||Sea level prediction using time series obtained from satellite altimetry observations
||Sea level prediction using time series obtained from satellite altimetry observations
||Department of Geomatics
sea level anomaly
In coastal management and ship navigation activities, there is an increasing demand for accurately predicting sea level fluctuations. In order to achieve this goal, accessible high-quality data and proper modeling process are critically required. The main purpose of the study focuses on developing and validating different modelling approach for analysis and forecasting of Caspian Sea level anomalies based on Topex/Poseidon and Jason-1 altimetry data generally covering 1993-2008-2013, which are originally developed and optimized for open oceans but have the considerable capability to monitor inland water level changes. Since these altimetric measurements comprise of a large datasets and then are complicated to be used for our purposes, for the first stage of the study, Principal Component Analysis (PCA) is adopted to reduce the complexity of large time series data analysis. Furthermore, Autoregressive Integrated Moving Average (ARIMA) model is applied for further analyzing and forecasting the time series. According to our analysis, ARIMA (1,1,0)(0,1,1) model has been found as an optimal representative model capable of predicting pattern of Caspian Sea level anomalies reasonably. Due to presence of temporal and spatial data gaps, least squares polynomial interpolation is thus performed to fill the gaps of along-track sea surface heights used for the next stage of study. The data were then adapted to Holt-Winters exponential smoothing (HWES) for investigating the capability of another linear approach for predicting the Caspian Sea level behavior. Although the modeling results agree well with the observed time series, but due to stochastic and nonlinearity nature of most water resources time series, these methods may not always perform well when applied in modeling hydrological time series. Therefore, in order to provide more applicable modelling approach, different artificial intelligent techniques were used for the short term Caspian Sea level forecasting. The forecast is performed by Multi-layer Perceptron network (MLP), Radial Basis Function (RBF), and Generalized Regression Neural Networks (GRNN), Support Vector Machine (SVM), and Gene Expression Programming (GEP). The overall results show that comparing with a routine Autoregressive Moving Average (ARMA) model, different neural network methodologies perform satisfactorily as a powerful tool in providing reliable results for predicting the short term Caspian Sea level anomalies. While all artificial intelligent approaches showed superior performance compare with conventional linear methods, the inter-comparison analysis verified that SVM has the best performance in predicting Caspian Sea-level anomalies, given the minimum Root Mean Square Error (RMSE=0.035) and maximum coefficient of determination (R2=0.96). The results of the study may lead to a better understanding of applicable tools in forecasting stochastic time series and giving an effective insight for more precise prediction-based decision making in water management scenarios.
List of content
CHAPTER I 1
1.1 Contributions to sea level change 3
1.1.1 Short-term and periodic changes 4
1.1.2 Longer-term changes 4
1.1.3 Glaciers and ice caps 5
1.2 Short-term variability and long-term trends 8
1.3 Lake level fluctuations: causes and implications 10
1.3.1 The challenge 10
1.3.2 Climatic factors 12
18.104.22.168 Other contributors on Great Lakes water levels: 12
22.214.171.124 Human factors 13
126.96.36.199 Impacts of fluctuating water levels 13
188.8.131.52 Ecosystems 13
184.108.40.206 Recreational boating and tourism 14
220.127.116.11 Commercial navigation 14
18.104.22.168 Shore property interests 14
22.214.171.124 Municipal, industrial and domestic water use 14
126.96.36.199 Hydropower operation 15
1.4 Sea level measurement 15
1.4.1 Historic lake levels 17
188.8.131.52 Measuring sea level – Tides 19
184.108.40.206 Satellite measurements of sea level change: where have we been and where are we going 22
1.4.2 Sea level change in the satellite era 22
220.127.116.11 Instruments on board the Jason 2 26
CHAPTER II 29
2.Literature Review 29
2.1 Sea level change in the era of recording tide gauge 29
2.2 The role of satellite data sets in hydrology applications 31
2.3 The necessity of predictions 35
2.4 Modeling approach 37
CHAPTER III 42
3. Study area: Caspian Sea 42
3.1 Coasts 45
3.2 River deltas 47
3.3 Climate 54
3.4 Wind and waves 55
3.5 Storm surges 58
3.6 Sea ice 60
3.7 Water balance 62
3.8 Sea level problem 66
3.9 Research objectives 74
CHAPTER IV 75
4. Methodology 75
4.1 Principal Component Analysis (PCA) and Autoregressive Integrated Moving Average Models 75
4.1.1 Principal Component Analysis 77
4.1.2 ARIMA modelling approach 81
4.2 Polynomial Interpolation and Holt-Winters Exponential Smoothing 84
4.2.1 Least squares polynomial interpolation 85
4.2.2 Exponential Smoothing Approach 88
4.3 Artificial Neural Networks 90
4.3.1 Multilayer perceptron network 93
4.3.2 Radial basis function (RBF) 94
4.3.3 General regression neural network (GRNN) 95
4.3.4 Models’ Performance Analysis 96
4.4 Evolutionary Support Vector Regression Algorithms and Gene Expression Programming 97
4.4.1 Support vector regression 102
4.4.2 Gene expression programming 105
4.4.3 Cascade correlation neural network 111
CHAPTER V 112
5. Results and discussion 112
5.1 Analysis and Prediction of Caspian Sea Level pattern Anomalies Observed by Satellite Altimetry Using Autoregressive Integrated Moving Average Models 112
5.1.1 Data 112
5.1.2 Results and discussion 115
5.2 Accurate Forecasting of Satellite-derived Seasonal Caspian Sea Level Anomaly Using Polynomial Interpolation and Holt-Winters Exponential Smoothing 127
5.2.1 Data 127
5.2.2 Results and discussion 128
18.104.22.168 Smoothing and Forecasting 129
5.3 Caspian Sea Level Prediction Using Satellite Altimetry by Artificial Neural Networks 134
5.3.1 Data 134
5.3.2 Results and Discussion 135
5.4 Forecasting Caspian Sea level changes using satellite altimetry based on evolutionary support vector regression algorithms and gene expression programming 147
5.4.1 Data 147
5.4.2 Results and discussion 148
CHAPTER VI 163
6. Conclusion 163
Abuzyarov, E.K. and Nesterov, E.S., 1999. In: 70 years of the Hydrometeorological Center of the Russian Federation. Gidrometeoizdat, St. Petersburg, p 216.
