SPF ICE: A Novel Approach to Model the Amount And Effectiveness of
Silica to Preserve Glaciers Using Reinforcement Learning
Glaciers cover nearly 10 percent of the earth’s surface but are melting
at an inexorable rate. Antarctica’s Doomsday Glacier’ is melting faster
and could raise global sea levels by two feet. As three-quarters of the
earth’s fresh water is stored in glaciers, its melting depletes
freshwater resources for millions of people. Glaciers also play a huge
role in the climate crisis. Silica microspheres are promising materials
to prevent glacier melting as it reflects most of the sun’s radiation.
When spread in layers over the glacier, it can slow the rate of melt and
aid in new ice formation. However, currently, no modeling is available
to show the amount of silica needed and its effectiveness in advance.
This paper introduces a novel method SPF ICE that models the silica
amount based on glacier’s properties by testing reinforcement learning
agents in a custom OpenAI Gym environment. The environment simulates a
real-world model of a glacial setting using specific data, such as the
glacier’s mass balance, average accumulation, and ablation. After
testing RL agents like DQN and SARSA, the proposed solution modeled the
silica amount that reduced glacial melting by an average of 60.40%
extending its lifetime by many years. The results indicate SPF ICE is a
promising and cost-effective solution to curb glacier melting.