Gaussian Process Regression for Arctic Coastal Erosion Forecasting

• Main Authors: Kupilik, Matthew, Witmer, Frank, MacLeod, Euan-Angus, Wang, Caixia, Ravens, Tom
• Format: Report
• Language: unknown
• Published: arXiv 2017
• Subjects: Geophysics physics.geo-ph; Machine Learning stat.ML; FOS Physical sciences; FOS Computer and information sciences; Arctic; Beaufort Sea; Ice; permafrost
• Online Access: https://dx.doi.org/10.48550/arxiv.1712.00867; https://arxiv.org/abs/1712.00867

Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shorefast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern, since the majority of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers increasingly need models capable of scenario-based predictions to assess and mitigate the effects of coastal morphology on infrastructure and land use. Our research uses Gaussian process models to forecast Arctic coastal erosion along the Beaufort Sea near Drew Point, AK. Gaussian process regression is a data-driven modeling methodology capable of extracting patterns and trends from data-sparse environments such as remote Arctic coastlines. To train our model, we use annual coastline positions and near-shore summer temperature averages from existing datasets and extend these data by extracting additional coastlines from satellite imagery. We combine our calibrated models with future climate models to generate a range of plausible future erosion scenarios. Our results show that the Gaussian process methodology substantially improves yearly predictions compared to linear and nonlinear least squares methods, and is capable of generating detailed forecasts suitable for use by decision makers.