scholarly journals Prediction of Sea Level Nonlinear Trends around Shandong Peninsula from Satellite Altimetry

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4770
Author(s):  
Jian Zhao ◽  
Ruiyang Cai ◽  
Yanguo Fan
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Prediction Models ◽  
Stationary Time Series ◽  
Intrinsic Mode Functions ◽  
Shandong Peninsula ◽  
Rbf Network ◽  
Multi Scale ◽  
Nonlinear Trends ◽  
Prediction Approach

Sea level change is a key indicator of climate change, and the prediction of sea level rise is one of most important scientific issues. In this paper, the gridded sea level anomaly (SLA) data from satellite altimetry are used to analyze the sea level variations around Shandong Peninsula from 1993 to 2016. Based on the Complete Ensemble Empirical Mode Decomposition (CEEMD) method and Radial Basis Function (RBF) network, the paper proposes an improved sea level multi-scale prediction approach, namely, CEEMD-RBF combined model. Firstly, the multi-scale frequency oscillatory modes (intrinsic mode functions (IMFs)) representing different oceanic processes are extracted by CEEMD from the highest frequency to the lowest frequency oscillating mode. Secondly, RBF network is used to establish prediction models for various IMF components to predict their future trends, and each IMF is used as an input factor of the RBF network separately. Finally, the prediction results of each IMF component with RBF network are reconstructed to obtain the final predictions of sea level anomalies. The results shows that CEEMD is particularly suitable for analyzing nonlinear and non-stationary time series and RBF network is applicable for regional sea level prediction at different scales.

scholarly journals Multi-scale analysis of teleconnection indices: climate noise and nonlinear trend analysis

2009 ◽  
Vol 16 (1) ◽  
pp. 65-76 ◽  
Author(s):  
C. Franzke
Keyword(s):  
Time Series ◽  
Decadal Variability ◽  
Statistical Significance ◽  
Stationary Time Series ◽  
Intrinsic Mode Functions ◽  
Multi Scale ◽  
The North ◽  
Teleconnection Indices ◽  
Nonlinear Trends ◽  
Climate Noise

Abstract. The multi-scale nature and climate noise properties of teleconnection indices are examined by using the Empirical Mode Decomposition (EMD) procedure. The EMD procedure allows for the analysis of non-stationary time series to extract physically meaningful intrinsic mode functions (IMF) and nonlinear trends. The climatologically relevant monthly mean teleconnection indices of the North Atlantic Oscillation (NAO), the North Pacific index (NP) and the Southern Annular Mode (SAM) are analyzed. The significance of IMFs and trends are tested against the null hypothesis of climate noise. The analysis of surrogate monthly mean time series from a red noise process shows that the EMD procedure is effectively a dyadic filter bank and the IMFs (except the first IMF) are nearly Gaussian distributed. The distribution of the variance contained in IMFs of an ensemble of AR(1) simulations is nearly χ2 distributed. To test the statistical significance of the IMFs of the teleconnection indices and their nonlinear trends we utilize an ensemble of corresponding monthly averaged AR(1) processes, which we refer to as climate noise. Our results indicate that most of the interannual and decadal variability of the analysed teleconnection indices cannot be distinguished from climate noise. The NP and SAM indices have significant nonlinear trends, while the NAO has no significant trend when tested against a climate noise hypothesis.

scholarly journals Studying Sea Level Variability in Indonesia Sea Based on Satellite Altimetry

2020 ◽  
Vol 618 ◽  
pp. 012040
Author(s):  
Dina A Sarsito ◽  
Muhammad Syahrullah ◽  
Dudy D Wijaya ◽  
Dhota Pradipta ◽  
Heri Andreas
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Sea Level Variability

scholarly journals Evolution of Sea Level of the Big Aral Sea from Satellite Altimetry and Its Implications for Water Balance

2005 ◽  
Vol 31 (4) ◽  
pp. 520-534 ◽  
Author(s):  
Jean-Francois Crétaux ◽  
Alexey V. Kouraev ◽  
Fabrice Papa ◽  
Muriel Bergé-Nguyen ◽  
Aanny Cazenave ◽  
...  
Keyword(s):  
Water Balance ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Aral Sea

The Black Sea level trends from tide gages and satellite altimetry

2013 ◽  
Vol 38 (5) ◽  
pp. 329-333 ◽  
Author(s):  
A. A. Kubryakov ◽  
S. V. Stanichnyi
Keyword(s):  
Sea Level ◽  
Black Sea ◽  
Satellite Altimetry ◽  
The Black Sea

Impact of Satellite Altimetry on Simulations of Sea Level Variability by an Indian Ocean Model

2001 ◽  
Vol 24 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. K. Singh ◽  
Sujit Basu ◽  
Raj Kumar ◽  
Vijay K. Agarwal
Keyword(s):  
Indian Ocean ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Ocean Model ◽  
Sea Level Variability

Validation of sea surface heights from satellite altimetry along the Indian coast

2021 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanian ◽  
Onkar Dikshit
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Atmospheric Correction ◽  
North Indian Ocean ◽  
Sea Surface ◽  
Indian Coast ◽  
Vertical Land Motion ◽  
And Performance

<p>Satellite altimetry provides measurements of sea surface height of centimeter-level accuracy over open oceans. However, its accuracy reduces when approaching the coastal areas and over land regions. Despite this downside, altimetric measurements are still applied successfully in these areas through altimeter retracking processes. This study aims to calibrate and validate retracted sea level data of Envisat, ERS-2, Topex/Poseidon, Jason-1, 2, SARAL/AltiKa, Cryosat-2 altimetric missions near the Indian coastline. We assessed the reliability, quality, and performance of these missions by comparing eight tide gauge (TG) stations along the Indian coast. These are Okha, Mumbai, Karwar, and Cochin stations in the Arabian Sea, and Nagapattinam, Chennai, Visakhapatnam, and Paradip in the Bay of Bengal. To compare the satellite altimetry and TG sea level time series, both datasets are transformed to the same reference datum. Before the calculation of the bias between the altimetry and TG sea level time series, TG data are corrected for Inverted Barometer (IB) and Dynamic Atmospheric Correction (DAC). Since there are no prior VLM measurements in our study area, VLM is calculated from TG records using the same procedure as in the Technical Report NOS organization CO-OPS 065. </p><p>Keywords— Tide gauge, Sea level, North Indian ocean, satellite altimetry, Vertical land motion</p>

Forecasting the risk at infractions: an ensemble comparison of machine learning approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Li ◽  
Desheng Wu
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Model Performance ◽  
Large Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Content Type ◽  
Day To Day Operations ◽  
Prediction Approach

PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

scholarly journals Prediction of benign and malignant breast cancer using data mining techniques

2018 ◽  
Vol 12 (2) ◽  
pp. 119-126 ◽  
Author(s):  
Vikas Chaurasia ◽  
Saurabh Pal ◽  
BB Tiwari
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Low Income ◽  
Prediction Models ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Low Income Countries ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Rbf Network

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.

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