With the development of marine resources, more and more marine geological disasters are encountered to the construction of marine engineering (such as submarine cables, submarine oil production equipment and oil and gas pipelines). The purpose of this paper is to study the impact of geological hazards on marine engineering in the context of machine learning. On the basis of collecting, sorting and analyzing the existing landslide, subsidence and rockfall geological disasters, the cause of block movement and the impact of geological disasters on marine engineering are studied, and the marine engineering geological environment of an oilfield in the sea area is evaluated. The landslide risk assessment model based on support vector machine is applied to the marine engineering geological environment information system. The results show that the landslide risk assessment results are consistent with the actual landslide distribution, indicating that the constructed landslide risk assessment model has a good effect.

Saleme Estevao. The Impact of Geological Hazards on Marine Engineering Based on Machine Learning. Frontiers in Ocean Engineering (2020), Vol. 1, Issue 2: 1-8. https://doi.org/10.38007/FOE.2020.010201.

[1] Biswas R N , Islam M N , Islam M N . Modeling on management strategies for spatial assessment of earthquake disaster vulnerability in Bangladesh. Modeling Earth Systems & Environment, 2018, 4(4):1377-1401. https://doi.org/10.1007/s40808-018-0507-0

[2] Hajar, Farhan, Ismael H , et al. Newly modified method and its application to the coupled Boussinesq equation in ocean engineering with its linear stability analysis. Communications in Theoretical Physics, 2020, v.72(11):13-20. https://doi.org/10.1088/1572-9494/aba25f

[3] Uffelen L , Miller J H , Potty G R . Underwater acoustics and ocean engineering at the University of Rhode Island. The Journal of the Acoustical Society of America, 2019, 145(3):1707-1707. https://doi.org/10.1121/1.5101260

[4] Tozar A , Kurt A , Tasbozan O . New wave solutions of an integrable dispersive wave equation with a fractional time derivative arising in ocean engineering models. Kuwait Journal of Science, 2020, 47(2):22-33.

[5] JF Hernández, Z Díaz, Segovia M J , et al. Machine Learning and Statistical Techniques. An Application to the Prediction of Insolvency in Spanish Non-life Insurance Companies. The International Journal of Digital Accounting Research, 2020, 5(9):1-45.

[6] Gaillat T , Ballier N , Manon Bouyé, et al. Predicting CEFR levels in learners of English: The use of microsystem criterial features in a machine learning approach. ReCALL, 2020, 34(2):130-146. https://doi.org/10.1017/S095834402100029X

[7] Sharma A , Kumar N , Kumar A , et al. Comparative investigation of machine learning algorithms for detection of epileptic seizures. Intelligent Decision Technologies, 2020, 15(2):269-279. https://doi.org/10.3233/IDT-200091

[8] Nouasse H , Chiron P , Archimede B . Contribution to a flood situation management: a supervisory control scheme to reduce disaster impact. Water Science & Technology Water Supply, 2018, 16(3):587-598. https://doi.org/10.2166/ws.2015.160

[9] Michael N J , Regan P . Psychometric evaluation of disaster impact. International Journal of Disaster Resilience in the Built Environment, 2018, 9(3):00-00. https://doi.org/10.1108/IJDRBE-01-2017-0006

[10] Naushad V A , Bierens J J , Nishan K P , et al. A Systematic Review of the Impact of Disaster on the Mental Health of Medical Responders. Prehospital and disaster medicine: the official journal of the National Association of EMS Physicians and the World Association for Emergency and Disaster Medicine in association with the Acute Care Foundation, 2019, 34(6):1-12. https://doi.org/10.1017/S1049023X19004874

[11] Shibuya Y , Tanaka H . How does a large-scale disaster impact on the used-car market? A case study of the Great East Japan Earthquake and Tsunami. International Journal of Economic Policy Studies, 2019, 13(1):89-117. https://doi.org/10.1007/s42495-018-0003-6

[12] Halik G , Putra V S , Wiyono R . Assessment of climate change impact on drought disaster in Sampean Baru watershed, East Java, Indonesia based on IPCC-AR5. Natural Hazards, 2020, 112(2):1705-1726. https://doi.org/10.1007/s11069-022-05245-7

[13] Kirsch T D , Reed M P , Strauss-Riggs K , et al. Strengths, weaknesses, opportunities, and threats that impact disaster health research. American Journal of Disaster Medicine, 2020, 15(3):169-185. https://doi.org/10.5055/ajdm.2020.0366

[14] MV Gavilánez, Bonilla W P , Castro A S , et al. The post-disaster psychological impact after a seismic threat in the urban area of the canton Jama in the province of Manabi, Ecuador. Disaster Advances, 2020, 13(2):51-54.

[15] Rodrigo N , Wilkinson S . Impact of post-disaster government policy on reconstruction: A case study of post-earthquake Christchurch, New Zealand. International Journal of Construction Supply Chain Management, 2020, 10(2):172-193. https://doi.org/10.14424/ijcscm100220-172-193

[16] Sharma B . Flood Disaster and Its Impact on Livelihood: A Case of Kandra River Basin of Kailali District. NUTA Journal, 2018, 5(1-2):56-66. https://doi.org/10.3126/nutaj.v5i1-2.23458

[17] Salgotra A K , Rajak D . June 2013 Disaster: Its Nature, Impact And Management In Uttarakhand, India. Indian Journal of Economics and Business, 2019, 18(1):1-12.

[18] Kim D K , Lee J R , Seo E J . Long-term Social Impact of Environmental Disaster: Two Fishing Villages After 11 Years from the Hebei Sprit Oil Spill Accident. Journal of Social Science, 2019, 30(4):213-235. https://doi.org/10.16881/jss.2019.10.30.4.213