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GIS-Based Landslide Spatial Modeling Using Batch-Training Back-propagation Artificial Neural Network: A Study of Model Parameters

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Advances and Applications in Geospatial Technology and Earth Resources (GTER 2017)

Abstract

The ability of delivering accurate appraisal on landslide occurrences is of practical need for establishing land-use plans in regional scales. Backpropagation Artificial Neural Network (BpANN) has been demonstrated to be an effective tool for landslide spatial prediction. In this study, Batch-Training Back-Propagation Artificial Neural Network which is an integration of BpANN, batch-training strategy, and early stopping criteria is proposed for landslide spatial modeling. The employed early stopping criteria include the Generalization Loss (GL) criterion and the Quotient of Generalization Loss and Progress (QGP) criterion. In addition, BpANN training performance has been known to be highly dependent on various tuning parameters. This paper focuses on the parameter setting of BpANN regarding the investigated early stopping criteria. A Geographic Information System (GIS) database, collected from the mountainous regions in Northern Vietnam, is utilized as a case study. Experimental results show that GL criterion may result in an underfitted BpANN; meanwhile, QGP criterion can help to avoid overfitting. Based on experimental outcomes, several recommendations are put forward for future studies on landslide spatial modeling with batch-training BpANN.

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Author information

Authors and Affiliations

  1. Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809 - K7/25 Quang Trung, Danang, Vietnam

    Nhat-Duc Hoang

  2. Geographic Information System Group, Department of Business and IT, School of Business, University College of Southeast Norway, Gullbringvegen 36, 3800, Bø, Norway

    Dieu Tien Bui

Authors
  1. Nhat-Duc Hoang
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  2. Dieu Tien Bui
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Corresponding author

Correspondence to Nhat-Duc Hoang .

Editor information

Editors and Affiliations

  1. GIS group, Department of Business and IT, University College of Southeast Norway , Bo i Telemark, Norway

    Dieu Tien Bui

  2. Department of Underground and Mining Construction, Hanoi University of Mining and Geology , Hanoi, Vietnam

    Anh Ngoc Do

  3. Department of Exploration Geology, Hanoi University of Mining and Geology , Hanoi, Vietnam

    Hoang-Bac Bui

  4. Faculty of Civil Engineering, Duy Tan University , Da Nang, Vietnam

    Nhat-Duc Hoang

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Hoang, ND., Tien Bui, D. (2018). GIS-Based Landslide Spatial Modeling Using Batch-Training Back-propagation Artificial Neural Network: A Study of Model Parameters. In: Tien Bui, D., Ngoc Do, A., Bui, HB., Hoang, ND. (eds) Advances and Applications in Geospatial Technology and Earth Resources. GTER 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-68240-2_15

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