Geospatial Forecasting With Teleconnection and Location Embeddings
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Solution Overview
Problem
Accurately predicting local climate conditions is difficult due to myriad contributing factors, including teleconnections between remote geographic locations, which existing methods struggle to effectively capture and utilize.
Innovation Solution
Leveraging location embeddings that encode spatial heterogeneity and spatial dependence, combined with teleconnection features, using machine learning models like LSTM networks to generate joint embeddings for predicting geospatial measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If teleconnection features from remote geographic locations are incorporated into the prediction model, then prediction accuracy is improved, but model complexity increases
Solution Approach 1:
The model segments the complex prediction task into distinct components: location embeddings capture spatial heterogeneity and dependence, teleconnection features capture remote influences, and the sequence encoder processes temporal patterns. This segmentation allows each component to specialize in specific aspects of the prediction problem.
Solution Approach 2:
The patent introduces an intermediary encoding layer that transforms diverse inputs (location embeddings, teleconnection features) into a unified joint embedding representation. This intermediary layer simplifies the integration of multiple feature types while maintaining their individual information content.
2Measurement precision
If location embeddings capturing both spatial heterogeneity and spatial dependence are used, then prediction accuracy is improved, but computational requirements increase
Solution Approach 1:
The model applies partial action by selectively capturing spatial heterogeneity and spatial dependence to the extent necessary for accurate prediction. Rather than exhaustively modeling all spatial relationships, the embedding captures the essential patterns needed for the prediction task.
3Measurement precision
If multiple teleconnection features from disparate geographic locations are integrated, then forecasting accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple teleconnection features from disparate geographic locations into a unified joint embedding that integrates both teleconnection-derived information and location embedding information. This combining approach consolidates diverse data sources into a coherent representation.
Solution Approach 2:
The sequence encoder serves multiple functions: it processes temporal patterns in teleconnection features, integrates them with location embeddings, and generates predictions. This multi-functionality reduces the need for separate specialized components.
Data Source
AI summary
Implementations are described herein for leveraging teleconnections and location embeddings to predict geospatial measures for a geographic location of interest. In various implementations, a plurality of reference geographic locations may be identified that are disparate from a geographic location of interest and influence a geospatial measure in the geographic location of interest. One or more features may be extracted from each of the plurality of reference geographic locations. The extracted features and a location embedding generated for the geographic location of interest may be encoded into a joint embedding. A sequence encoder may be applied to the joint embedding to generate encoded data indicative of the predicted geospatial measure.