| Ocean Systems Engineering Volume 7, Number 4, December 2017, pages 319-328 DOI: https://doi.org/10.12989/ose.2017.7.4.319 | |
Potential of regression models in projecting sea level variability due to climate change at Haldia Port, India | |
Thendiyath Roshni, Md. Sajid K. and Pijush Samui
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| Abstract [Buy Article] | |
| Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling. | |
| Key Words | |
| WNN; MPMR; FFNN; sea-level; statistical downscaling | |
| Address | |
| Thendiyath Roshni, Md. Sajid K. and Pijush Samui: Department of Civil Engineering, National Institute of Technology Patna, Ashok Rajpath, Bihar, India-800005 | |
