Helping Reduce Environmental Impact of Aviation with Machine Learning

TL;DR

This paper explores machine learning approaches to reduce aviation's environmental impact by optimizing flight routes through improved wind forecasts and uncertainty-aware routing methods.

Contribution

It introduces a novel approach combining wind forecast improvements and uncertainty-aware routing to minimize flight times and environmental impact.

Findings

Enhanced wind forecast accuracy for flight planning.

Development of an aircraft routing method considering wind forecast uncertainty.

Potential reduction in flight times and emissions.

Abstract

Commercial aviation is one of the biggest contributors towards climate change. We propose to reduce environmental impact of aviation by considering solutions that would reduce the flight time. Specifically, we first consider improving winds aloft forecast so that flight planners could use better information to find routes that are efficient. Secondly, we propose an aircraft routing method that seeks to find the fastest route to the destination by considering uncertainty in the wind forecasts and then optimally trading-off between exploration and exploitation.