干旱区生态环境知识资源中心(Arid): A contribution to drought resilience in East Africa through groundwater pump monitoring informed by in-situ instrumentation, remote sensing and ensemble machine learning

Arid

DOI	10.1016/j.scitotenv.2021.146486
	A contribution to drought resilience in East Africa through groundwater pump monitoring informed by in-situ instrumentation, remote sensing and ensemble machine learning
	Thomas, Evan; Wilson, Daniel; Kathuni, Styvers; Libey, Anna; Chintalapati, Pranav; Coyle, Jeremy
通讯作者	Thomas, E (corresponding author), Univ Colorado, Mortenson Ctr Global Engn, Boulder, CO 80309 USA. ; Thomas, E (corresponding author), SweetSense Inc, Portland, OR 97201 USA.
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2021
卷号	780
英文摘要	The prevalence of drought in the Horn of Africa has continued to threaten access to safe and affordable water for millions of people. In order to improve monitoring of water pump functionality, telemetry-connected sensors have been installed on 480 electrical groundwater pumps in arid regions of Kenya and Ethiopia, designed to im -prove monitoring and support operation and maintenance of these water supplies. In this paper, we describe the development and validation of two classification systems designed to identify the functionality and non-functionality of these electrical pumps, one an expert-informed conditional classifier and the other leveraging machine learning. Given a known relationship between surface water availability and groundwater pump use, the classifiers combine in-situ sensor data with remote sensing indicators for rainfall and surface water. Our val-idation indicates a overall pump status sensitivity (true positive rate) of 82% for the expert classifier and 84% for the machine learner. When the pump is being used, both classifiers have a 100% true positive rate performance. When a pump is not being used, the specificity (true negative rate) is about 50% for the expert classifier and over 65% for the machine learner. If these detection capabilities were integrated into a repair service, the typical up -time of pumps during drought periods in this region could potentially, if budget resources and institutional incen-tives for pump repairs were provided, result ina drought-period uptime improvement from 60% to nearly of 85% -a 40% reduction in the relative risk of pump downtime. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
英文关键词	Water pump Africa Drought Machine learning Remote sensing
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000653087900014
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351667
作者单位	[Thomas, Evan; Libey, Anna; Chintalapati, Pranav; Coyle, Jeremy] Univ Colorado, Mortenson Ctr Global Engn, Boulder, CO 80309 USA; [Thomas, Evan; Wilson, Daniel; Kathuni, Styvers; Coyle, Jeremy] SweetSense Inc, Portland, OR 97201 USA
推荐引用方式 GB/T 7714	Thomas, Evan,Wilson, Daniel,Kathuni, Styvers,et al. A contribution to drought resilience in East Africa through groundwater pump monitoring informed by in-situ instrumentation, remote sensing and ensemble machine learning[J],2021,780.
APA	Thomas, Evan,Wilson, Daniel,Kathuni, Styvers,Libey, Anna,Chintalapati, Pranav,&Coyle, Jeremy.(2021).A contribution to drought resilience in East Africa through groundwater pump monitoring informed by in-situ instrumentation, remote sensing and ensemble machine learning.SCIENCE OF THE TOTAL ENVIRONMENT,780.
MLA	Thomas, Evan,et al."A contribution to drought resilience in East Africa through groundwater pump monitoring informed by in-situ instrumentation, remote sensing and ensemble machine learning".SCIENCE OF THE TOTAL ENVIRONMENT 780(2021).