Ahmad, S.I. Khan, H., and Parida, B.P., 2001. Performance of stochastic approaches for forecasting river water quality. Water Research 35(18), 4261-4266.
Alsdorf, D., Birkett, C., and Dunne, T., 2001. Water level changes in a large Amazon lake measured with spaceborne radar interferometry and altimetry. Geophysical Research Letter. 28, 2671–2674.
Amirahmadi, H., 2008. The Caspian Region at a Crossroad: Challenges of a New Frontier of Energy and Development (Hardcover). St. Martin's Press. Retrieved, 112 pp.
Anandhi, A., Srinivas, V.V., Nanjundiah, R.S., and Kumar, D.N., 2008. Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Intranasal Journal of Climatology. 28, 401 – 420.
Angel, J. and Kunkel, K., 2009. The response of Great Lakes water levels to future climate scenarios with an emphasis on Lake Michigan-Huron. Journal of Great Lakes Research 36,51-58.
Aqil, M., Kita, I., Yano, A., and Nishiyama, S., 2007. Analysis and prediction of flow from local source in a river basin using a neuro-fuzzy modeling tool. Journal of Hydrology 85(1), 215–223.
Arpe, K.and Leroy, S.A.G., 2007. The Caspian Sea Level forced by the atmospheric circulation, as observed and modeled. Quaternary International 173-174, 144-152. Doi:10.1016/j.quaint.2007.03.008.
ASCE Task Committee, 2000. Artificial neural networks in hydrology I. Journal of Hydrologic Engineering ASCE 5(2), 115–123.
AVISO, 1996. AVISO User Handbook for Merged TOPEX/POSEIDON Products. AVI-NT-02-101-CN ed. 3.0, CNES.
Aytek, A. and Alp, M., 2008. An application of artificial intelligence for rainfall run off modeling. Journal of Earth System Science 117(2), 145–155.
Baedke, S. J. and Thompson. T., 2000. A 4,700-Year record of Lake Level and Isostasy for Lake Michigan. Journal of Great Lakes Research 26, 416-426.
Baidin, S.S. and Kosarev, A.N., (eds) (1986) The Caspian Sea. Hydrology and hydrochemistry. Nauka, Moscow.
Basistha, A., Arya D.S., and Goel, N.K., 2008. Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region. Water Resources Management 22, 1325-1346.
Beckley, B. D., Zelensky, N. P., Holmes, S. A., Lemoine, F. G., Ray, R. D., Mitchum, G. T., Desai S. D., and Brown, S. T., 2010. Assessment of the Jason-2 Extension to the TOPEX/Poseidon, Jason-1 Sea-Surface Height Time Series for Global Mean Sea Level Monitoring, Marine Geodesy, Special Issue: OSTM/Jason-2 Calibration/Validation, 33, 447-471.
Benada, R. J., 1997. Merged GDR (TOPEX/POSEIDON). Generation B Users Handbook, Version 2.0, Physical Oceanography Distributed Active Archive Center (PODAAC). Jet Propulsion Laboratory, Pasadena, JPL D-11007, 131 pp.
Bilgili, M., Sahin, B., and Yasar, A., 2007. Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renewable Energy 32: 2350–2360.
Bindoff, N.L. and Willebrand, J., 2007. Chapter 5: Observations: Oceanic Climate Change and Sea Level, Section 22.214.171.124: Interannual and decadal variability and long-term changes in sea level, in IPCC AR4 WG1 2007.
Birkett, C.M., 1995. The contribution of Topex/Poseidon to the global monitoring of climatically sensitive lakes. Journal of Geophysical Research 100, 25204-25179.
Bowerman, B. L., R. T. O’connell, and A. B. Koehler, 2005. Forecasting, Time series, and Regression. Printed in the United States: Thomson 295-298, 325-341, 366-385 pp.
Box, G.E.P., Jenkins, G.M., and Reinsel G.C., 2007. Time series analysis: forecasting and control, Third Edition. Dorling Kindersley (India) Pvt Ltd, New Delhi, India (licensees of Pearson Education in South Asia).
Box, G.E.P., Jenkins, G.M.,and Reinsel, G.C., 1991. Time series analysis, forecasting and control. NJ. USA: Prentice Hall, Engle wood Cliffs.
Box, G.E.P., Jenkins, G.M., 1976. Time series analysis, forecasting and control, Revised edn. San Francisco, CA: Holden - Day.
Cadenas, E. And W. Rivera, 2007. Wind speed forecasting in the south coast of Oaxaca, Me´xico. Renewable Energy 32, 2116–28.
Cadenas, E., Jaramillo, O.A., and Rivera, W., 2010. Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method. Renewable Energy 35, 925–930.
