scholarly journals Multivariate analysis of extreme storm surges in a semi-enclosed bay

2017 ◽  
Author(s):  
Yao Luo ◽  
Hui Shi ◽  
Dongxiao Wang
Keyword(s):  
Multivariate Analysis ◽  
Storm Surges ◽  
Multidimensional Data ◽  
Practical Case ◽  
Analysis Method ◽  
Extreme Storm ◽  
Practical Guidelines ◽  
Enclosed Bay ◽  
Joint Probabilities

Abstract. The prediction of extreme storm surges is a critical task for coastal area protection. This study examines extreme storm surges in Beibu Bay, a semi-enclosed bay in the South China Sea, and their joint probabilities. A method for the advanced prediction of the extreme storm surges is proposed using a multivariate extreme statistical method. We further present practical guidelines of the proposed multivariate analysis method, including guidelines for simulation. The simulation can be extended to multidimensional data to simplify computation, so the proposed approach can be extended to use more points' data from the semi-enclosed bay for predicting extreme storm surges probabilities. A practical case study illustrates the application of the proposed techniques for extreme storm surges prediction. A comparison of the conditional probabilities obtained from observations and simulation data show that the proposed method is effective.

Metocean Design Criteria for Deep Water Offshore Systems

2015 ◽  
Author(s):  
Michele Drago ◽  
Matteo Mattioli ◽  
Federico Quondamatteo
Keyword(s):  
Multivariate Analysis ◽  
Water Column ◽  
Production Systems ◽  
Wind Waves ◽  
Practical Case ◽  
Current Profile ◽  
Design Data ◽  
Deep Waters ◽  
Surface Development

In the last decades the off-shore hydrocarbon extraction industry has extended its field of activities in very deep waters up to more than 2000 m. Extraction and production systems can vary between complete subsea development with export pipelines to on-shore treatment plants and surface development by means of surface units (SSFU) connected to subsea wells by risers and anchored by mooring systems which extend through the whole water column. For exclusively subsea developments, including sealines, the metocean design data and criteria to be developed and the applicable methodologies to derive them are well established. Univariate theory is usually applied in order to quantify the risk of failure due to (extreme) sea conditions. The surface developments and the connections through the water column (e.g. risers, moorings) are newly challenging aspects. They could suffer from severe damages due to the occurrence of critical combinations of different variables during a single sea storm:: thus, it may be important to consider the joint occurrence of different forcing conditions (i.e. multivariate analysis). The present manuscript provides a simplified methodology in order to carry out a sensible multivariate analysis of the contemporary data such as wind, waves and current. Three different cases are analyzed: i) the correlation of extremes of different variables (wind, wave and current), ii) the extreme profiles of current and iii) the current profile climate. A practical case study is illustrated throughout the paper.

scholarly journals ENSEMBLE FORECAST OF EXTREME STORM SURGE: A CASE STUDY OF 2013 TYPHOON HAIYAN

2017 ◽  
pp. 10 ◽  
Author(s):  
Ryota Nakamura ◽  
Tomoya Shibayama
Keyword(s):  
Storm Surge ◽  
Storm Surges ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Forecast System ◽  
Extreme Storm ◽  
Ensemble Mean ◽  
Almost All ◽  
Simple Numerical Model

The object of this study is to evaluate an ensemble forecast of extreme storm surge by using a case of Typhoon Haiyan (2013) and its associated storm surge. A simple numerical model composed of ARW-WRF, FVCOM and SWAN is employed as a forecast system for storm surge. This ensemble system can successfully forecast storm surge 3-4 days before it happened. However, the typhoons in almost all ensemble members were underpredicted probably because of its difficulty in forecasting a track and central pressure of highly intense typhoon. This leads to the underestimation of a prediction of storm surges around Leyte Gulf. Compensating the underestimation of forecasted extreme storm surge, it can be important to not only examine the ensemble mean among members but also consider the phase-shifted manipulation and the worst ensemble member in the case where the extreme storm surge is forecasted. In addition, the ensemble forecast system can have a potential to determine the time at which the peak of extreme surge appears with a high precision.

Blockchain in Supply Chain: Case Study of Lottemart Ciputat

2019 ◽  
Vol 5 (1) ◽  
pp. 38-49 ◽  
Author(s):  
B. K. Handoyo ◽  
M. R. Mashudi ◽  
H. P. Ipung
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Information Asymmetry ◽  
New Technology ◽  
Business Case ◽  
Analysis Method ◽  
Chain Management ◽  
Production Processes

Current supply chain methods are having difficulties in resolving problems arising from the lack of trust in supply chains. The root reason lies in two challenges brought to the traditional mechanism: self-interests of supply chain members and information asymmetry in production processes. Blockchain is a promising technology to address these problems. The key objective of this paper is to present qualitative analysis for blockchain in supply chain as the decision-making framework to implement this new technology. The analysis method used Val IT business case framework, validated by the expert judgements. The further study needs to be elaborated by either the existing organization that use blockchain or assessment by the organization that will use blockchain to improve their supply chain management.

The impact of business tourism clustering on the regional economy's development: A Greater Moscow case study

2020 ◽  
Vol 18 (7) ◽  
pp. 1397-1414
Author(s):  
K.S. Golondarev
Keyword(s):  
Multivariate Analysis ◽  
The State ◽  
Investment Climate ◽  
Private Investors ◽  
The Impact

Subject. This article explores the issues of business tourism clustering in Greater Moscow. Objectives. The article intends to justify the need to create a business tourism cluster in Greater Moscow to improve the investment climate in the region. Methods. For the study, I used a multivariate analysis, forecasting, and extrapolation. Results. The article shows a certain relationship between the efficient functioning of the business tourism cluster and the economy's development. Conclusions and Relevance. Certain types of tourist clusters can serve as platforms for attracting investors and implementing marketing plans. The business tourism cluster is a link between buyers and sellers in various industries. The results of the study can be used to improve the effectiveness of the cluster initiative in business tourism, as well as find ways of cooperation between the State and private investors when creating the business tourism cluster in Greater Moscow.

Algorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex composition

2020 ◽  
Vol 86 (7) ◽  
pp. 12-19
Author(s):  
I. V. Plyushchenko ◽  
D. G. Shakhmatov ◽  
I. A. Rodin
Keyword(s):  
Mass Spectrometry ◽  
Multivariate Analysis ◽  
Large Scale ◽  
Complex Composition ◽  
Unified Protocol ◽  
Chromatography Mass Spectrometry ◽  
Marker Substances ◽  
Selection Testing ◽  
Untargeted Profiling

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

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