In the context of the decarbonization of the shipping industry, the application of clean energy technologies is a catalyst for decarbonization. With the number of potential clean energy technologies expanding, the uncertainties in terms of technology maturity, policy regulation, and economics make clean energy technologies decision much more difficult. Therefore, it is urgent to establish a clean energy technology selection scenario for the green ship industry to assist shipowners in decision-making. Based on this, a technology selection model based on rough set (RS) and approximate ideal solution ranking (TOPSIS) is constructed. Using RS to reduce the evaluation index and calculate the weight can avoid the one-sidedness of subjective weighting. Using the TOPSIS method to rank alternatives. This paper selects seven clean energy technology alternatives, namely LNG power, LPG power, methanol power, HVO power, pure battery power, hydrogen fuel cell, and ammonia fuel cell, respectively, as the evaluation objects. Taking two types of vessels as examples, it is concluded that LNG power technology is suitable for large coastal ro-ro passenger vessels, and pure battery power technology is suitable for small inland river short-distances vessels. The results are in line with reality, which verifies the scientificity and validity of the proposed model.

Seafarers are prone to reduce behavioral reliability under high workloads, resulting in human errors and accidents. To explore the changes in seafarers’ workload and physiological activities under complex task conditions, a bridge simulator experiment was conducted to collect the EEG and ECG data of 23 seafarers. The power in different EEG sub-bands was extracted from a one-channel EEG acquisition headset employed by Welch’s method and ratio processing. The features such as root mean square of RR interval difference (RMSSD) were extracted from ECG. Then, an improved seafarer workload recognition method based on EEG combined with ECG and complex task scenarios was proposed, and the performance of the machine learning algorithm was evaluated by cross-validation. Compared with the recognition model that only uses the task scenarios as the workload calibration, the EEG recognition model based on the workload level calibrated by the ECG and the task scenarios is more effective, with an accuracy rate of 92.5%, an increase of 25.9%. The results show that the improved workload recognition model can effectively identify seafarers’ workload, and the model trained with the bagging algorithm has the best performance. Furthermore, time domain features of EEG and ECG fluctuate regularly with the task scenarios’ complexity. The research results can develop online intelligent monitoring, and human–computer interaction active early warning technology and equipment.

Water transportation accidents have occurred frequently in recent years. In order to improve the emergency response capability of water transportation systems under traffic-intensive conditions, this paper identifies and analyzes the vulnerability in traffic-intensive areas of water transportation systems. Firstly, the vulnerability identification model was constructed based on the analysis of characteristics and the vulnerability-influencing factors of water transportation systems. The newly proposed model is composed of three parts including the DEMATEL (Decision Making Trial and Evaluation Laboratory) method, ISM (interpretative structural modeling) model, and AHP (Analytic Hierarchy Process)–entropy weight method. Finally, a case study of the Yangtze River was conducted to test the logicality and feasibility of the proposed model. The research results reveal that traffic flow density, ship traffic, tides, fog, and bad weather are the key factors affecting the vulnerability of water transportation in traffic-intensive areas of the Yangtze River estuary. However, the influence of navigation aid configuration, berth, anchorage, and obstruction on the system vulnerability is relatively lower. The findings of this study can provide helpful references for maritime administration authorities on the management of water transportation safety.

Navigation safety improving investment aims at mitigating risk and improving safety of shipping system, while decision-makers’ attitudes toward the uncertainty of shipping safety possess a characteristic of “bounded rationality.” To study the tendency of shipping safety investment decision-making with different risk perception and appetite, a decision-making method based on cumulative prospect theory is proposed in this article. First, we extract the decision attributes through analyzing the factors affecting shipping safety investment. Then, according to cumulative prospect theory, the value function and the probability weighting function for calculating cumulative prospect values of shipping investment attributes are given. Under the risk-based multi-attribute group decision-making framework, linear programming model and projection method are introduced to aggregate the weights of attributes and decision-makers, respectively. Furthermore, through a case study, the proposed methodology is utilized in Three Gorges Dam area, and the desirable safety investment scheme is determined from a set of candidate alternatives. The case study shows not only validity and feasibility of the decision-making approach but also the mechanism of shipping safety investment decision-making with consideration of the behavior characteristics of decision-makers such as reference dependence, risk appetite distortion, and loss aversion.