Chapter 1 Introduction
Research on the collision risk of ships in China’s waters is an important topic that has received significant attention in recent years. This article aims to explore the use of support vector machines (SVMs) in analyzing and predicting the collision risk of ships in China’s waters.
1.1 Research background
Maritime traffic in China’s waters has been increasing in recent years, leading to increased concerns about the safety of ships navigating these waters. Collision incidents between ships can result in significant damage to both the vessels involved and the surrounding environment. As such, it is important to develop methods for analyzing and predicting the collision risk of ships in China’s waters.
1.2 Purpose and significance of the research
The purpose of this research is to develop a method for analyzing and predicting the collision risk of ships in China’s waters based on SVMs. This research is significant as it will contribute to the understanding of the factors that contribute to the collision risk of ships in China’s waters and help to develop strategies for reducing this risk.
1.3 Overseas and domestic research status
1.3.1 Research status of ship risk analysis
There has been a significant amount of research on ship risk analysis in both domestic and international literature. Much of this research has focused on the use of statistical methods and mathematical models to analyze and predict the collision risk of ships.
1.3.2 Research status of SVM
SVMs have been widely used in a variety of fields, including image recognition, natural language processing, and bioinformatics. However, the application of SVMs to ship risk analysis is relatively new. There have been a few studies that have used SVMs to analyze and predict the collision risk of ships, but these studies have mainly focused on the use of SVMs in conjunction with other methods.
1.3.3 Research status of Brownian motion model
The Brownian motion model is a mathematical model that has been widely used in ship risk analysis. This model is based on the assumption that the motion of ships is random and can be described by a stochastic process. The Brownian motion model has been used to analyze and predict the collision risk of ships in a variety of settings, including in straight channels and in cross-channels.
1.4 Research thoughts and methods
This research will use a combination of the Brownian motion model and SVMs to analyze and predict the collision risk of ships in China’s waters. The Brownian motion model will be used to model the motion of ships, while SVMs will be used to analyze and predict the collision risk based on this motion. The data used in this research will be collected from real-world ship collision incidents in China’s waters.
Chapter 2 A brief introduction of the TSS in China waters
2.1 Overview of the route in Chengshanjiao water
2.2 Investigation and analysis of Marine traffic situation in China water
2.3 Investigation and analysis of the traffic accidents in China water
2.4 Chapter summary
Chapter 3 Introduction and modelling of ship collision probability algorithm
3.1 Establishment of Brownian motion model
3.1.1 The basic assumptions of the model
3.1.2 Collision probability model with the same direction
3.1.3 Collision probability model under cross-channel
3.2 Establishment of IWRAP model
3.2.1 Geometric collision model of straight channel
3.2.2 Geometric collision model of cross channel
3.3 Chapter summary
Chapter 4 Support vector machine algorithm introduction and modelling
4.1 Establishment of SVM
4.1.1 Support vector machine principle introduction
The SVM algorithm is a supervised machine learning algorithm that is used for classification and regression tasks. The algorithm is based on the concept of finding a hyperplane that maximally separates different classes in a high-dimensional feature space. In the case of regression, the SVM algorithm is used to find a hyperplane that best fits the data, allowing for accurate predictions of the output variable.
4.1.2 Linearly separable
When the data is linearly separable, the SVM algorithm can find a linear decision boundary that separates the different classes in the data. This is achieved by finding the hyperplane that maximizes the margin, which is the distance between the decision boundary and the closest data points from each class.
4.1.3 Linearly non-separable
When the data is not linearly separable, the SVM algorithm can still be used by introducing non-linearity through the use of kernel functions. These functions map the data into a higher-dimensional feature space, where a linear decision boundary can be found. Common kernel functions include the polynomial kernel, radial basis function (RBF) kernel, and sigmoid kernel.
4.1.4 Nonlinear situation
In nonlinear situations, the SVM algorithm can be used to find a non-linear decision boundary by using kernel functions. These functions map the data into a higher-dimensional feature space, where a non-linear decision boundary can be found. This allows the SVM algorithm to accurately classify or predict outcomes for nonlinear data.
4.2 Classification of the consequences of ship collision accidents
In this research, the SVM algorithm will be used to classify the consequences of ship collision accidents. The classification will be based on factors such as the type of ship, the speed of the ships at the time of the collision, and the location of the collision.
4.3 Determination of Assessment indicators
The Assessment indicators used in this research will include accuracy, precision, recall, and F1-score. These indicators will be used to evaluate the performance of the SVM algorithm in classifying the consequences of ship collision accidents.
4.4 Selection and disposal of training samples
The training samples used in this research will be selected from real-world ship collision incidents that have occurred in China’s waters. The samples will be randomly divided into a training set and a test set.
4.5 Establishment of analysis model of ship collision consequence
The SVM algorithm will be used to establish an analysis model of ship collision consequences based on the training samples. The model will be trained using the training set and then tested using the test set.
4.6 Verification of ship collision consequence analysis model
The performance of the ship collision consequence analysis model will be verified by comparing the predicted outcomes to the actual outcomes for the test set. The Assessment indicators discussed in 4.3 will be used to evaluate the performance of the model.
4.7 Chapter summary
This chapter discussed the use of the SVM algorithm in analyzing and predicting the collision risk of ships in China’s waters. The SVM algorithm was used to classify the consequences of ship collision accidents and establish an analysis model based on real-world data. The performance of the model was verified using Assessment indicators and the chapter concluded with a summary of the methods used.
Chapter 5 Real evidence analysis
5.1 Data collection and preprocessing
5.2 Analysis of ship collision accident data using the established model
5.3 Comparison with traditional methods
5.4 Chapter summary
In conclusion, this research proposed a method for analyzing and predicting the collision risk of ships in China’s waters based on the combination of the Brownian motion model and support vector machines (SVMs). The Brownian motion model was used to model the motion of ships, while the SVM algorithm was used to classify the consequences of ship collision accidents and establish an analysis model based on real-world data. The performance of the model was verified using Assessment indicators, and the results were compared to traditional methods. Through this research, it was found that using SVMs in conjunction with the Brownian motion model can provide a more accurate and efficient method for analyzing and predicting the collision risk of ships in China’s waters. This can help to enhance the safety of maritime traffic and reduce the potential damage caused by ship collisions.
Bibliography
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