Cai, X. and Ji. W., 2009. Wetland hydrologic application of satellite altimetry – A case study in the Poyang Lake watershed. Progress in Natural Science 19, 1781–1787.
Carslaw, D.C., 2005. On the changing seasonal cycles and trends of ozone at Mace Head, Ireland. Atmospheric Chemical Physics 5, 3441-3450.
Cazenave, A., Bonnefond, P., Dominh, K., Schaeffer, P., 1997. Caspian sea-level form Topex-Poseidon altimetry: level now falling. Geophysical Research Letters 24, 881- 884.
Cazenave, A., Dominh, K., Guinehut, S., Berthier, E., Llovel, W., Ramillien, G., Ablain, M., and Larnicol, G., 2008. Sea level budget over 2003-2008: A reevaluation from GRACE space gravimetry, satellite altimetry and Argo. Global andPlanetery Change 65(1-2), 83-88.
Cazenave, A. and Nerem, R.S., 2004. Present-day sea level change: Observations and causes, Rev. Geophysical. 42. Doi:10.1029/2003RG000139.
Chatfield, C. Yar, M., 1988. Holt-winters forecasting: some practical issues. The Statistician 37, 129-140.
Chattopadhyay, S. and Chattopadhyay, G., 2010. Univariate modeling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN. Comptes Rendus Geoscience 342,100-107.
Chelton, D.B., Ries., J.C., Haines, B.J., Fu, L.L., and Callahan, P.S., 2001. Satellite altimetry. In Satellite Altimetry and Earth Sciences: A handbook of techniques and applications, eds. L. L. Fu, and A. Cazanave, 1-131. Academic Press, San Diego.
Chen. G., Wang, Z., Qian, C., Lv, C., and Han, Y., 2010. Seasonal-to-decadal modes of global sea level variability derived from merged altimeter data. Remote Sensing of Environment 114, 2524-2535.
Cherkassky, V. and Ma, Y., 2004. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17, 113–126.
Chu, Y.H., Li, J.C., and Jiang, W.P., Zou, X., Fan, C., Xu, X., and Dadzie, I., 2008. Monitoring level fluctuations of the lakes in the Yangtze River basin from radar altimetry. Terrestrial Atmospheric and Oceanic Science 19(1–2), 63–70.
Church, J. and White, N.J., 2006. A 20th century acceleration in global sea-level rise. Geophysical Research Letter 33. Doi:10.1029/2005GL024826.
Church, J., Woodworth, A., Aarup, T., and Wildon, W.S., 2010. Understanding Sea-Level Rise and Variability. Trans. Royal Soc. B 325, 437-455.
Cichocki, A. and Unbehauen, R., 1993. Neural networks for optimization and signal processing. Wiley-Teubner, Chichester, UK.
Corberan-Vallet, A., Bermudez, J.D., and Vercher, E., 2011. Forecasting correlated time series with exponential smoothing models. International Journal of Forecasting 27, 252–265.
Cretaux, J.F. and Birkett, C., 2006. Lake studies from satellite radar altimetry. CR Geoscience. 338(14–15):1098–112.
Cretaux, J.F., Jelinski, W., Calmant, S., Kouraev, V., and Vuglinski, M.V., 2011. SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data. Advances in Space Research 47, 1497-1507.
Cristianini, N. and Taylor, J.S., 2000. An Introduction to Support Vector Machines: and Other Kernel-Based Learning Methods, Cambridge University Press, New York.
Cubasch, U. and Meehl, G. A., 2002. Projections of future climate change. Climate Change 2002: the Scientific Basis, J. T. Houghton et al., eds., Cambridge University Press, Cambridge UK.
Cybenko, G., 1989. Approximation by superposition of a sigmoidal function. Mathematics of Control, Signals and Systems 2(4), 303–314.
Daley, R., 1991. Atmospheric data analysis. Cambridge University Press. ISBN 0-521-38215-7, pp 457.
Demuth, H.B., Beale, M.H., and Hagan, M.T., 2008. Mathworks. Neural Network Toolbox User's Guide.
Donahue, M., 2011. An Institutional/Governance Analysis for Implementing a Non-regulation Adaptive Response to Water Level Related Impacts. Prepared for the IUGLS Adaptive Management Technical Work Group.
Douglas, B., 2001. Sea level change in the era of the recording tide gauge, in Sea Level Rise: History and consequences, edited by B. Douglas, M. Kearney, and S. Leatherman, Ch. 3, Academic, San Diego.
Douglas, B.C., 1992. Global sea level acceleration. Journal of Geophysical Research 97, 12,699-612,706.
Douglas, B.C., 1991. Global sea level rise. Journal of Geophysical Research 96, 6981-6992.
Duan, Q., Gupta, H.V., Sorooshian, S., Rousseau, A.N., and Turcotte, R., eds., 2003. Calibration of Watershed Models, American Geophysical Union, Washington, DC, USA.
Duffy, P.B., Govindasamy, B., Milovich, J., Taylor, K., and Thompson. S., 2003. High
resolution simulations of global climate, Part 1: Present climate. Climate Dynamics, 21, 371-390.
Durand, F., Shankar, D., Birol, A., and Shenoi, S.S.C., 2008. Estimating boundary currents from satellite altimetry: A case study for the east coast of India. Journal of Oceanography, 64, 831-845.
Fahlman, S. E., and Lebiere, C., 1990. The Cascade-Correlation Learning Architecture in Advances in Neural Information Processing Systems 2, D. S. Touretzky (ed.), Morgan- Kaufmann, Los Altos CA, 1990.
