Multi-modal spatio-temporal meteorological forecasting with deep neural network

Zhang, Xinbang; Jin, Qizhao; Yu, Tingzhao; Xiang, Shiming; Kuang, Qiuming; Prinet, V.; Chen, Pan

doi:10.1016/j.isprsjprs.2022.03.007

Cited by 10 publications

(1 citation statement)

References 58 publications

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“…crop prediction [130][131][132], crop classification [133]), environmental science (e.g. environmental impact assessment [134], waterlogging risk estimation [135], water storage estimation [30], water potential mapping [136], meteorological forecasting [137], ocean behaviour prediction [138]), geology (e.g. oil well placement [139], soil roughness estimation [32], soil moisture estimation [31], landslide risk estimation [140,141]), transportation (e.g.…”

Section: Sdss With Automlmentioning

confidence: 99%

Spatial Decision Support Systems with Automated Machine Learning: A Review

Wen¹,

Li²

2022

Preprint

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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.

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