Astronomical Tide ◽  
Tide Level ◽  
Surge Height ◽  
Bpnn Model

Abstract. Precise predictions of storm surges during typhoon events have the necessity for disaster prevention in coastal seas. This paper explores an artificial neural network (ANN) model, including the back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS) algorithms used to correct poor calculations with a two-dimensional hydrodynamic model in predicting storm surge height during typhoon events. The two-dimensional model has a fine horizontal resolution and considers the interaction between storm surges and astronomical tides, which can be applied for describing the complicated physical properties of storm surges along the east coast of Taiwan. The model is driven by the tidal elevation at the open boundaries using a global ocean tidal model and is forced by the meteorological conditions using a cyclone model. The simulated results of the hydrodynamic model indicate that this model fails to predict storm surge height during the model calibration and verification phases as typhoons approached the east coast of Taiwan. The BPNN model can reproduce the astronomical tide level but fails to modify the prediction of the storm surge tide level. The ANFIS model satisfactorily predicts both the astronomical tide level and the storm surge height during the training and verification phases and exhibits the lowest values of mean absolute error and root-mean-square error compared to the simulated results at the different stations using the hydrodynamic model and the BPNN model. Comparison results showed that the ANFIS techniques could be successfully applied in predicting water levels along the east coastal of Taiwan during typhoon events.

IMPACT ASSESSMENT ON STORM SURGE RISKS TO CONTAINERS CONSIDERING GLOBAL WARMING

2017 ◽  
Author(s):  
Rikito Hisamatsu ◽  
Rikito Hisamatsu ◽  
Kei Horie ◽  
Kei Horie
Keyword(s):  
Risk Assessment ◽  
Global Warming ◽  
Storm Surge ◽  
Storm Surges ◽  
Assessment Tools ◽  
Inundation Depth ◽  
Risk Assessment Tools ◽  
Risk Maps ◽  
The Impact ◽  
Vulnerability Functions

Container yards tend to be located along waterfronts that are exposed to high risk of storm surges. However, risk assessment tools such as vulnerability functions and risk maps for containers have not been sufficiently developed. In addition, damage due to storm surges is expected to increase owing to global warming. This paper aims to assess storm surge impact due to global warming for containers located at three major bays in Japan. First, we developed vulnerability functions for containers against storm surges using an engineering approach. Second, we simulated storm surges at three major bays using the SuWAT model and taking global warming into account. Finally, we developed storm surge risk maps for containers based on current and future situations using the vulnerability function and simulated inundation depth. As a result, we revealed the impact of global warming on storm surge risks for containers quantitatively.

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