Review
Version 1
Preserved in Portico This version is not peer-reviewed
Spatial Decision Support Systems with Automated Machine Learning: A Review
Version 1
: Received: 28 September 2022 / Approved: 29 September 2022 / Online: 29 September 2022 (10:06:18 CEST)
A peer-reviewed article of this Preprint also exists.
Wen, R.; Li, S. Spatial Decision Support Systems with Automated Machine Learning: A Review. ISPRS Int. J. Geo-Inf. 2023, 12, 12. Wen, R.; Li, S. Spatial Decision Support Systems with Automated Machine Learning: A Review. ISPRS Int. J. Geo-Inf. 2023, 12, 12.
Abstract
Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.
Keywords
Spatial; Decision Support; Machine Learning; Automation; Framework; System; SDSS; AutoML; GIS
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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