A Survey on Data-Driven Models for Soil Moisture Regression and Classification
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Computer Science > Machine Learning
Title:A Survey on Data-Driven Models for Soil Moisture Regression and Classification
Abstract:Soil Moisture (SM) modelling constitutes a complex spatiotemporal learning problem characterised by nonlinear environmental interactions, heterogeneous data sources, and limited ground observations. Physics-based approaches, such as water balance models, rely on explicit hydrological equations and high-quality inputs, but their computational cost and scalability limitations restrict large-scale deployment. Data-driven artificial intelligence (AI) methods have emerged as flexible alternatives, enabling the extraction of empirical relationships between soil moisture and environmental variables with reduced modelling assumptions. This work presents a structured survey of AI-based models for soil moisture estimation and classification. Existing approaches are organized into five categories: (a) statistical time-series models, (b) geostatistical methods (c) classical machine learning (ML) models, (d) Deep Learning (DL) models and (e) Probabilistic/Bayesian methods. These models leverage historical soil moisture records, meteorological variables, vegetation indices, topography, soil characteristics, and geolocation data to perform regression or classification tasks.
| Comments: | 14 pages, 3 figures, AIAI 2026 Conference |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.18316 [cs.LG] |
| (or arXiv:2606.18316v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18316
arXiv-issued DOI via DataCite
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Submission history
From: Ilektra Tsimpidi [view email][v1] Tue, 16 Jun 2026 11:31:46 UTC (2,086 KB)
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