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Title: Optimizing dynamics of integrated food–energy–water systems under the risk of climate change
Abstract

Integrated management of food–energy–water systems (FEWS) requires a unified, flexible and reproducible approach to incorporate the interdependence between sectors, and include the risk of non-stationary environmental variations due to climate change. Most of the recently developed methods in the literature fall short of one or more aspects in such integration. In this article, we propose a novel approach based upon fundamentals of decision theory and reinforcement learning that (1) quantifies and propagates uncertainty, (2) incorporates resource interdependence, (3) includes the impact of uncontrolled variables such as climate variations, and (4) adaptively optimizes management decisions to minimize the costs and environmental impacts of crop production. Moreover, the proposed method is robust to problem-specific complexities and is easily reproducible. We illustrate the framework on a real-world case study in Ventura County, California.

 
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Award ID(s):
1739676
NSF-PAR ID:
10306275
Author(s) / Creator(s):
; ;
Publisher / Repository:
IOP Publishing
Date Published:
Journal Name:
Environmental Research Letters
Volume:
14
Issue:
7
ISSN:
1748-9326
Page Range / eLocation ID:
Article No. 074010
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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