Vessel gross tonnage can represent the overall internal volume of a ship in the maritime industry, including all spaces, such as cargo areas, living quarters, and on-board spaces. However, the vessel gross tonnage can be limited to a highly complex calculation and low accuracy in general. In this study, an optimal formula was proposed to integrate feature engineering with multiple algorithms using machine learning. Initially, the feature variables were mined using the structural parameters of the ship. A correlation analysis was also conducted on the feature variables. Their correlation was also determined for the Pearson correlation coefficients between the feature variables and the gross tonnage. Three types of nonlinear regressions were performed on the gross tonnage: The linear multiplicative model, the sub-item exponential model, and the hybrid model. The nonlinear relationship between the feature variables and the gross tonnage was then obtained after optimization. Subsequently, the dataset of 1 913 fishing vessels in the South China Sea region was divided into the training and testing sets in a ratio of 8:2. Specifically, the dataset included 448 trawlers, 479 purse seiners, 440 gillnetters, 237 setnetters, and 309 longliners. Regression of the vessel gross tonnage was also performed using Backpropagation Neural Network (BPNN) and Random Forest (RF). Model fitting and validation were then conducted using the nonlinear least squares method (LSM) and particle swarm optimization (PSO). Ultimately, the robustness tests were carried out to verify the models. Different models were also selected with the various noise intensities of 0%, 3%, 5%, 10%, and 20%. The results showed that the Pearson correlation coefficients between the feature variables (length L, width B, and dapth D) and the gross tonnage (GT) were 0.937 7, 0.820 4, and 0.932 7, respectively. These values were all close to 1, indicating a strong correlation between L, B, D, and GT. Therefore, the L, B, and D were selected as the feature variables. Furthermore, RF outperformed BPNN in various error metrics, including mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R²), indicating a higher accuracy of the prediction on the gross tonnage of fishing vessels. The nonlinear LSM outperformed the PSO in various evaluation metrics, including mean bias error (MBE), MAE, MAPE, and computational efficiency. The hybrid GT prediction model also outperformed the linear multiplicative model and the sub-item exponential model. Moreover, there was significantly better interference resistance under different noise intensities, compared with the linear product and the sub-item exponential model. The values of the MBE, RMSE, MAE, MAPE, and R² were -1.244 4, 32.036 2, 24.481, 9.94%, and 0.961 9, respectively, indicating better generalization and robustness. Vessel gross tonnage was calculated to integrate the feature engineering with multiple algorithms. A comparison was also made on the actual ship gross tonnage, according to the International GT measurement formula, and the prediction. The higher accuracy was achieved to validate the effectiveness of the prediction. Artificial intelligence and machine learning were employed to optimize the prediction for the gross tonnage of the fishing vessels, significantly improving the accuracy of the calculations. This finding can provide valuable references for the digital and intelligent management of the fishing vessels in the fisheries.

Potential knowledge can be extracted from the safety text of distant water fishing (DWF) vessels. However, the previous approaches have not yet been fully developed for the safety text of fishing vessels. Some challenges remained, such as the low accuracy of text classification, and insufficient depth of knowledge extraction. In this study, an analytical approach was proposed to combine text classification, knowledge mining, and co-occurrence network technology under the Cape Town Agreement(CTA) of 2012 . The text data on DWF vessel safety was also collected from the fishery management organizations, associations, and over 20 fishery enterprises from eight Chinese coastal provinces and cities, including Zhejiang, Shanghai, and Fujian. The DWF vessel safety corpus consisted of more than 5,000 valid questions and 100,000 characters. The analytical approach comprised three stages. Firstly, a hybrid deep learning model was developed using bidirectional encoder representations from transformers-text convolutional neural networks (BERT-TextCNN), according to the characteristics of DWF vessel safety text, such as diverse data types, sparse data features, and fuzzy boundaries. The character vectors were generated to extract the contextual semantic and deep syntactic information of the text using BERT during text representation. Multiple convolutional kernels of TextCNN were utilized to spatially model the generated character vectors and then to extract the local features for the accurate classification of safety theme. Secondly, term rrequency-inverse document frequency (TF-IDF) was employed to extract the key safety knowledge of fishing vessels, considering the importance and prevalence of knowledge within each safety theme. Finally, a co-occurrence network was constructed to visualize the safety knowledge of fishing vessels, including distributional patterns and interconnections. The results show that the BERT-TextCNN model achieved an accuracy, macro average recall rate, and macro average F₁ value of 98.20%, 98.02%, and 98.05%, respectively. The performance outperformed the other 17 comparative models, which utilized three text representations (BERT, Word2vec, and Character embedding) and six neural networks (TextCNN, Softmax, DPCNN, BiLSTM-Attention, RCNN, and Transformer). Meanwhile, the theme-based knowledge mining and analytical approach achieved clear rankings of DWF vessel compliance and safety management knowledge, as well as relationship networks crossing ten safety knowledge themes of fishing vessels, including provisions, structure, stability, electrical installations, fire protection, crew protections, life-saving equipment, emergency procedures, wireless communication, and shipborne navigation equipment. Intelligent safety knowledge services and decision-making tools were obtained to improve the compliance level and safety management efficiency in DWF. The finding can provide a strong reference to promote the application and development of knowledge service systems and the smart fishing industry.

