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Open AccessArticle

An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping

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Department of Computer Engineering, Gazi University, Ankara 06570, Turkey
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Department of Geological Engineering, Hacettepe University, Ankara 06800, Turkey
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Department of Computer Engineering, Hacettepe University, Ankara 06800, Turkey
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 578; https://doi.org/10.3390/ijgi8120578
Received: 27 October 2019 / Revised: 27 November 2019 / Accepted: 9 December 2019 / Published: 11 December 2019
Natural hazards have a great number of influencing factors. Machine-learning approaches have been employed to understand the individual and joint relations of these factors. However, it is a challenging process for a machine learning algorithm to learn the relations of a large parameter space. In this circumstance, the success of the model is highly dependent on the applied parameter reduction procedure. As a state-of-the-art neural network model, representative learning assumes full responsibility of learning from feature extraction to prediction. In this study, a representative learning technique, recurrent neural network (RNN), was applied to a natural hazard problem. To that end, it aimed to assess the landslide problem by two objectives: Landslide susceptibility and inventory. Regarding the first objective, an empirical study was performed to explore the most convenient parameter set. In landslide inventory studies, the capability of the implemented RNN on predicting the subsequent landslides based on the events before a certain time was investigated respecting the resulting parameter set of the first objective. To evaluate the behavior of implemented neural models, receiver operating characteristic analysis was performed. Precision, recall, f-measure, and accuracy values were additionally measured by changing the classification threshold. Here, it was proposed that recall metric be utilized for an evaluation of landslide mapping. Results showed that the implemented RNN achieves a high estimation capability for landslide susceptibility. By increasing the network complexity, the model started to predict the exact label of the corresponding landslide initiation point instead of estimating the susceptibility level. View Full-Text
Keywords: natural hazard assessment; landslide mapping; deep learning; recurrent neural networks natural hazard assessment; landslide mapping; deep learning; recurrent neural networks
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Mutlu, B.; Nefeslioglu, H.A.; Sezer, E.A.; Akcayol, M.A.; Gokceoglu, C. An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping. ISPRS Int. J. Geo-Inf. 2019, 8, 578.

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