Faruk, D.U., 2010. A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence 23, 586-594.
Fausset, L., 1994. Fundamental of Neural Networks Architectures, Algorithms and Applications, Prentice Hall International, 461.
Fenoglio-Marc, L., Braitenberg, C., and Tunini, L., 2011. Sea level variability and trends in the Adriatic Sea in 1993–2008 from tide gauges and satellite altimetry. Physics and Chemistry of the Earth doi:10.1016/j.pce.2011.05.014.
Ferreira, C., 2001a. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, Vol. 13, issue 2: 87-129.
Ferreira, C., 2001b. Gene expression programming in problem solving. In: 6th Online World Conference on Soft Computing in Industrial Applications, September 10-24, 2001. (invited tutorial). http://www.gene-expression-programming.com.
Ferreira, C., 2006a. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. 2nd Edition, Springer-Verlag, Homburg.
Ferreira, C., 2006b. Designing Neural Networks Using Gene Expression Programming. In A. Abraham, B. De Baets, M. Köppen, and B. Nickolay, eds., Applied Soft Computing Technologies: The Challenge of Complexity, pages 517-536, Springer-Verlag, New York.
Frappart, F., Calmant, S., and Cauhop, M., 2006. Preliminary results of Envisat RA-2-derived water levels validation over the Amazon basin. Remote Sensing Environment100(2), 252–64.
Gadgil, S., Rajeevan, M. And Nanjundiah, R., 2005, Monsoon prediction - Why yet another failure. Current Science 88(9), 1389-1400.
Gaur, S. and Deo M.C.,2008. Real-time wave forecasting using genetic programming. Ocean Engineering. 35(11–12), 1166–1172.
Gerald, C.F., and Wheatley, P.O., 2008. Applied Numerical Analysis. Addison Wesley Longman. India. Sri Lanka, 624 pp.
Ghanbarpour, M.R., Abbaspour, K.C., Jalalvand, G., and Moghaddam. G.A., 2010. Stochastic modeling of surface stream flow at different time scales: Sangsoorakh karst basin, Iran. Journal of Cave and Karst Studies 72, 1–10. DOI: 10.4311/jcks2007es0017.
Gigov, A. and Nikolova, M., 2002. Mapping Air Temperature Changes in Bulgaria Using GIS Spatial Analyses. Proceedings of the International Scientific Conference in Memory of Prof. Dimitar Yaranov, Varna, Bulgaria 114-121.
Gilchrist, B. and Cressman, G., 1954. An experiment in objective analysis. Tellus 6, 309–318.
Golitsyn, G.S., 1995. The Caspian Sea level as a problem of diagnosis and prognosis of the regional climate change. Atmospheric and Oceanic Physics, 31, 366–372.
Gregory, J.M. and Lowe, J.A., 2000. Predictions of global and regional sea-level rise using aogcms with and without flux adjustment. Geophysical Research Letters 27, 3069–3072.
Guisan, A. and Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecology Model 135, 147–186.
Guman, M.D., 1997. Determination of global mean sea level variations using multi-satellite altimetry, Ph.D. Dissertation, The University of Texas at Austin, US.
Hagan, M.T., Demuth, H., and Beale, M., 1995. Neural Network Design. PWS Publishing Company, Boston, MA.
Hallam, A., 1989. The Case for Sea-Level Change as a Dominant Causal Factor in Mass Extinction of Marine Invertebrates. Phil. Trans. R. Soc. B 325, 437-455.
Hanke, J. E. and Wichern, D.W, 2003. Business forecasting. 9 edition. Prentice Hall, Upper Saddle River, NJ, USA
Harvey, N., Belperio, A.,Bourman, R., and Mitchell, W., 2002. Geologic, isostatic and anthropogenic signals affecting sea level records at tide gauge sites in southern Australia. Global and Planetary Change 32, 1 –11.
Haykin, S., 1999a. Neural Networks,a Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ, USA.
Haykin, S.,1999b. Neural Networks: a Comprehensive Foundation. Prentice-Hall, Upper Saddle River, NJ, 842 pp.
Heimes, F. and Heuveln, B., 1998. The normalized radial basis function neural network. Systems, man, and cybernetics. In Proceedings of the IEEE international conference on 11–14 October 1998 Vol.2, pp. 1609–1614.
Holton, J. R., 1992. An Introduction to Dynamic Meteorology. Academic Press, San Diego.
Hornik, K., Stinchcombe, M., and White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2(5): 359–366.
Hosseini, S.S. and Gandomi, A.H., 2012. Short-term load forecasting of power systems by gene expression Programming. Neural Computer & Applicaction 21,377–389.
Howlett,R.J. and Jain, L.C., 2001. Radial basis function networks 1: recent developments in theory and applications. New York : Physica-Verlag, 2001
Hsu, C.W., Chang, C.C., and Lin, C.J., 2003. Last updated: April/2010. A practical guide to support vector classification. Department of Computer Science, National Taiwan University, Taipei, Taiwan.
Hwang, C.W., Peng, M.F., Ning, J.S., and Sui, C.H., 2004. Lake level variations in China from TOPEX/Poseidon altimetry. Geophysical Journal International 161, 1–11 Doi:10.1111/j.1365-246X.2005.02518.x.
Imani, M., You R.J., and Kuo, C. Y., 2013. Caspian Sea Level prediction using artificial neural networks (anns) and satellite altimetry. Int. J. Environ. Sci. Te. In press. DOI: 10.1007/s13762-013-0287-z.
