气候变化领域集成服务门户(CCPortal): Adaptive water management in the face of uncertainty: Integrating machine learning, groundwater modeling and robust decision making

CCPortal

DOI	10.1016/j.crm.2021.100383
	Adaptive water management in the face of uncertainty: Integrating machine learning, groundwater modeling and robust decision making
	Miro M.E.; Groves D.; Tincher B.; Syme J.; Tanverakul S.; Catt D.
发表日期	2021
ISSN	2212-0963
卷号	34
英文摘要	This study examines climate change and water resources management challenges facing water suppliers in drought-prone regions and is particularly relevant to the American West, where agencies balance the management of imported and local water resources across multiple future uncertainties. We apply Robust Decision Making (RDM) to water management planning challenges facing the San Bernardino Valley Municipal Water District (Valley District) and investigate the performance of a machine learning-based representation of two local groundwater basins. To do so, we assess three machine learning methods–Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Networks (ANN)–and their ability to simulate the output of a high-resolution MODFLOW model. We find that RF produces the most accurate results, and thus we incorporate the RF version of the MODFLOW model into the study's RDM approach. This constitutes an advancement to the field of decisionmaking under deep uncertainty (DMDU) through a novel application of machine learning that shortens modeling run times and allows for a greater exploration of the uncertainty space, including a broad range of future climate changes and drought conditions. This paper also constitutes an advancement to the field of empirical groundwater modeling by showing that RF is capable of simulating average basin groundwater level changes. Our results also suggest that demand management can significantly reduce vulnerabilities to drought and other climate changes, and we provide recommendations on additional adaptive management options and key signposts to track for the Valley District. © 2021
英文关键词	Climate adaptation; Climate change; Drought; Groundwater; Machine learning; Random forest; Robust decision making; Southern California; Water resources management
语种	英语
来源期刊	Climate Risk Management
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/256103
作者单位	RAND Corporation, United States; San Bernardino Valley Municipal Water District, United States
推荐引用方式 GB/T 7714	Miro M.E.,Groves D.,Tincher B.,et al. Adaptive water management in the face of uncertainty: Integrating machine learning, groundwater modeling and robust decision making[J],2021,34.
APA	Miro M.E.,Groves D.,Tincher B.,Syme J.,Tanverakul S.,&Catt D..(2021).Adaptive water management in the face of uncertainty: Integrating machine learning, groundwater modeling and robust decision making.Climate Risk Management,34.
MLA	Miro M.E.,et al."Adaptive water management in the face of uncertainty: Integrating machine learning, groundwater modeling and robust decision making".Climate Risk Management 34(2021).