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

• Main Author: Aadhav Prabu (10963221)
• Format: Report
• Language: unknown
• Published: 2021
• Subjects: Computing and Processing; Reinforcement Learning (RL); Deep Q Network (DQN); State–action–reward–state–action (SARSA); Silicon Dioxide; Glacier Mass Balance; Average Accumulation; Average Ablation; Conductive heat flux; Arctic; Pacific; Antarc*; Sea ice
• Online Access: https://doi.org/10.36227/techrxiv.14774967.v3

Glaciers cover nearly 10 percent of the earth’s surface but are melting at an inexorable rate. According to the Pacific Standard magazine, the Arctic Sea ice has lost 80 percent of its volume since 1979. 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, it is necessary to determine the ideal amount of silica to achieve the desired result with minimum environmental impact. This paper introduces a novel method SPF ICE to determine the optimal amount of silica based on glacier’s properties using reinforcement learning agents and 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, temperature, and average accumulation and ablation. After testing the agents, the proposed solution reduced glacial melting by an average of 60.40% using the optimal amount of silica. The results indicate SPF ICE is a promising and cost-effective solution to curb glacier melting.