Artificial intelligence techniques for predicting tidal effects based on geographic locations in Ghana
Tidal forces as a result of attraction of external bodies (Sun, Moon and Stars) through gravity and are a source of noise in many geoscientific field observations. The solid earth tides cause deformation. This deformation results in displacement in geographic positions on the surface of the earth. The displacement due to tidal effects can result in deformation of engineering structures, loss of lives, and economic cost. Tidal forces also help in detecting other environmental and tectonic signals. This study quantifies the effects of solid earth tides on stationary survey controls in five regions of Ghana. The study is in two stages: firstly, the solid earth tides were estimated for each control by a geometric approach (combining Navier’s equation of motion and Love theories). Secondly, estimation using two artificial intelligence methods (Multivariate Adaptive Regression Splines (MARS) and Backpropagation Artificial Neural Network (ANN)). Based on statistical indices of Mean Square Error (MSE) and Correlation Coefficient (R), BPANN, and MARS models can be used as a realistic alternative technique in quantifying solid earth tides for the study area. The MSE and R (MSE; BPANN = 1.3249 × 10–04 and MSE; MARS = 2.2052 × 10–06; R; BPANN = –0.6067 and R; MARS 0.6570) values indicate that MARS outperforms BPANN in quantifying solid earth tides in the study area. BPANN and MARS can be used as an efficient tool for quantifying tidal values based on geographic positions for geodetic deformation studies within the study area.
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Anane, O. E. (2015). Assessing the methods of estimating ellipsoidal height for a local geodetic network (Unpublished BSc Thesis). University of Mines and Technology, Tarkwa, Ghana.
Beltrami, G. M. (2008). An ANN algorithm for automatic, real-time tsunami detection in deep-sea level measurements, Ocean Engineering, 35(5–6), 572–587. https://doi.org/10.1016/j.oceaneng.2007.11.009
Craven, P., & Wahba, G. (1979). Smoothing noisy data with spline function: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik, 31, 317–403. https://doi.org/10.1007/BF01404567
Durmaz, M., & Karslioglu, M. O. (2011). Non-parametric regional VTEC modelling with Multivariate Adaptive Regression B-Splines. Advances in Space Research, 48, 1523–1530. https://doi.org/10.1016/j.asr.2011.06.031
Friedman, J. H. (1991). Multivariate adaptive regression splines. Annals Statistics, 19, 1–67. https://doi.org/10.1214/aos/1176347963
Heping, S., Bernard, D., Houze, X., Leslie, V., Jianqiao, X., & Jiangcun, Z. (2005). Adaptability of the ocean and earth tidal models based on global observations of the superconducting gravimeters. Science in China Series D: Earth Sciences, 48(11), 1859–1869. https://doi.org/10.1360/04yd0071
Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME – Journal of Basic Engineering, 82(1), 35–45. https://doi.org/10.1115/1.3662552
Kolvankar, V. G., Deshpande, S. S., Manjre, A. S., Pansare, More, S. S., & Thakur, N. (2010). Lunar periodicities and earthquakes. New Concepts in Global Tectonics Newsletter, (56), 32–49.
Kurinsky, N. (2013). Tidal prediction and compensation in aLIGO (Final Report, pp. 1–58). Tufts University, LIGO Hanford Observatory.
Kutoglu, H. S. (2006). Artificial neural networks versus surface polynomials for determination of local geoid. Paper presented at 1st International Gravity Symposium, Istanbul, Turkey.
Lu, Z., Qu, Y., & Qiao, S. (2014). Geodesy: Introduction to Geodetic Datum and Geodetic Systems. Springer. https://doi.org/10.1007/978-3-642-41245-5_3
Mendieta, C. (2001). A comparison of artificial neural networks models for predicting tide levels (Master Thesis). Texas A & M University-Corpus Christi.
Mohammed, A. S. (2015). Performance assessment of the methods used in transformation from cartesian coordinates to geodetic coordinates (Unpublished BSc report). University of Mines and Technology, Tarkwa, Ghana.
Mueller, V. A., & Hemond, F. H. (2013). Extended artificial neural networks: in-corporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmental relevant levels. Talenta, 117, 112–118. https://doi.org/10.1016/j.talanta.2013.08.045
Munk, W. H., & Cartwright, D. E. (1966). Tidal spectroscopy and predication. Philosophical Transactions of the Royal Society of London, 259(1105), 533–581. https://doi.org/10.1098/rsta.1966.0024
Pavlis, K. N., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). An earth gravitational model to degree 2160. In EGU General Assembly 2008 (pp. 1–5). Vienna, Austria. https://doi.org/10.1190/1.3063757
Rafiq, M., & Santos, M. (2004). Study of Eastern Canadian coastal site displacement due to ocean tide loading using a GPS network in Atlantic Canada. In Joint AGU/CCU Scientific Meeting, Montreal, Canada.
Samui, P., & Kim, D. (2012). Modelling of reservoir-induced earthquakes: a multivariate adaptive regression spline. Journal of Geophysics and Engineering, 9, 494–497. https://doi.org/10.1088/1742-2132/9/5/494
Samui, P., & Kothari, D. P. (2012). A multivariate adaptive regression spline approach for prediction of maximum shear modulus (Gmax) and minimum damping ratio (£min). Engineering Journal, 16(5), 1–10. https://doi.org/10.4186/ej.2012.16.5.69
Samui, P. (2013). Multivariate Adaptive Regression Spline (MARS) for prediction of elastic modulus of jointed rock mass. Geotechnical and Geological Engineering, 31, 249–253. https://doi.org/10.1007/s10706-012-9584-4
Siek, M., & Solomatine, D. P. (2010). Nonlinear chaotic model for predicting storm surges. Nonlinear Processes in Geophysics, 17, 405–420. https://doi.org/10.5194/npg-17-405-2010
Straser, V. (2010). Variations in gravitational field, tidal force, electromagnetic waves and earthquakes. New Concepts in Global Tectonics Newsletter, (57), 98–108.
Torge, W. (1991). Geodesy (2 ed.). Walter de Gruyter. https://doi.org/10.1515/9783111542683
Vaziri, M. (1997). Predicting Caspian Sea surface water level by ANN and ARIMA models. Journal of Waterway, Port, Coastal and Ocean Engineering, 123, 158–162. https://doi.org/10.1061/(ASCE)0733-950X(1997)123:4(158)
Yakubu, I. (2008). Local area deformation monitoring a multi GPS receiver network system approach-a case study (MPhil Thesis). University of Mines and Technology, Tarkwa, Ghana.
Yegnanarayana, B. (2005). Artificial neural networks. PrenticeHall of India Private Limited.
Zabihi, M., Pourghasemi, H. R., Pourtjhi, Z. S., & Behzadfar, M. (2016). GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environmental Earth Sciences, 75(665), 646–665. https://doi.org/10.1007/s12665-016-5424-9
Ziggah, Y. Y., Youjian, H., Tierra, A., Konate, A. A., & Hui, Z. (2016a). Performance evaluation of artificial neural networks for planimetric coordinate transformation – A case study, Ghana. Arabian Journal of Geosciences, 9, 698–714. https://doi.org/10.1007/s12517-016-2729-7
Ziggah, Y. Y., Youjian, H., Yu, X., & Basommi, L. P. (2016b). Capability of artificial neural network for forward conversion of geodetic coordinates (Ф, λ, h) to cartesian coordinates (X, Y, Z). Mathematical Geosciences, 48, 687–721. https://doi.org/10.1007/s11004-016-9638-x