Imani, M., You, R.J., and Kuo, C.Y. 2012. Accurate Forecasting of Satellite-derived Seasonal Caspian Sea Level Anomaly Using Polynomial Interpolation and Holt-Winters Exponential Smoothing. Terrestrial Atmospheric and Oceanic Science 24,521-530.
Inggs, M. R. And R. T. Lord, 2002. Interpolating satellite derived wind field data using ordinary Kriging, with application to the nadir gap. Geoscience and Remote Sensing 34, 250-256.
Intergovernmental Oceanographic Commission, 1985. Manual on sea level Measurement and interpretation, Manuals and Guides Volume I - Basic Procedures, 1985 UNESCO, http://www.psmsl.org/train_and_info/training/manuals/ioc_14i.pdf.
Jet Propulsion Laboratory, 2009. Last viewed 22 May 2009 “Ocean surface topography from space”; “Rising waters: new map pinpoints areas of sea-level increase”, CNES.
Jevrejeva, S., Moore, J.C., Grinsted, A., and Woodworth, P.L., 2008. Recent global sea level acceleration started over 200 years ago. Geophysical Research Letters 35, L08715, doi:10, 1029, 2008 GL033611.
Jolliffe, I.T., 2002. Principal Component Analysis, Series: Springer Series in Statistics, 2nd ed. Springer, NY, 487 p. 28 illus. ISBN 978-0-387-95442-4.
Kallache, M.H., Rust, W., and Kropp, J., 2005. Trend assessment: applications for hydrology and climate research. Nonlinear Processes in Geophysics 12, 201-210.
Katimon, A. and Demun, A.S., 2004. Water use trend at universiti teknologi malaysia: Application of arima model. Journal of Technology 41, 47-56.
Kim, B., Kim, S., and Kim, K., 2003. Modelling of plasma etching using a generalized regression neural network. Vacuum 71, 497–503.
Kim, K.J., 2003. Financial time series forecasting using support vector machines, Neurocomputing, 55, 307 – 319.
Kisi, O., Shiri, J., and Nikoofar, B., 2012. Forecasting daily lake levels using artificial intelligence approaches. Computer. Geoscience.-UK., 41, 169–180.
Koblinsky, C.J., Clarke, R.T., Brenner, A.C, et al. 1993. Measurement of river level variations with satellite altimetry. Water Resour Res. 29(6),1839–48.
Koçak, K., 2008. Practical ways of evaluating wind speed persistence. Energy, 33, 65–70.
Komen, G. and Smith. N., 1999. Wave and sea level monitoring and prediction in the service module of the Global Ocean Observing System (GOOS). Journal of Marine Systems 19, 235–250.
Koshinskii, S.D.,1975. Regime characteristics of strong winds over the seas of the USSR. Gidrometeoizdat, Leningrad.
Kostianoy, A. G. and Kosarev. A. N., 2005. The Caspian Sea Environment. Berlin: Springer Verlag. New York: Heidelberg.
Koza, J. R., 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.
Kozaka, J. and Krajnc, M., 2007. Geometric interpolation by planar cubic polynomial curves. Computer Aided Geometric Design 24, 67–78.
Krishna Kumar, K., Hoerling, M., and Rajagopalan, B. 2005: Advancing dynamical prediction of Indian monsoon rainfall. Geophysical Research Letter, 32(8), L08704, 10.1029/2004GL021979, 4.
Krishnamurti, T.N., Kishtawal, C.M., larow, T.E., Bachiochi, D.R., Zhang, Z., Williford, C.E., Gadgil, S., and Surendran, S., 2000, Multimodel ensemble forecasts for weather and seasonal climate. Jouranl Climate, 13, 4196-4216.
Kroonenberg, S.B., Rusakov, G.V., and Svitoch, A.A., 1997. The wandering of the Volga delta: A response to rapid Caspian sea-level change. Sedimentary Geology 107, 189-209.
Kropáceka, J., Brauna, A., Kang, C., Feng, C., Yeb, Q., and Hochschild, V., 2011. Analysis of lake level changes in Nam Co in central Tibet utilizing synergistic satellite altimetry and optical imagery. International Journal of Applied Earth Observation and Geoinformation Doi:10.1016/j.jag.2011.10.001.
Kumar, M., Kumar, A., Mahanti, N.C., Mallik, C., and Shukla, R.K., 2009. Surface flux modeling using ARIMA technique in humid subtropical monsoon area. Journal of Atmospheric and Solar-Terrestrial Physics 71, 1293-1298.
Kuo C. Y. and Shum. C. K., 2004. Vertical crustal motion determined by satellite altimetry and tide gauge data in Fennoscandia. Geophysical research letters 31, l01608, doi:10.1029/2003gl019106.
Kuo, C.Y. and Kao, H.C., 2011. Retracked Jason-2 Altimetry over Small Water Bodies: Case Study of Bajhang River, Taiwan. Marine Geodesy 34(3-4), 382-392.
Kurtzman, D. and Kadmon, R., 1999. Mapping of temperature variables in Israel: a comparison of different interpolation methods. Climate Research 13, 33-43.
Kutzbach, J.E., 1967. Empirical eigenvectors of sea level pressure, surface temperature and precipitation complex over North America. Journal of Applied Meteorology 6, 791-802.
Lambeck, K. and Chappell, J., 2001. Sea level change through the last glacial cycle. Science 292, 679-686.
Lanciani, A. and Salvati, M., 2008. Spatial interpolation of surface weather observations in Alpine meteorological services. Università degli Studi di Trento. ISBN 978–88–8443–225–4.
