Reinforcement Learning for Sociohydrology

• URL: http://arxiv.org/abs/2405.20772v1
• Date: Fri, 31 May 2024 13:28:37 GMT
• Title: Reinforcement Learning for Sociohydrology
• Authors: Tirthankar Roy, Shivendra Srivastava, Beichen Zhang,
• Abstract summary: We discuss how reinforcement learning provides an effective framework for solving sociohydrology problems. We present a simple case study to demonstrate the implementation of RL in a problem of runoff reduction.
• Score: 8.68732851119558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we discuss how reinforcement learning (RL) provides an effective and efficient framework for solving sociohydrology problems. The efficacy of RL for these types of problems is evident because of its ability to update policies in an iterative manner - something that is also foundational to sociohydrology, where we are interested in representing the co-evolution of human-water interactions. We present a simple case study to demonstrate the implementation of RL in a problem of runoff reduction through management decisions related to changes in land-use land-cover (LULC). We then discuss the benefits of RL for these types of problems and share our perspectives on the future research directions in this area.

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