A Survey on Data-Driven Models for Soil Moisture Regression and Classification
A new survey paper published on arXiv details data-driven artificial intelligence (AI) models for soil moisture estimation and classification. The paper categorizes existing AI approaches into five groups: statistical time-series, geostatistical, classical machine learning, deep learning, and probabilistic/Bayesian methods. These models utilize various data sources, including historical soil moisture records, meteorological variables, and topographical data, to perform regression or classification tasks. AI
IMPACT Provides a structured overview of AI applications in environmental science, potentially guiding future research and development in soil moisture modeling.