Coastal Risk Forecast System

Sabino, André; Rodrigues, Armanda; Poseiro, P.; Reis, Maria Teresa; Fortes, C. J. E. M.; Reis, R.

doi:10.5220/0005469902010209

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…com/) are taken as research objects; the closing prices of solstice from January 2, 2015 to December 31, 2019 are collected as the research object. Since all of the data are time series, data preprocessing is required to ensure the stability of data before building the model by these data [25]. In this study, the logarithmic rate of yield r of various industries is selected for empirical analysis.…”

Section: Data Preprocessing and Edge Distribution Determinationmentioning

confidence: 99%

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

Liang-xiong

Muwafak

2021

Applied Mathematics and Nonlinear Sciences

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In order to accurately describe the risk dependence structure and correlation between financial variables, a scientific financial risk assessment was carried out, and the basis for accurate financial decision-making was provided; the basic theory of copula function is established first, and the mixed copula model is constructed; then, the hybrid copula model is nested in a hidden Markov model (HMM); the risk dependences among banking, insurance, securities and trust industries are analysed, and the Copula-Garch model is constructed for empirical analysis of investment portfolio; finally, the deep learning Markov model is adopted to predict the financial index. The results show that the mixed copula model based on the HMM is more effective than the single copula model and the mixed copula model. The empirical structure shows that among the four major financial industries in China, banking and insurance industries have strong interdependence and a high probability of risk contagion. The investment failure rate under 95%, 97.5% and 99% confidence intervals calculated by the Copula-Garch model is 4.53%, 2.17% and 1.08%, respectively. Moreover, the errors of the deep learning Markov model in stock price prediction of Shanghai Pudong Development Bank (sh600000), Guizhou Moutai (sh600519) and China Ping An Insurance (sh601318) are 2.56%, 2.98% and 3.56%, respectively, which indicates that the four major financial industries in China have strong interdependence and risk contagion so that the macro or systemic risks may arise, and the deep learning Markov model can be adopted to predict the stock prices.

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Section: Data Preprocessing and Edge Distribution Determinationmentioning

confidence: 99%

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

Liang-xiong

Muwafak

2021

Applied Mathematics and Nonlinear Sciences

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“…Other types of events to be forecasted for safety reasons are the wave run-up and the wave overtopping. The project HIDRALERTA [26] developed for the safeguard of the Portugal coasts is an interesting case because it is a tool for evaluating the risk, taking a decision, and eventually alerting the involved stakeholders. The explained methodology is composed by four parts and can be applied to different geographic area with different data sources:…”

Section: Floodsmentioning

confidence: 99%

The Internet of Things for Natural Risk Management (Inte.Ri.M.)

Beltramo¹,

Cantore²,

Vesce³

et al. 2019

Perspectives on Risk, Assessment and Management Paradigms

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This chapter deals with the development of a management system, which integrates the use of IoT in natural risk detection, revention, and management with economic evaluation of each stage. In the introductory part, recent data are presented that document the importance that natural disasters have for the environment and for the Italian economy. Section 2 presents the Inte.Ri.M. project-the Internet of Things for Natural Risk Management-its purpose, activity plan, and bodies involved. Technical aspects are treated in Section 3 with the choice of hardware and software components and the solutions for collecting and transmitting data. Section 4 is about the economic aspects considering the stages of prevention, intervention, and restoration and the relation between the intensity of human activity and environment to define a range of situations. These scenarios call for different economic methodologies useful to estimate economic implications of each stage in the short, medium, and long term. Section 5 describes the structure of the Inte.Ri.M. management system and the foreseen functionalities. In the conclusion, the critical points are discussed, and the steps for the transposition of the work carried out on the territory are outlined, according to the provisions of the work program.

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“…In this context, LNEC has developed a forecasting, warning and risk assessment system for overtopping and flooding events in coastal and port areas, called HIDRALERTA (Sabino et al, 2015). This system was tested in the port of Praia da Vitória, in the Azorean island of Terceira and in the Costa da Caparica area, Almada.…”

Section: Introductionmentioning

confidence: 99%

Risk Analysis and Management of Moored Ships in Ports

Pinheiro

Fortes

Santos³

2018

Volume 3: Structures, Safety, and Reliability

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The risks associated with mooring of ships are a major concern for port and maritime authorities. Sea waves and extreme weather conditions can lead to excessive movements of vessels and mooring loads affecting the safety of ships, cargo, passengers, crew or port infrastructures. Normally, port activities such as ships’ approach manoeuvres and loading/unloading operations, are conditioned or suspended based solely on weather or wave forecasts, causing large economic losses. Nevertheless, it has been shown that some of the most hazardous events with moored ships happen on days with mild sea and wind conditions, being the culprit long waves and resonance phenomena. Bad weather conditions can be managed with an appropriate or reinforced mooring arrangement. A correct risk assessment must be based on the movements of the ship and on the mooring loads, taking into account all the moored ship’s system. In this paper, the development of a forecast and warning system based on the assessment of risks associated with moored ships in port areas, SWAMS ALERT, is detailed. This modular system can be scaled and adapted to any port, providing decision-makers with accurate and complete information on the behaviour of moored ships, movements and mooring loads, allowing a better planning and integrated management of port areas.

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Coastal Risk Forecast System

Cited by 4 publications

References 5 publications

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

The Internet of Things for Natural Risk Management (Inte.Ri.M.)

Risk Analysis and Management of Moored Ships in Ports

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