Abstract
The estimation of the monthly mean sea-level variations is a key issue in coastal regions’ planning due to their vulnerability to sea-level changes. The objective of this study is to test the digital spatial data and their accuracy for investigation of sea-level-change impact on the coastal area of New York City. Recent investigations show that there is a meaningful correlation between climatic signals’ variations such as sea-level pressure (SLP) and sea-surface temperature (SST), and sea-level changes. In this study, data-driven models are used to explore this relationship over the eastern coastal line of New York. The considered climatic signals are SST and SLP at the nine grid points (with \(2.5^{\circ } \times 2.5^{\circ }\) resolution) around the study region. The most effective predictors of sea-level changes are identified through correlation analysis. Different models of ANFIS (adaptive-network- based FIS) and three artificial neural networks (ANNs) including a multilayer perceptron (MLP), Elman recurrent neural network, and a special class of time-delay neural network (TDNN)—namely IDNN—are used to simulate sea-level changes. An MLP is also developed to hybridize the models’ outputs and provide more accurate estimation of sea level. Even though the results show that the hybrid model can significantly improve the accuracy of sea-level predictions, because of the uncertainties associated with development of the flood maps and evacuation zones, the impact study of sea-level fluctuation remains as a challenge.
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This paper is dedicated to the late Mr. Mehdi Kia who died at age 30 from liver disease. He will be remembered and we cherish his dedication to science and engineering.
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Karamouz, M., Kia, M. & Nazif, S. Prediction of Sea Level Using a Hybrid Data-Driven Model: New Challenges After Hurricane Sandy. Water Qual Expo Health 6, 63–71 (2014). https://doi.org/10.1007/s12403-014-0119-5
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DOI: https://doi.org/10.1007/s12403-014-0119-5