A conditional Wasserstein GAN generates plausible future SWI drought trajectories for French insurance risk management under climate change.
Hydrology and Earth System Sciences 28, 917–943 (2024)
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A structured survey categorizing AI models for soil moisture estimation into statistical time-series, geostatistical, classical ML, deep learning, and probabilistic/Bayesian approaches.
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A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence
A conditional Wasserstein GAN generates plausible future SWI drought trajectories for French insurance risk management under climate change.
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A Survey on Data-Driven Models for Soil Moisture Regression and Classification
A structured survey categorizing AI models for soil moisture estimation into statistical time-series, geostatistical, classical ML, deep learning, and probabilistic/Bayesian approaches.