||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
184.108.40.206 Other contributors on Great Lakes water levels: 12
220.127.116.11 Human factors 13
18.104.22.168 Impacts of fluctuating water levels 13
22.214.171.124 Ecosystems 13
126.96.36.199 Recreational boating and tourism 14
188.8.131.52 Commercial navigation 14
184.108.40.206 Shore property interests 14
220.127.116.11 Municipal, industrial and domestic water use 14
18.104.22.168 Hydropower operation 15
1.4 Sea level measurement 15
1.4.1 Historic lake levels 17
22.214.171.124 Measuring sea level – Tides 19
126.96.36.199 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
188.8.131.52 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
184.108.40.206 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
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