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Parallelization of ensemble neural networks for spatial land-use modeling

Authors:

Jean-Claude ThillAuthors Info & Claims

LBSN '12: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks

Pages 48 - 54

https://doi.org/10.1145/2442796.2442808

Published: 06 November 2012 Publication History

Abstract

Artificial neural networks have been widely applied to spatial modeling and knowledge discovery because of their high-level intelligence and flexibility. Their highly parallel and distributed structure makes them inherently suitable for parallel computing. As the technology of parallel and high-performance computing evolves and computing resources become more widely available, new opportunities exist for spatial neural network models to benefit from this advancement in terms of better handling computational and data intensity associated with spatial problems. In this study, we present a hybrid parallel ensemble neural network approach for modeling spatial land-use change. Our approach combines the shared-memory paradigm and the embarrassingly parallel method by leveraging the power of multicore computer clusters. The efficacy of this approach is demonstrated by the parallelization of Fuzzy ARTMAP neural network models, which have been extensively used in land-use modeling applications. We adopt an ensemble structure of neural networks to train multiple models in parallel and make use of the entire dataset simultaneously. We evaluate the proposed parallelization approach by examining performance variation of training datasets with alternative sizes. Experimental results reveal great potential of higher performance achievement when our hybrid parallel computing approach is applied to large spatial modeling problems.

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http://www.epa.gov/mrlc/change.html

Cited By

Clarke K(2019)Mathematical Foundations of Cellular Automata and Complexity TheoryThe Mathematics of Urban Morphology10.1007/978-3-030-12381-9_8(163-170)Online publication date: 24-Mar-2019
https://doi.org/10.1007/978-3-030-12381-9_8
Clarke K(2017)Land Use Change Modeling with SLEUTH: Improving Calibration with a Genetic AlgorithmGeomatic Approaches for Modeling Land Change Scenarios10.1007/978-3-319-60801-3_8(139-161)Online publication date: 28-Oct-2017
https://doi.org/10.1007/978-3-319-60801-3_8
Gong ZThill JLiu W(2015)ART-P-MAP Neural Networks Modeling of Land-Use Change: Accounting for Spatial Heterogeneity and UncertaintyGeographical Analysis10.1111/gean.1207747:4(376-409)Online publication date: 8-May-2015
https://doi.org/10.1111/gean.12077
Show More Cited By

Index Terms

Parallelization of ensemble neural networks for spatial land-use modeling
1. Computing methodologies
  1. Modeling and simulation

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Published In

cover image ACM Conferences

LBSN '12: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks

November 2012

67 pages

ISBN:9781450316989

DOI:10.1145/2442796

Program Chairs:
Gabriel Ghinita
University of Massachusetts at Boston
,
Jennifer Neville
Purdue University
,
Shawn Newsam
University of California at Merced

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSPATIAL: ACM Special Interest Group on Spatial Information

In-Cooperation

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2012

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National Science Foundation

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SIGSPATIAL'12

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SIGSPATIAL

SIGSPATIAL'12: SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems

November 6, 2012

California, Redondo Beach

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Overall Acceptance Rate 8 of 15 submissions, 53%

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Cited By

Clarke K(2019)Mathematical Foundations of Cellular Automata and Complexity TheoryThe Mathematics of Urban Morphology10.1007/978-3-030-12381-9_8(163-170)Online publication date: 24-Mar-2019
https://doi.org/10.1007/978-3-030-12381-9_8
Clarke K(2017)Land Use Change Modeling with SLEUTH: Improving Calibration with a Genetic AlgorithmGeomatic Approaches for Modeling Land Change Scenarios10.1007/978-3-319-60801-3_8(139-161)Online publication date: 28-Oct-2017
https://doi.org/10.1007/978-3-319-60801-3_8
Gong ZThill JLiu W(2015)ART-P-MAP Neural Networks Modeling of Land-Use Change: Accounting for Spatial Heterogeneity and UncertaintyGeographical Analysis10.1111/gean.1207747:4(376-409)Online publication date: 8-May-2015
https://doi.org/10.1111/gean.12077
Ebenezer RManoj PJoseph HVisumathi J(2013)AI image recognizing agent through a scalable neural network2013 International Conference on Human Computer Interactions (ICHCI)10.1109/ICHCI-IEEE.2013.6887773(1-6)Online publication date: Aug-2013
https://doi.org/10.1109/ICHCI-IEEE.2013.6887773

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