Lax, P., 1997. Linear Algebra. New York. Wiley & Sons.
Lebedev, S.A. and Kostianoy, A.G., 2008. Integrated use of satellite altimetry in the investigation of the meteorological. Hydrological, and Hydrodynamic Regime of the Caspian Sea. Terrestrial Atmospheric and Oceanic Science 19(1-2): 71-82.
Lee, H.K., 2008. Radar Altimetry Methods for Solid Earth Geodynamic Studies. Ohio university, reported NO. 489: www.geology.osu.edu/~jekeli.1/OSUReports/reports/report_489.pdf
Lee, H.K., Shum, C. K., Tseng, K.H., Guo, J.Y., and Kuo, C.Y., 2010. Present-Day Lake Level Variation from Envisat Altimetry over the Northeastern Qinghai-Tibetan Plateau: Links with Precipitation and Temperature. Doi: 10.3319/TAO.2010.08.09.01.
Lemoine, F. G., Kenyon, S. C., Factor, J. K., Trimmer, R. G., Pavlis, N. K., and Chinn, D. S., 1998. The Development of the joint NASA GSFC and NIMA Geopotential Model EGM96. NASA/TP-1998-206861. 575 pp.
Leuliette, E.W., Nerem, R.S., and Mitchum, G.T., 2004. Results of TOPEX/Poseidon and Jason-1 Calibration to Construct a Continuous Record of Mean Sea Level, Marine Geodesy, 27, 79-94.
Li, J. and Castagna, J., 2004. Support Vector Machine (SVM) pattern recognition to AVO classification, Journal Geophysical Research 31, L02609, doi:10.1029/2003GL018299.
Lins, I.D., Araujo, M., Moura, M.C., Silva, M.A., and Droguett, M.L., 2013. Prediction of sea surface temperature in the tropical Atlantic by support vector machines. Computational Statistics and Data Analysis 61, 187–198.
Liong, S.Y., Gautam, T.R., Khu, S.T., Babovic, V., Keijzer, M., and Muttil, N., 2002. Genetic programming: a new paradigm in rainfall run off modeling. Journal of American Water Resources Association 38(3), 705–718.
Ljung, G.M. and Box, G.M.P., 1978. On a measure of lack of fit in time series models. Biometrika 65: 297 303.
Lorenz, E., 1956. Empirical orthogonal function and statistical weather prediction, Science Report No. 1. Statistical Forecasting Project. MIT, Cambridge, U.S.A.
Lu, J.C., Lee, T.S., and Chiu, C.C., 2009. Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems 47, 115–125.
Lukyanova, S.A. and Solov’eva, G.D., 2001. In: Coastal zone of seas, lakes, and reservoirs. Vol 1, Novosibirsk, p 166.
Malinin, V.M. 1994. The problem of the Caspian Sea level forecast. St. Petersburg.
Medina, C.E., Gomez-Enri, J., and Alonso, J.J., 2008. Water level fluctuations derived from Envisat radar altimeter (RA-2) and in-situ measurements in a subtropical waterbody: Lake Izabal (Guatemala). Remote Sensing Environment 112(9), 3604–17.
Meijerink. A. M. J., 1996. Remote sensing applications to hydrology: groundwater. Hydrological sciences- Journal- des sciences Hydrologiques 41, 549-561.
Mercier, F.,Cazenave, A., and Maheu, C., 2002.Interannual lake level fluctuations in Africa (1993 - 1999) from Topex-Poseidon: Connections with ocean-atmosphere interactions over the Indian Ocean. Global and Planetary Change, 32, 141-163. Doi. 10.1016/S0921-8181(01)00139-4.
Meshcherskaya, A.V., Golod, M.P., and Belyankina, I.G., 2002. In: Climatic changes and their aftereffects. Nauka, St. Petersburg, p 180.
Mikhailov, V.N, 1997. Mouths of the rivers of Russia and adjacent countries: present, past, and future. Geos, Moscow.
Mikhailov, V.N., Kravtsova, V.I., Magritskiy, D.V., Mikhailova, M.V., and Isupova, M.V., 2004. Deltas of the Caspian rivers and their response to changes of the sea level, Caspian Sea Bulletin, № 6, pp. 60–104. (In Russian).
Mikhailov, V.N., Dobrovol’skii, A.D., and Dobrolyubov, S.A, 2005. Hydrology. Vysshaya shkola, Moscow.
Miller, K.G., 2005. The Phanerozoic Record of Global Sea-Level Change. Science 310, 1293-1298.
Milne, G. A., Gehrels, W. R., and Hughes, C. W., Tamisiea, M. E., 2009. Identifying the causes of sea-level change. Nature Geoscience 2, 471-478.
Mitchum, G. T.,Nerem, R. S., Merrifield, A., and Baker, T., 2006. 20th Century Sea-level Rise and Variability Estimates from Tide Gauges and Altimeters, Position Paper for World Climate Research Programme Workshop and a WCRP contribution to the Global Earth Observation System of Systems, UNESCO, Paris, France.
Mitchum, G.T., 2000. An Improved Calibration of Satellite Altimetric Heights Using Tide Gauge Sea Levels with Adjustment for Land Motion. Marine Geodesy 23,145-166.
Morris, C.S. and Gill, S.K., 1994. Variation of great-lakes water levels derived from GEOSAT altimetry. Water Resource Research 30(4), 1009–17.
Naidenov, V.I. and Kozhevnikova, I.A., 2000. Nonlinear variations of the level of the Caspian sea and the global climate. Doklady Physics 46, (5) 340-345.
