- GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel
- Christian Albrecht University of Kiel
- MARUM - Center for Marine Environmental Sciences, University Bremen
Together, the creatures of the oceans and the physical features of their habitat play a significant role in sequestering carbon and taking it out of the atmosphere. Through the biological processes of photosynthesis, predation, decomposition, and the physical movements of the currents, the oceans take in more carbon than they release. With sediment accumulation in the deep seafloor, carbon gets stored for a long time, making oceans big carbon sinks, and protecting our planet from the devastating effects of climate change.
Despite the significance of seafloor sediments as a major global carbon sink, direct observations on the mass accumulation rates(MAR) of sediments are sparse. The existing sparse data set is inadequate to quantify the change in the composition of carbon and other constituents at the seabed on a global scale. Machine learning techniques such as the k-nearest neighbour’s algorithm have provided predictions of sparse sediment accumulation rates, by correlating known features(predictors) such as bathymetry, bottom currents, distance to coasts and river mouths, etc.
In my current work, global maps of the sediment accumulation rates at the seafloor are predicted using the known fea ture maps and the sparse dataset of sediment accumulation rates using multi-layer perceptrons(supervised models). Despite a good model accuracy, the predictions are not reliable, according to expert knowledge. Some of the main problems are the low availability of labelled data, uneven distribution(both spatially and mathematically) of sediment accumulation rates, and low knowledge about feature relevance. To understand the unreliability of predictions and the impact of the problems, model uncertainty is being studied using Bayesian neural networks.
In the presentation, the predictions using the multi perceptron model(in comparison to the previously published results using k-nearest neighbour algorithm) and the model uncertainty using Bayesian neural networks would be shown.