A monitoring system is required for the offshore fishing capacity. Mathematical modelling can also be applied to analyze the impact of fishing vessel parameters on fishing capacity, from the perspectives of fishery engineering and ship design. However, it is still lacking in the fishing capacity of a single vessel. Only a few influencing factors have been considered, leading to the insufficient analysis of the subjective and objective weights of each influencing factor. Consequently, there is a high demand for the quantitative fishing capacity of a fishing vessel. In this study, an assessment model of single-vessel fishing capacity was proposed to consider the multiple influencing factors. The fishing data was also collected to conduct modelling analysis. Firstly, a comprehensive analysis was made to determine the impact of each fishing vessel parameter on fishing capacity. An evaluation index system of single-vessel fishing capacity was then established to combine the opinions of experts and fishermen in the fields. The indicators were divided into the quantifiable and non-quantifiable ones. Preliminary evaluation criteria were also formulated for the non-quantifiable indicators. Secondly, a questionnaire titled "Questionnaire on the Weight of Factors Affecting the Fishing Capacity of China's Offshore Fishing Vessels" was distributed to collect the scoring data from experts and fishermen. The analytic hierarchy was used to calculate the weights of each indicator. The results indicated that the weights of each indicator were as follows: fishing equipment (0.108), power (0.094), trawl (0.074), operating time (0.071), total tonnage (0.049), fish detection equipment (0.047), gillnet (0.040), captain (0.032), net (0.028), purse seine (0.024), fishing (0.021), operating environment (0.019), steel material (0.019), cover net (0.017), fiberglass material (0.016), ship age (0.013), wooden material (0.012). The weights of fishing gear-related indicators were: main size of net gear (0.402), net structure (0.149), assembly technology (0.093), and manufacturing materials (0.051). Finally, the advantages of subjective and objective weights were given to combine the subjective weights from the analytic hierarchy process (AHP) and the objective weights from the Random Forest using the additive synthesis, and game theory. The minimum discriminant information was obtained from the target weights, in order to facilitate further analysis and calculations in the following sections. A validation analysis was then conducted. Given that the fishing vessel capacity assessment model was suitable for the motorized fishing vessels, a random sample of data from 12 motorized fishing vessels was collected for validation. Spearman rank correlation coefficient was used to calculate the graded correlation coefficient between the fishing capacity evaluation and the actual catch. The results revealed that the outcomes with the game theory shared the highest correlation, with a correlation coefficient as high as 0.937. Therefore, the combined weights were used as the target for the evaluation indicators. The finding can greatly contribute to the scientific management of offshore fisheries, in order to alleviate the overfishing in the sustainable marine industry.

Fishing vessels are closely related to the energy efficiency in the sustainable maritime industry. However, some challenges are still remained to evaluate the energy efficiency of fishing vessels, particularly on the limited data of sample sizes. It is also lacking on a specific quantification formula for the energy efficiency of existing fishing vessels. Furthermore, the EEXI (energy efficiency existing ship index) standards that developed by the International Maritime Organization (IMO) cannot include the current fishing vessels. In this study, a reference line model was constructed for the EEXI of fishing vessels. Various types of fishing vessels were also selected, including trawlers, gillnetters, and purse seine vessels. The key parameters were then evaluated, such as the total tonnage, the number of main engines, total engine power, and cruising speed. Finally, the comparison was made using the nonlinear least squares and feedforward neural networks. The results demonstrate that the nonlinear least squares outperformed the feedforward neural networks over the multiple evaluation metrics. Specifically, mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R² were found to be 131.20 (g/(t∙nm))², 7.77 g/(t∙nm), 11.45 g/(t∙nm), 15% and 0.63, respectively, when using nonlinear least squares to fit the fishing vessel EEXI reference line formula. The excellent generalization and robustness were achieved reliable for the practical applications. In addition, a case study was carried out to verify the effectiveness of the improved model. The practical applicability was further validated in the real-world scenarios. As such, the reference line calculation model was suitable for the EEXI of existing fishing vessels. New insights and technical support were provided to evaluate the energy efficiency of fishing vessels. A data-driven research approach was also adopted to improve the energy efficiency of fishing vessels. The findings can greatly contribute to promote the IMO’s standards in the sustainable maritime industry.