Performance Evaluation for Mean Sea Level Prediction using Multivariate Adaptive Regression Spline and Artificial Neural Network
Keywords:
Mean Sea Level, Artificial Neural Network, Multivariate Adaptive Regression SplineAbstract
Mean sea level (MSL) has been used as a vertical datum for geodetic levelling and mapping in most countries all over the world. This is because the MSL approximates the geoid and serves as a realist reference surface that could be determined mostly through tide measurements over a period of time. However, sea levels have been rising over the years due to global warming and its associated climate change which continuous to melt ice sheets around the Polar Regions. This phenomenon is likely to affect the reliability of MSL, thus it is important to determine the local MSL at regular time periods. This study assessed the performance of Artificial Neural Network (ANN) and Multivariate Adaptive Regression Spline (MARS) models in predicting the MSL. Tide gauge records from the Takoradi Harbour of Ghana were used in the study. Monthly maximum, minimum and mean tidal values were derived from the secondary data and used for both model formulation and model testing. A comparative analysis of both models showed that the ANN model performed better than the MARS model. A Root Mean Square Error (RMSE) of 0.0359 m was obtained for the ANN model, whereas 0.0555 m was obtained for the MARS model. Mean Absolute Percentage Error (MAPE) of 3.1414% was obtained for the ANN model and whereas the MARS model yielded 5.6349%. A Mean Absolute Error (MAE) for the ANN model was 0.0284 m as against 0.0446 m for the MARS model. Correlation coefficient values of 0.9720 and 0.8874 were obtained for the ANN model and the MARS model respectively. An optimum ANN structure was found to be ANN 2-11-1. Based on the outcome of this study, it is recommended that ANN model should be adopted for forecasting local mean sea level for the study area.
References
Alreja, J., Parab, S., Mathur, S. and Samui, P. (2015), “Estimating Hysteretic Energy Demand in Steel Moment Resisting Frames Using Multivariate Adaptive Regression Spline and Least Square Support Vector Machineâ€, Ain Shams Engineering Journal, pp. 2-6.
Anon. (2017), “Mean Sea Levelâ€, https://en.m.wikipedia.org/wiki/sea-level. Accessed: April 13, 2017.
Chang, L.Y. (2014), “Analysis of Bilateral Air Passenger Flows: A Non-Parametric Multivariate Adaptive Regression Spline Approachâ€, Journal of Air Transport Management, Vol. 34, pp. 123-130.
Domenico, M.D., Ghorbani, M.A., Makarynskyy, O., Makarynska, D. and Asadi,H. (2013), “Chaos and Reproduction in Sea Levelâ€, Applied Mathematical Modelling, Vol. 37, pp. 3687-3697.
Filippo, A., Torres, A.R., Kjerfve, B. and Monat, A. (2012), “Application of Artificial Neural Network (ANN) to improve Forecasting of Sea Levelâ€, Ocean and Coastal Management, Vol. 55, pp. 101-110.
Ghorbani, M.A., Khatibi, R., Aytek, A., Makarynskyy, O. and Shiri, J. (2010), “Sea Water Level Forecasting Using Genetic Programming and Comparing the Performance with Artificial Neural Networksâ€, Computers and Geosciences, Vol. 36, pp. 620-627.
Jain, A.K., Mao, J. and Mohiuddin, K.M., (1996), “Artificial neural networks: A tutorialâ€. Computer, 29(3), pp.31-44.
Karimi, S., Kisi, O., Shiri, J. and Makarynskyy, O. (2013), “Neuro-Fuzzy and Neural Networks Techniques for Forecasting Sea Level in Darwin Harbor, Australiaâ€, Computers and Geosciences, Vol. 52, pp.50-59.
Kisi, O. and Parmar, K.S. (2015), “Application of Least Square Support Vector Machine and Multivariate Adaptive Regression Spline Models in Long Term Prediction of River Water Pollutionâ€, Journal of Hydrology, Vol. 45, pp.1-15.
KiÅŸi, Ö., (2007), “Streamflow forecasting using different artificial neural network algorithmsâ€, Journal of Hydrologic Engineering, 12(5), pp.532-539
Koivo, H.N. (2008), “Neural Networks: Basics Using Matlab Neural Network Toolboxâ€, Author Website, 59 pp.
Lu, Z., Qu, Y. and Qiao, S. (2014), Introduction to Geodetic Datum and Geodetic Systems, Berlin, Heidelberg: Springer Berlin Heidelberg, 401pp.
Makarynska, D. and Makarynskyy, O. (2008), Predicting Sea Level Variations at the Cocos (Keeling) Island with Artificial Neural Networkâ€, Computers and Geosciences, Vol. 34, pp.1910-1917.
Makarynskyy, O., Makarynska, D., Kuhn, M. and Featherstone, W.E. (2004), “Predicting Sea Level Variations with Artificial Neural Networks at Hillarys Boat Harbour, Western Australia, Estuarine Coastal and Shelf Sciences, Vol. 61, pp.351-360.
Makarynskyy, O., Makarynska, D., Kuhn, M. and Featherstone, W.E. (2004), “Predicting Sea Level Variations with Artificial Neural Networks at Hillarys Boat Harbour, Western Australia, Estuarine Coastal and Shelf Sciences, Vol. 61, pp.351-360.
Samui, P. and Kim, D. (2012), “Modelling of Reservoir-Induced Earthquakes: A Multivariate Adaptive Regression Splineâ€, Journal of Geophysics and Engineering, Vol. 9, pp.494-497.
Shetty, R. and Dwarakish, G.S. (2013), “Prediction of Tides Using Neural Networks at Karwar, West Coast of Indiaâ€, Development and Applications of Oceanic Engineering, Vol. 2, No. 1, pp. 77-85.
Yakubu, I. (2015), “Principles of Geodesyâ€, Unpublished BSc Lecture Notes, University of Mines and Technology, Tarkwa, 122pp.
Yusif-Attah, A.S. (2016), “Integration of Wavelet Analysis and Artificial Neural Network for Geodetic Deformation Modellingâ€, Unpublished BSc Project Report, University of Mines and Technology, Tarkwa, 34 pp.
Valentine Zabihi, M., Pourghasemi, H.R., Pourtaghi, Z.S. and Behzadfar, M. (2016), “GIS-Based Multivariate Adaptive Regression Spline and Random Forest Models for Groundwater Potential Mapping in Iranâ€, Environmental Earth Science, Vol. 75, pp. 2-19.
Ziggah, Y.Y., Youjian, H., Yu, X. and Basommi, L.P. (2016), “Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates to Cartesian Coordinatesâ€, International Association for Mathematical Geosciences, Vol. 48, pp. 687-721.
Pugh, D. T. (1996), “Tides, surges and mean sea levelâ€, John Wiley and sons ltd, pp 463.
Singh, V., Gupta, I. and Gupta, H.O. (2007), “ANN-based estimator for distillation using Levenberg–Marquardt approachâ€. Engineering Applications of Artificial Intelligence, 20(2), pp.249-259.
Downloads
Published
Issue
Section
License
Copyright © 2021 University of Mines and Technology (UMaT), Tarkwa. Ghana
