A sequential decision-making framework with uncertainty quantification for groundwater management

Groundwater contamination causes significant groundwater supply risks that severely affect humans and livestock, especially in the era of climate change. Consequently, the need to develop and optimize remediation strategies such as pump and treat is indispensable. An effective and efficient strategy can reduce the spread of contaminants and cost of clean-up. However, developing such a strategy is challenging due to the inherent uncertainty and sequential nature of the problem. For example, taking actions such as drilling wells is interwoven with information gathering over time. In this paper, we formulate the groundwater remediation planning and decision problem as a partially observable Markov decision process (POMDP). A POMDP formulation allows planning by optimizing the trade-off between information gathering and the performance of possible future scenarios. We use a state-of-the-art POMDP solver called DESPOT for planning remediation strategies. The results show that DESPOT can yield much better remediation strategies than hand crafted heuristics and optimization methods. The superior performance of DESPOT is due to the incorporation of both previous information and future reward into the current decision. We also demonstrate the robustness of the POMDP formulation to measurement error.

Automated summary

This paper proposes a method of planning groundwater remediation as a POMDP, which can derive better remediation strategies by optimizing the trade-off between information gathering and future scenario performance. Empirical results show that DESPOT outperforms handcrafted heuristics and optimization methods by incorporating both previous information and future reward.