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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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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