## 4 Conclusions  
In this study, a two-dimensional hydrodynamic model and an oil spill model of the Lancang River crude oil crossing pipeline basin were developed based on Mike 21. The oil spill transport behavior was simulated under different river flow rates, wind speeds and directions, and spill modes. The oil spill transport state dataset was established, and the oil spill transport state prediction model was built using a combined machine learning algorithm. The main conclusions are as follows:  
1. For the two-dimensional hydrodynamic model of the Lancang River crude oil crossing pipeline basin, the flow velocity and water level of the model were verified according to the measured hydrological data. The _MAE_ of the flow velocity is 0.054 m/s, and the _MAE_ of the water level is 0.062 m. The simulated values are in good agreement with the actual values. It shows that the model is suitable for the study of oil spill transport behavior in this basin.
2. The transport behavior of oil spills under different spill scenarios was simulated. It was found that the higher the river flow, the faster the migration, diffusion, and longitudinal extension behavior of the oil spill; under downwind and headwind conditions, the effects of wind speed on the oil spill transport behavior are small. In the remaining wind directions, the migration and diffusion behaviors of the oil spill will slow down with the increase of wind speed, and the longitudinal extension behaviors of the oil spill will speed up firstly and then slow down with the increase of wind speed; the effects of leakage mode on the migration rate of the oil spill are small. Under the perforation leakage, the increase in oil spill volume will make the oil spill diffuse and extend longitudinally in the river channel faster. The simulation results can provide guidance for the development of emergency response plans for river crossing pipeline oil spill accidents.
3. An oil spill transport state dataset was established through 320 sets of numerical experiments. The oil spill transport state prediction model was established by machine learning combination algorithms. The three machine learning algorithms, PSO-SVR, GA-BPNN, and PSO-BPNN, have the best performance in predicting the oil spill migration distance, oil spill area, and oil spill contaminated zone length, respectively, with the _R\({}^{2}\)_ and _1-MAPE_ above 0.971, and the accuracy of the prediction model is high. The model can provide support for rapid emergency response to sudden oil spill accidents of river crossing pipelines.  
Conceptualization, J.L.; Methodology, L.C.; Software, J.L.; Validation, J.L.; Formal analysis, D.X.; Data curation, D.X.; Writing--original draft, J.L.; Supervision, L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 15: Difference between predicted and actual values of the three combined models.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets presented in this article are not readily available because the data is part of the subject being researched. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
**Conflicts of Interest:** The authors declare no conflict of interest.