Nerem, R. S., 1995. Measuring global mean sea level variations using TOPEX/POSEIDON altimeter data. Journal of Geophysical Research: Oceans 100, 25135–25151.
Nerem, R..S., Chambers, D.P., Leuliette, E.W., Mitchum, G.T., and Giese, B.S., 1999. Variations in global mean sea level associated with the 1997-1998 ENSO event: Implications for measuring long term sea level change. Geophysical Research Letters 26, 3005-3008.
Niedzielski, T. and Kosek,W., 2005. Multivariate stochastic prediction of the global mean sea level anomalies based on TOPEX/Poseidon satellite altimetry. Artificial Satellites 40, 185–198.
Niedzielski, T. and Kosek, W., 2009. Forecasting sea level anomalies from TOPEX/ Poseidon and Jason-1 satellite altimetry. Journal of Geodesy 83, 469–476.
Niedzielski, T. and Kosek, W., 2010. El Nino's impact on the probability distribution of sea level anomaly fields. Polish Journal of Environmental Studies19, 611–620.
Oyatoyea, E. O. And Fabsonb, T. V. O., 2011. A comparative study of simulation and time series model in quantifying bullwhip effect in supply chain. Serbian Journal of Management 6(2), 145-154.
Palit, A.K., Popovik, D., 2005. Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications, In: Grimble, M.J., Johnson, M.A. (Eds.), Advances in Industrial Control. Springer-Verlag, London, pp. 3–142 .
Pavlis, N., Kenyon, S., Factor, J., and Holmes, S., 2008. Earth gravitational model 2008. In SEG Technical Pro-gram Expanded Abstracts 27, 761-763.
Stone, P.H., 2004. Climate Prediction: The Limits of Ocean Models. Geophysical Monograph 150, 19. 259-267.
Picot, N., Case, K., Desai, S., and Vincent, P., 2006. AVISO and PODAAC User Handbook. IGDR and GDR Jason Products. SMM-MU-M5-OP-13184-CN (AVISO). JPL D-21352 (PODAAC). Edition 3. 112 pp.
Plag, H.P. and Tsimplis. M.N., 1999. Temporal variability of the seasonal sea-level cycle in the North Sea and Baltic Sea in relation to climate variability. Global and Planetary Change 20, 173–203.
Rahmstorf, S., 2007. A semi-empirical approach to projecting future sea-level rise. Science 315, 368–370.
Rajeevan M., Pai D.S., Dikshit S.K., and Kelkar R.R., 2004, IMD’s New Operational Models for Long Range Forecast of South-west Monsoon Rainfall over India and their Verification for 2003. Current Science 86, 422-431.
Reed, R.E., Dickey, D., Burkholder, J.M., Kinde, C.A., and Brownie, C., 2008. Water level variations in the Neuse and Pamlico Estuaries, North Carolina due to local and remote forcing. Estuarine, Coastal and Shelf Science 76, 431-446.
Röske, F., 1997. Sea level forecasts using neural networks. Ocean Dynamics 49, 71–99.
Sachs, J.D., Mellinger, A.D., and Gallup, J.L., 2001. The geography of poverty and wealth. Scientific America 284(3), 70-75.
Scherneck, H. G., J. M. Johansson, M. Vermeer, J. L. Da¬vis, G. A. Milne, and J. X. Mitrovica. 2001. BIFROST project: 3-D crustal deformation rates derived from GPS confirm postglacial rebound in Fennoscandia. Earth Planets and Space 53, 703-708.
Sfetsos, A., 2000. A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy 21 23–35.
Shahwan, T., Odening, M., 2007. Computational intelligence in economics and finance, 63-74. Berlin Heidelberg, newyork: Springer.
Sheta, A.F. and Mahmoud, A., 2001. Forecasting using genetic programming. In: Proceedings of the 33rd Southeastern Symposium on System Theory, Athens, OH, USA, pp. 343–347.
Shiri, J. and Kisi, O., 2011. Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers &Geosciences 37, 1692–170.
Tripathi, S., Srinivas, V.V., Ravi. S. and Nanjundiah. N., 2006. Downscaling of precipitation for climate change scenarios: A support vector machine approach. Journal of Hydrology 330, 621– 640.
Small, C. and Nicholls, R.J., 2003. A Global Analysis of Human Settlement in Coastal Zones, Journal of Coastal Research,Volume 19, Issue 3, p.584-599, (2003).
Snyder, R. D., Koehler, A. B., Hyndman, R. J., Ord, J. K., 2004. Exponential smoothing models: means n variances for lead-time demand. European Journal of Operational Research 444–5.
Soltani, S., Modarres, R., and Eslamian, S.S., 2007. The use of time series modeling for the determination of rainfall climates of Iran. International Journal of Climatology 27, 819-829.
Specht, D.F., 1991. Enhancements to probabilistic neural network. In Proceedings of the international joint conference neural network 1, 761–768.
Srinivas, K., Revichandran, C., Dinesh Kumar, P.K., 2003. Statistical forecasting of met-ocean parameters in the Cochin estuarine system, southwest coast of India, Indian Journal Marine Science 32, 285-293.
Sudheer, K.P., Gosain, A.K., and Ramasastri, K.S, 2002. A data-driven algorithm for constructing artificial neural network rainfall–runoff models. Hydrological Processes 16(6), 1325–1330.
Talebizadeh, M. and Moridnejad, N., 2011.Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Systems with Applications 38, 4126–4135.
