Gaussian Process Regression for Arctic Coastal Erosion Forecasting

TL;DR

This paper presents a Gaussian process regression model to forecast Arctic coastal erosion, integrating satellite data and climate models to improve prediction accuracy for decision-making.

Contribution

The study introduces a Gaussian process-based approach for Arctic erosion forecasting, enhancing prediction accuracy over traditional methods in data-sparse environments.

Findings

Gaussian process models outperform linear and nonlinear least squares in accuracy.

The model can generate detailed future erosion scenarios.

Forecasts are suitable for decision-making applications.

Abstract

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.