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Creating an advanced backpropagation neural network toolbox within GIS software

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Abstract

An artificial neural network (ANN) toolbox is created within GIS software for spatial interpolation, which will help GIS users to train and test ANNs, perform spatial analysis, and display results as a single process. The performance is compared to that of the open source Fast Artificial Neural Network library and conventional interpolation methods by creating digital elevation models (DEMs) given that nearly exact solutions exist. Simulation results show that the advanced backpropagations such as iRprop speed up the learning, while they can get stuck in a local minimum depending on initial weight sets. Besides, the division of input–output examples into training and test data affects the accuracy, particularly when the distribution of the examples is skewed and peaked, and the number of data is small. ANNs, however, show the similar performance to inversed distance weighted or kriging and outperform polynomial interpolations as a global interpolation method in high-dimensional data. In addition, the neural network residual kriging (NNRK) model, which combines the ANN toolbox and kriging within GIS software, is performed. The NNRK outperforms conventional methods and well captures global trends and local variations. A key outcome of this work is that the ANN toolbox created within the de facto standard GIS software is applicable to various spatial analysis including hazard risk assessment over a large area, in particular when there are multiple potential causes, the relationship between risk factors and hazard events is not clear, and the number of available data is small given its performance for DEM generation.

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Acknowledgments

This research was supported by a grant (NEMA-BAEKDUSAN-2012-1-3) from the Volcanic Disaster Preparedness Research Center sponsored by National Emergency Management Agency of Korea. We would like to express our appreciation to professor Jacek Mańdziuk for his kindest advices and Steffen Nissen for his reply on our questions about the FANN.

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Authors and Affiliations

  1. Department of Mathematics, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 305-764, Republic of Korea

    Sunju Lee

  2. Extreme-scale Computing Research Team, National Institute for Mathematical Sciences, 70 Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 305-811, Republic of Korea

    John J. Oh

  3. Department of Civil Engineering, University of Suwon, Wow Angil 17, Bongdameub, Hwaseong-si, Gyeonggi-do, 445-743, Republic of Korea

    Hyunuk An

  4. Research Institute of Social Criticality, Pusan National University, 2, Busandaehak-ro 63 beon-gil, Geumjeong-gu, Busan, 609-735, Republic of Korea

    Soonyoung Yu

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  1. Sunju Lee
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Correspondence to Soonyoung Yu.

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Lee, S., An, H., Yu, S. et al. Creating an advanced backpropagation neural network toolbox within GIS software. Environ Earth Sci 72, 3111–3128 (2014). https://doi.org/10.1007/s12665-014-3216-7

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