Tamisiea, M.E., Mitrovica, J.X., Nerem, R.S., Leuliette, E.W., Milne, G.A., 2006. Correcting satellite derived estimates of global sea level changes for glacial isostatic adjustment, Geophysical Journal International 33, 21-34.
Tay, F.E.H. anf Cao, L.J., 2001. A comparative study of saliency analysis and genetic algorithm for feature selection in support vector machines. Intelligent Data Analysis 5 (3), 191 - 209.
Taylor, J. W., 2004a. Smooth Transition Exponential Smoothing. Journal of Forecasting 23, 385–394.
Taylor, J. W., 2004b. Volatility forecasting with smooth transition exponential smoothing. International Journal of Forecasting 20, 273–286.
Taylor, J. W., 2010. Exponentially Weighted Methods for Forecasting Intraday Time Series with Multiple Seasonal Cycles. International Journal of Forecasting 26, 627-646.
Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D., Schummer M., and Haussler, D., 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, (10) 906-914.
Terziev, F.S., Kosarev, A.N., and Aliev, A.A., 1992. (eds) Hydrometeorology and hydrochemistry of the seas, Vol. 6. The Caspian Sea, Issue 1. Hydrometeorological conditions. Gidrometeoizdat, St. Petersburg.
Thain, R.H., Priestley,A.D., and Davidson, M.A., 2004.The formation of a tidal instruction front at the mouth of a macrotidal, partially mixed estuary: a field study of the Dart Estuary. UK Estuarine and Shelf Science 61,161–172.
Hastie, T., Tibshirani, R., and Friedman, J., 2001. The Elements of Statistical Learning Data Mining, Inference, and Prediction. Second Edition. Springer Series in Statistics.
Turner, R.K., Subak, S., and Adger, N.W., 1996. Pressures, trends, and impacts in coastal zones: Interactions between socioeconomic and natural systems. Environmental Management, 20, 159-173.
Ustoorikar, K. and Deo M.C., 2008. Filling up gaps in wave data with genetic Programming. Marine Structure. 21,177–195.
Vapnik, V., Golowich S., and Smola A., 1997. Support vector method for function approximation, regression estimation, and signal processing. In: Mozer, M., M. Jordan and T. Petsche (Eds.), Advances in Neural Information Processing Systems, vol. 9. MIT Press, Cambridge, MA, pp. 281–287.
Vapnik, V.N., 1992. Principles of risk minimization for learning theory. Advance Neural. In. 4, 831–838.
Vapnik, V.N., 1999. An overview of statistical learning theory. IEEE T. Neural Network. 10 (5), 988–999.
Vapnik, V.N., 2000. The Nature of Statistical Learning Theory, Springer, New York.
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.
Velicogna, I., 2009. Increasing rates of ice mass loss from the Greenland and Antarctic ice sheets revealed by GRACE. Geophysical Research Letters 36 (19).
Wang, Y., Sun, G., and Liao, M., Using MODIS images to examine the surface extents and variations derived from the DEM and laser altimeter data in the Danjiangkou Reservoir, China. International Journal of Remote Sensing 29(1):293–311.
Wang, B., Ding, Q., Fu, X., Kang, I., Jin, K., Shukla, J., and Doblas-Reyes, F., 2005, Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophysical Research Letter 32, No. 15, L15711, doi. 10.1029/2005GL022734.
Weare, B.C., 1977. Empirical orthogonal analysis of Atlantic Ocean surface temperatures. Quarterly Journal of the Royal Meteorological Society 103 (437), 467-478.
Wilcox, D. A., Thompson, T.A., Booth R.K., Nicholas, J. R., 2007. Lake-level variability and water availability in the Great Lakes: U.S. Geological Survey Circular 1311, 25.
Wood, M.J. and Hirst, J.D., 2004. Predicting protein secondary structure by cascade-correlation neural networks. Bioinformatics Applications Note 20 (3), 419–420.
Woodworth, P.L., 1990. A search for accelerations in records of European mean sea level. International Journal of Climatology 10, 129–143.
Wöppelmann, G., Martin Miguez, B., Bouin, M.N., and Altamimi, Z., 2007. Geocentric sea-level trend estimates from GPS analyses at relevant tide gauges world-wide, Global and Planetary Change 57, 3–4, 396–406.
Wróblewski, A., 1998. The effect of the North Sea on oscillations of the mean monthly sea levels in the Baltic Sea. Continental Shelf Research 18, 501-514.
Yapo, P. O., Gupta, H.V., and Sorooshian, S., 1996. Automatic calibration of conceptual rainfall runoff models: Sensitivity to calibration data. Journal of Hydrology 181(1-4), 23-48.
Zhang, J.Q., Xu, K.Q., Qi, L.H., et al. 2005. Estimation of freshwater and material fluxes from the Yangtze River into the East China Sea by using Topex/Poseidon altimeter data. Hydrol Process 19(18), 3683–98.
Zhang, J.Q., Xu, K.Q., and Yang, Y.H., 2006. Measuring water storage fluctuations in lake Dongting, China, by Topex/Poseidon satellite altimetry. Environ Monit Assess,115(1–3), 23–37.
Zhang, M.M., 2009. Satellite Radar Altimetry for Inland Hydrologic Studies. PhD dissertation , Ohio University, US. http://rave.ohiolink.edu/etdc/view?acc_num=osu1236711299.
Zonn, I.S., 2004. The Caspian Encyclopaedia. Mezhdunarodnye otnosheniya, Moscow.
Zwally, H.J., 1989. Mass changes of the Greenland and Antarctic ice sheets and shelves and contributions to sea-level rise. 1992–2002.