Sedimentation rates in the Baltic Sea: A machine learning approach. (1st February 2021)

• Record Type: Journal Article
• Authors: Mitchell, P.J.; Spence, M.A.; Aldridge, J.; Kotilainen, A.T.; Diesing, M.
• Is Part Of: Continental shelf research. Volume 214(2021)
• Publication Date: 2021-02-01
• Subjects: Sediment -- Sedimentation rate -- Baltic Sea -- Machine learning; Continental shelf -- Periodicals; Submarine geology -- Periodicals; 551.41
• DOI: 10.1016/j.csr.2020.104325
• Languages: English
• ISSNs: 0278-4343

Abstract

Abstract: Sedimentation rate data are applied in a range of studies such as understanding the sediment carbon budget and accumulation of pollutants in marine sediments. Over many years, sedimentation rate samples have been collected within the Baltic Sea. However, to understand patterns in sedimentation rates more broadly across this sea basin requires converting these point data into continuous spatial predictions. The generation of continuous maps remains problematic and under-studied. This study explores the feasibility of machine learning to estimate sedimentation rates, using 137 Cs measurements from the Baltic Sea. A random forest model was applied to predict sedimentation rates based on a range of predictor variables that related to the hydrodynamic regime, bathymetric complexity of the seabed, substrate type and proximity to sediment sources. The accuracy of this prediction was tested against an independent set of sedimentation rate samples that had been withheld from model training. The model was also compared against a simple spatial interpolation to assess whether machine learning produces an improvement on previously applied methods. Overall, the modelling approach explained 41.9% of the variance, far surpassing the spatial interpolation method (4.2% variance explained). This study is a first step towards the spatial prediction of sediment accumulation rates in a repeatable and validated way, but further refinement is desirable to improve the accuracy of the predictions. We discuss the potential sources of model error that could have limited the success of this approach and suggest how some of these could be addressed. Our results indicate that short-term sedimentation rates are highest in small coastal basins, while rates in the deep basins of the Baltic Sea are generally low, thereby seemingly contradicting long held views of the deep-basins as the major depocentres in the Baltic Sea. This apparent contradiction might be attributed to the higher spatial detail of our analysis and differences in the time scales of analysis, indicating that sedimentation patterns in the Baltic Sea might be complex in space and time. Highlights: Machine learning was used to spatially predict sedimentation rates across the Baltic Sea. Initial results are promising, as the model outperformed simpler predictive methods. Potential improvements to predicting sedimentation rates are discussed. Results point at spatially and temporally complex sedimentation patterns in the Baltic Sea.