Groundwater data interpolation in California’s Central Valley using multimodal data fusion and multivariate sequence-to-sequence transformation models
Description
We describe novel distributed Artificial Intelligence/Multi Agent algorithms to allocate observations in a constellation and compare their performance to centralized and highly distributed algorithms using realistic problem and orbit distributions.
Detailed example
compare their performance to centralized and highly distributed algorithms using realistic problem and orbit distributions.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
We describe novel distributed Artificial Intelligence/Multi Agent algorithms to allocate observations in a constellation and compare their performance to centralized and highly distributed algorithms using realistic problem and orbit distributions.
Controls / human review
ATO: Not reported; PIA: Not published