本文選題：海上人命損失　切入點：規(guī)律　出處：《大連海事大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)前階段海上交通安全特點已經(jīng)發(fā)生顯著變化,研究當(dāng)下海上人命損失特點,對于提高海上人命安全防護能力具有重要意義。論文在既往研究的基礎(chǔ)上,明確了相關(guān)的研究目標、思路和任務(wù),圍繞海上人命損失規(guī)律、趨勢和可接受標準等開展了以下幾方面的研究。首先,研究海上人命損失的分布、時間、形態(tài)和致死率特征,定量分析海上人命損失的總體規(guī)律。其次,利用Apriori算法對造成海上人命損失的事故屬性進行了多維關(guān)聯(lián)規(guī)則挖掘研究,發(fā)現(xiàn)了關(guān)于海上交通事故與船上操作事故間的重要關(guān)聯(lián)規(guī)則,揭示了海上交通安全與船上操作安全具有緊密聯(lián)系,提出了預(yù)防海上人命損失的對策。再次,研究了基于自回歸滑動平均模型(Autoregressive Integrated Moving Average Model, ARIMA)和支持向量機(Support Vector Machine, SVM)的海上人命損失組合預(yù)測模型。預(yù)防海上人命損失一直是海上交通安全研究和實踐的焦點。針對現(xiàn)有海上人命損失預(yù)測方法的不足,提出了一種基于ARIMA模型和SVM方法相結(jié)合的預(yù)測模型。首先采用ARIMA模型對時間序列線性部分建模分析,然后采用SVM模型對非線性部分進行建模。經(jīng)驗證,組合模型相對于單模型的預(yù)測具有更高的精度,該組合預(yù)測模型是一種有效的海上人命損失預(yù)測模型。最后,應(yīng)用帕累托(Pareto)最優(yōu)等理論研究了海上個人風(fēng)險可接受標準(Marine Individual Risk Acceptance Criteria, MIRAC) o針對當(dāng)前海上交通領(lǐng)域尚未建立一個被廣泛接受的風(fēng)險標準的現(xiàn)狀,在定義MIRAC概念和閾值確定原則的基礎(chǔ)上,基于Pareto最優(yōu)原理確定了符合實際的MIRAC閾值,回答了海上交通安全系統(tǒng)“到底多安全才夠安全”的經(jīng)典問題。
[Abstract]:The characteristics of the current stage of maritime traffic safety has changed significantly, the current research of loss of life at sea, has an important significance for improving the safety of life at sea protection. Based on the previous research, the research target, ideas and tasks around the loss of life at sea, trend and acceptance criteria to carry out research in the following aspects. Firstly, the distribution of life at sea, the loss of time, morphology and mortality characteristics, quantitative analysis of overall loss of life at sea. Secondly, the accident caused loss of life at sea attribute of multidimensional association rule mining based on Apriori algorithm, found out about the operation of important association rules between the maritime traffic accident the accident with the boat, reveals is closely related to safe operation of maritime traffic safety and ship, puts forward some countermeasures for preventing the loss of life at sea. Again, the research on the autoregressive moving average model (Autoregressive Integrated Moving Average Model, ARIMA) and support vector machine (Support Vector Machine, SVM) the loss of life at sea, the combination forecasting model. To prevent loss of life at sea has been the focus of maritime traffic safety research and practice. Aiming at the shortage of the existing prediction methods of the loss of life at sea. Put forward a kind of ARIMA model and SVM method based on the combination forecast model. Firstly, by the analysis of ARIMA model of linear time series modeling, and then use SVM model to model the nonlinear part. After verification, compared to the single model prediction model combined with higher accuracy, the combination forecasting model is an effective sea loss of life prediction model. Finally, the application of Pareto (Pareto) optimal theory to study the risk of maritime personal acceptance criteria (Marine Individual Risk Acceptance Criteria, MIRAC o) according to the current status of maritime traffic field has not yet established a widely accepted risk criteria, based on determining the principles define the concept of MIRAC and the threshold, the Pareto optimal principle to determine the MIRAC threshold based on the practical, answered the classic problem of maritime traffic safety system is safe enough security ".

【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：U698

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