Reinforcement Learning for Sociohydrology

TL;DR

This paper explores how reinforcement learning can effectively model and solve sociohydrology problems by capturing the co-evolution of human-water systems, demonstrated through a runoff reduction case study.

Contribution

It introduces reinforcement learning as a novel framework for sociohydrology, illustrating its application and benefits in modeling complex human-water interactions.

Findings

RL effectively models sociohydrology problems

Demonstrated RL in runoff reduction management

Discussed future research directions in RL for sociohydrology

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.