Abstract
Numerical oil spill models, which predict the transport and behavior of oil spills, are an essential tool for risk assessment and clean-up during an actual accident. The existing numerical oil spill models are mainly applied to large-scale oil spills, while few models on small-scale oil spills exist. Therefore, this study focuses on the prediction model of small-scale oil spills. Oil diffusion experiments in seawater using different oil types, including heavy oil, light oil, and gasoline, at different addition amounts under various kinds of wind were carried out, and these diffusion processes were recorded by a camera. The experimental images were processed to obtain the spread oil film area. The oil film edge processing based on genetic algorithm (GA) and back propagation artificial neural network optimized by a particle swarm optimization (PSO-BP) is proposed. Numerical prediction models were then constructed using the BP artificial neural network, the genetic algorithm-optimized back propagation neural network (GA-BP), and the PSO-BP. Among the three methods, the PSO-BP has the fastest convergence speed and the highest stability, which can usually achieve the goal. The PSO-BP reduces the possibility of the BP-ANN and the GA-BP falling into a local optimum instead of reaching global optimization. The prediction performance evaluation data are R2 = 1 and MSE = 3.58e−9 – 8.87e−8. Results show that the GA and the PSO-BP provide a new approach to small-scale oil spill prediction.
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The data that support the findings of this study are available on request from the corresponding author.
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Acknowledgements
We thank Sinochem Xingzhong Petroleum Transportation (Zhoushan) Co., Ltd for crude oil sample support.
Funding
This research was funded by the National Natural Science Foundation of China (No. U1809214), Zhejiang Province Public Welfare Technology Research Project (No. LGF20D060001, No. LGF22D060003), the Open Research Subject of Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control (No. 2021Y04) and the Fundamental Research Funds for the Provincial Universities in Zhejiang Province (No. 2021J002).
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Q.C. contributed to conceptualization; X.C., X. H., and Q.C. contributed to methodology; X.C., X. H., Z.L. J. L., and C. G. contributed to investigation; X.C. and X. H. C. G. contributed to writing—original draft preparation; M. L., B. Z., Q. L., and Q.C. contributed to writing—review and editing; B. Z., M. L., Q.C., Q. L., and X. H. acquired funding.
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Cheng, X., Hu, X., Li, Z. et al. Using Genetic Algorithm and Particle Swarm Optimization BP Neural Network Algorithm to Improve Marine Oil Spill Prediction. Water Air Soil Pollut 233, 354 (2022). https://doi.org/10.1007/s11270-022-05771-x
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DOI: https://doi.org/10.1007/s11270-022-05771-x