SPF ICE: A Novel Approach to Model the Amount And Effectiveness of Silica to Preserve Glaciers Using Reinforcement Learning

Abstract

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 several years. The results indicate SPF ICE is a promising and cost-effective solution to curb glacier melting.