Natural Hazards

, Volume 82, Issue 1, pp 471–491 | Cite as

An efficient artificial intelligence model for prediction of tropical storm surge

  • M. Reza Hashemi
  • Malcolm L. Spaulding
  • Alex Shaw
  • Hamed Farhadi
  • Matt Lewis
Original Paper

Abstract

Process-based models have been widely used for storm surge predictions, but their high computational demand is a major drawback in some applications such as rapid forecasting. Few efforts have been made to employ previous databases of synthetic/real storms and provide more efficient surge predictions (e.g. using storm similarity of an individual storm to those in the database). Here, we develop an alternative efficient and robust artificial intelligent model, which predicts the peak storm surge using the tropical storm parameters: central pressure, radius to maximum winds, forward velocity, and storm track. The US Army Corp of Engineers, North Atlantic Comprehensive Coastal Study, has recently performed numerical simulations of 1050 synthetic tropical storms, which statistically represent tropical storms, using a coupled high resolution wave–surge modeling system for the east coast of the US, from Cape Hatteras to the Canadian border. This study has provided an unprecedented dataset which can be used to train artificial intelligence models for surge prediction in those areas. While numerical simulation of a storm surge at this scale and resolution (over 6 million elements scaling from 20 m to more than 100 km) is extremely expensive, the artificial intelligence takes the advantage of the previous simulations, and effectively learns the relationship between storm parameters representing storm forcing and surge. The artificial neural network method which was used for this study, was shown to outperform support vector machine for extreme storms. ANN model, which is based on a neurobiological analogy, can be conveniently developed, retrained by new data, and is nonparametric. The AI model, which was developed for Rhode Island, was validated using a set of randomly selected synthetic storms as well as real tropical storms in this region. The model performance was found satisfactory with root-mean-square error of <35 cm for observed and synthetic storms. It was also shown that it is not possible to develop a reliable artificial intelligence model for this region using a limited number of data (e.g. 200 storms), which is usually available in historical records.

