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系統識別號 U0026-2207201412492900
論文名稱(中文) Sea level prediction using time series obtained from satellite altimetry observations
論文名稱(英文) Sea level prediction using time series obtained from satellite altimetry observations
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 102
學期 2
出版年 103
研究生(中文) 莫錫霖
研究生(英文) Moslem Imani
學號 P68997010
學位類別 博士
語文別 英文
論文頁數 181頁
口試委員 指導教授-尤瑞哲
指導教授-郭重言
口試委員-Wolfgang Keller
召集委員-史天元
口試委員-江凱偉
中文關鍵字 none 
英文關鍵字 Caspian Sea  sea level anomaly  atellite altimetry  prediction  artificial intelligent 
學科別分類
中文摘要 none
英文摘要 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.
論文目次 List of content
Title Page
ABSTRACT
CHAPTER I 1
1.Introduction 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
1.3.2.1 Other contributors on Great Lakes water levels: 12
1.3.2.2 Human factors 13
1.3.2.3 Impacts of fluctuating water levels 13
1.3.2.4 Ecosystems 13
1.3.2.5 Recreational boating and tourism 14
1.3.2.6 Commercial navigation 14
1.3.2.7 Shore property interests 14
1.3.2.8 Municipal, industrial and domestic water use 14
1.3.2.9 Hydropower operation 15
1.4 Sea level measurement 15
1.4.1 Historic lake levels 17
1.4.1.1 Measuring sea level – Tides 19
1.4.1.2 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
1.4.2.1 Instruments on board the Jason 2 26
CHAPTER II 29
Title Page
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
Title Page
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
5.2.2.1 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
References 165
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