Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations
Name
aies-AIES-D-23-0011.1 (1).pdf
Description
Accepted version
Size
34.28 MB
Format
Adobe PDF
Checksum (MD5)
b69afe981815becd25288d42e5131de9
Author(s)
Siddiquee, Masudur R
Meray, Aurelien O
Xu, Zexuan
Gonzalez-Raymat, Hansell
Danielson, Thomas
Upadhyay, Himanshu
Lagos, Leonel E
Eddy-Dilek, Carol
Wainwright, Haruko M
Date Issued
October 1, 2024
Journal
Artificial Intelligence for the Earth Systems
Publisher
American Meteorological Society
Citation
Siddiquee, M. R., and Coauthors, 2024: Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations. Artif. Intell. Earth Syst., 3, e230011,
Version
Final published version
Abstract
Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1175/AIES-D-23-0011.1