Keywords

Storm surge Artificial neural networks Hurricane Rhode Island NACCS 

References

  1. Aggarwal K, Singh Y, Chandra P, Puri M (2005) Bayesian regularization in a neural network model to estimate lines of code using function points. J Comput Sci 1(4):505–509CrossRefGoogle Scholar
  2. As-Salek JA (1998) Coastal trapping and funneling effects on storm surges in the Meghna estuary in relation to cyclones hitting Noakhali-Cox’s Bazar coast of Bangladesh. J Phys Oceanogr 28(2):227–249CrossRefGoogle Scholar
  3. Bajo M, Umgiesser G (2010) Storm surge forecast through a combination of dynamic and neural network models. Ocean Model 33(1):1–9CrossRefGoogle Scholar
  4. Benardos P, Vosniakos G-C (2007) Optimizing feedforward artificial neural network architecture. Eng Appl Artif Intell 20(3):365–382CrossRefGoogle Scholar
  5. Cialone MA, Massey TC, Anderson ME, Grzegorzewski AS, Jensen RE, Cialone A, Mark DJ, Pevey KC, Gunkel BL, McAlpin TO, Nadal-Caraballo NC, Melby JA, Ratcliff JJ (2015) North Atlantic Coast Comprehensive Study (NACCS) coastal storm model simulations: waves and water levels. Technical report, Coastal and Hydraulics Laboratory, US Army Corps of Engineers, Engineer Research and Development Center, VicksburgGoogle Scholar
  6. Condon AJ, Sheng YP (2012) Evaluation of coastal inundation hazard for present and future climates. Nat Hazards 62(2):345–373CrossRefGoogle Scholar
  7. Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309(1):229–240CrossRefGoogle Scholar
  8. Das HS, Jung H, Ebersole B, Wamsley T, Whalin RW (2011) An efficient storm surge forecasting tool for coastal mississippi. Coast Eng Proc 1(32):21Google Scholar
  9. De Oliveira MM, Ebecken NFF, De Oliveira JLF, de Azevedo Santos I (2009) Neural network model to predict a storm surge. J Appl Meteorol Climatol 48(1):143–155CrossRefGoogle Scholar
  10. Fausett LV, Hall P (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Englewood CliffsGoogle Scholar
  11. Ferrarin C, Zaggia L, Paschini E, Scirocco T, Lorenzetti G, Bajo M, Penna P, Francavilla M, DAdamo R, Guerzoni S (2014) Hydrological regime and renewal capacity of the micro-tidal Lesina Lagoon, Italy. Estuar Coasts 37(1):79–93CrossRefGoogle Scholar
  12. Friedrichs F, Igel C (2005) Evolutionary tuning of multiple SVM parameters. Neurocomputing 64:107–117CrossRefGoogle Scholar
  13. Galarneau TJ Jr, Davis CA, Shapiro MA (2013) Intensification of Hurricane Sandy (2012) through extratropical warm core seclusion. Mon Weather Rev 141(12):4296–4321CrossRefGoogle Scholar
  14. Govindaraju RS, Rao AR (2010) Artificial neural networks in hydrology. Springer, BerlinGoogle Scholar
  15. Hashemi M, Ghadampour Z, Neill S (2010) Using an artificial neural network to model seasonal changes in beach profiles. Ocean Eng 37(14):1345–1356CrossRefGoogle Scholar
  16. Hastie T, Tibshirani R, Friedman J, Hastie T, Friedman J, Tibshirani R (2009) The elements of statistical learning. Springer, BerlinCrossRefGoogle Scholar
  17. Haykin S (2004) Neural networks: a comprehensive foundation. Pearson Prentice HallGoogle Scholar
  18. Ho FP, Myers VA (1975) Joint probability method of tide frequency analysis applied to Apalachicola Bay and St. George Sound, FloridaGoogle Scholar
  19. Holland G (2008) A revised hurricane pressure–wind model. Mon Weather Rev 136(9):3432–3445CrossRefGoogle Scholar
  20. Holland GJ (1980) An analytic model of the wind and pressure profiles in hurricanes. Mon Weather Rev 108(8):1212–1218CrossRefGoogle Scholar
  21. Hsieh WW, Tang B (1998) Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull Am Meteorol Soc 79:1855–1870CrossRefGoogle Scholar
  22. Irish JL, Song YK, Chang K-A (2011) Probabilistic hurricane surge forecasting using parameterized surge response functions. Geophys Res Lett 38:1–5. doi:10.1029/2010GL046347 CrossRefGoogle Scholar
  23. Lee T-L (2006) Neural network prediction of a storm surge. Ocean Eng 33(3):483–494CrossRefGoogle Scholar
  24. Lee T-L (2008) Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan. Eng Appl Artif Intell 21(1):63–72CrossRefGoogle Scholar
  25. Lewis M, Horsburgh K, Bates P (2014) Bay of Bengal cyclone extreme water level estimate uncertainty. Nat Hazards 72(2):983–996CrossRefGoogle Scholar
  26. Lewis M, Bates P, Horsburgh K, Neal J, Schumann G (2013) A storm surge inundation model of the northern Bay of Bengal using publicly available data. Q J R Meteorol Soc 139(671):358–369CrossRefGoogle Scholar
  27. Lin N, Emanuel K, Smith J, Vanmarcke E (2010) Risk assessment of hurricane storm surge for New York city. J Geophys Res Atmos (1984–2012) 115:1–11. doi:10.1029/2009JD013630 Google Scholar
  28. Luettich Jr R, Westerink J, Scheffner NW (1992) ADCIRC: an advanced three-dimensional circulation model for Shelves, Coasts, and Estuaries. Report 1. Theory and methodology of ADCIRC-2DDI and ADCIRC-3DL. Technical report, DTIC documentGoogle Scholar
  29. Malekmohamadi I, Ghiassi R, Yazdanpanah M (2008) Wave hindcasting by coupling numerical model and artificial neural networks. Ocean Eng 35(3):417–425CrossRefGoogle Scholar
  30. Mekanik F, Imteaz M, Gato-Trinidad S, Elmahdi A (2013) Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503:11–21CrossRefGoogle Scholar
  31. Pawlowicz R, Beardsley B, Lentz S (2002) Classical tidal harmonic analysis including error estimates in MATLAB using T\_TIDE. Comput Geosci 28(8):929–937CrossRefGoogle Scholar
  32. Quinn N, Lewis M, Wadey M, Haigh I (2014) Assessing the temporal variability in extreme storm-tide time series for coastal flood risk assessment. J Geophys Res Oceans 119(8):4983–4998CrossRefGoogle Scholar
  33. Rajasekaran S, Gayathri S, Lee T-L (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35(16):1578–1587CrossRefGoogle Scholar
  34. Smith JM, Resio DT, Zundel AK (1999) STWAVE: steady-state spectral wave model. Report 1: user’s manual for STWAVE version 2.0. Technical report, DTIC documentGoogle Scholar
  35. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222CrossRefGoogle Scholar
  36. Spaulding MLL (2014) STORMTOOLS: web tools to support coastal resilience analysis and planning for storms and sea level rise. Technical report, Coastal Resources Center. University of Rhode Island. www.beachsamp.org/maps/stormtools
  37. Spaulding ML, Isaji T, Damon C, Fugate G (2015) Applications of STORMTOOLS simplified flood inundation model, with and without sea level rise, to RI coastal waters. In: ASCE Solutions to Coastal Disasters, Boston, MAGoogle Scholar
  38. Toro GR, Resio DT, Divoky D, Niedoroda AW, Reed C (2010) Efficient joint-probability methods for hurricane surge frequency analysis. Ocean Eng 37(1):125–134CrossRefGoogle Scholar
  39. Vapnik V (2000) The nature of statistical learning theory. Springer, BerlinCrossRefGoogle Scholar
  40. Wamdi-Group (1988) The WAM model-a third generation ocean wave prediction model. J Phys Oceanogr 18(12):1775–1810Google Scholar
  41. Yegnanarayana B (2009) Artificial Neural Networks. Prentice-Hall of IndiaGoogle Scholar
  42. Yoon H, Jun S-C, Hyun Y, Bae G-O, Lee K-K (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1):128–138CrossRefGoogle Scholar
  43. Yu P-S, Chen S-T, Chang I-F (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3):704–716CrossRefGoogle Scholar
  44. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14(1):35–62CrossRefGoogle Scholar
  45. Zhang K, Douglas BC, Leatherman SP (2000) Twentieth-century storm activity along the us east coast. J Clim 13(10):1748–1761CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • M. Reza Hashemi
    • 1
  • Malcolm L. Spaulding
    • 1
  • Alex Shaw
    • 1
  • Hamed Farhadi
    • 2
  • Matt Lewis
    • 3
  1. 1.Department of Ocean Engineering and Graduate School of OceanographyUniversity of Rhode IslandNarragansettUSA
  2. 2.Department of Water EngineeringFerdowsi University of MashhadMashhadIran
  3. 3.School of Ocean SciencesBangor UniversityBangorUK

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