International business machines corporation (20240112442). FORECASTING LAND-BASED ENVIRONMENTAL VARIABLES USING SIMILARITY ANALYSIS AND TEMPORAL GRAPH CONVOLUATIONAL NEURAL NETWORKS simplified abstract

FORECASTING LAND-BASED ENVIRONMENTAL VARIABLES USING SIMILARITY ANALYSIS AND TEMPORAL GRAPH CONVOLUATIONAL NEURAL NETWORKS

Organization Name

international business machines corporation

Inventor(s)

Fearghal O'donncha of Aran Islands (IE)

Malvern Madondo of Atlanta GA (US)

Muneeza Azmat of East Lansing MI (US)

Michael Jacobs of Beacon NY (US)

Raya Horesh of North Salem NY (US)

FORECASTING LAND-BASED ENVIRONMENTAL VARIABLES USING SIMILARITY ANALYSIS AND TEMPORAL GRAPH CONVOLUATIONAL NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112442 titled 'FORECASTING LAND-BASED ENVIRONMENTAL VARIABLES USING SIMILARITY ANALYSIS AND TEMPORAL GRAPH CONVOLUATIONAL NEURAL NETWORKS

Simplified Explanation

The computer-implemented method described in the abstract involves analyzing a land region that has been divided into multiple sub-regions, extracting environmental descriptors for each sub-region, generating groups of sub-regions based on similarity analysis, encoding these groups into group-based graphs, and training a model using a spatio-temporal neural network.

• Feature extraction process extracts environmental descriptors for each sub-region
• Similarity analysis generates groups of sub-regions
• Creation of group-based graphs for each group
• Training a model using a spatio-temporal neural network

Potential Applications

This technology could be applied in various fields such as urban planning, environmental monitoring, agriculture, and natural disaster management.

Problems Solved

This technology helps in efficiently analyzing and understanding complex land regions by breaking them down into manageable sub-regions and extracting relevant environmental descriptors.

Benefits

The benefits of this technology include improved decision-making, better resource allocation, enhanced environmental monitoring, and increased efficiency in analyzing large land regions.

Potential Commercial Applications

Potential commercial applications of this technology include software tools for urban planners, environmental consultants, agricultural companies, and government agencies involved in land management.

Possible Prior Art

One possible prior art could be the use of neural networks for analyzing spatial data, but the specific application of decomposing land regions into sub-regions and training a model based on group-based graphs may be novel.

How does this technology compare to traditional methods of land region analysis?

This article does not provide a direct comparison between this technology and traditional methods of land region analysis.

What are the limitations of using a spatio-temporal neural network for training the model in this context?

The article does not address any potential limitations of using a spatio-temporal neural network for training the model based on group-based graphs.

Original Abstract Submitted

embodiments are directed to a computer-implemented method of analyzing a land region that has been decomposed into a plurality of regular or irregular sub-regions. the computer-implemented method includes applying, using a processor system, a feature extraction process that extracts a set of sub-region environmental descriptors for each of the plurality of sub-regions. the processor system applies a similarity analysis to the set of sub-region environmental descriptors to generate groups of the plurality of sub-regions. the processor system creates a plurality of group-based graphs by encoding each of the groups into a corresponding group-based graph. a spatio-temporal neural network is used to train a model based at least in part on the plurality of group-based graphs.