Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
This work addresses uncertainty estimation in soil moisture predictions for weather and climate modeling, but it is incremental as it applies existing methods to a new domain.
The study tackled the problem of predicting soil moisture using deep learning models by evaluating aleatoric and epistemic uncertainties with Monte Carlo dropout and an aleatoric term, showing that the method successfully captures predictive error after hyperparameter tuning and detects dissimilarity.
Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc. Accurately modeling soil moisture has important implications in both weather and climate models. The recently available satellite-based observations give us a unique opportunity to build data-driven models to predict soil moisture instead of using land surface models, but previously there was no uncertainty estimate. We tested Monte Carlo dropout (MCD) with an aleatoric term for our long short-term memory models for this problem, and asked if the uncertainty terms behave as they were argued to. We show that the method successfully captures the predictive error after tuning a hyperparameter on a representative training dataset. We show the MCD uncertainty estimate, as previously argued, does detect dissimilarity.