TY - JOUR
T1 - On the prediction of landslide occurrences and sizes via hierarchical neural networks
AU - Aguilera, Quinton
AU - Lombardo, L.
AU - Tanyas, H.
AU - Lipani, Aldo
N1 - Funding Information:
We would like to thank the five anonymous reviewers for their insightful comments. This article was partially supported by King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia, Grant URF/1/4338-01-01.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/8
Y1 - 2022/8
N2 - For more than three decades, the part of the geoscientific community studying landslides through data-driven models has focused on estimating where landslides may occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Networks. But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides on the 25th of April 2015. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in a githubsec repository in the hope that we would stimulate others to replicate similar analyses.
AB - For more than three decades, the part of the geoscientific community studying landslides through data-driven models has focused on estimating where landslides may occur across a given landscape. This concept is widely known as landslide susceptibility. And, it has seen a vast improvement from old bivariate statistical techniques to modern deep learning routines. Despite all these advancements, no spatially-explicit data-driven model is currently capable of also predicting how large landslides may be once they trigger in a specific study area. In this work, we exploit a model architecture that has already found a number of applications in landslide susceptibility. Specifically, we opt for the use of Neural Networks. But, instead of focusing exclusively on where landslides may occur, we extend this paradigm to also spatially predict classes of landslide sizes. As a result, we keep the traditional binary classification paradigm but we make use of it to complement the susceptibility estimates with a crucial information for landslide hazard assessment. We will refer to this model as Hierarchical Neural Network (HNN) throughout the manuscript. To test this analytical protocol, we use the Nepalese area where the Gorkha earthquake induced tens of thousands of landslides on the 25th of April 2015. The results we obtain are quite promising. The component of our HNN that estimates the susceptibility outperforms a binomial Generalized Linear Model (GLM) baseline we used as benchmark. We did this for a GLM represents the most common classifier in the landslide literature. Most importantly, our HNN also suitably performed across the entire procedure. As a result, the landslide-area-class prediction returned not just a single susceptibility map, as per tradition. But, it also produced several informative maps on the expected landslide size classes. Our vision is for administrations to consult these suite of model outputs and maps to better assess the risk to local communities and infrastructure. And, to promote the diffusion of our HNN, we are sharing the data and codes in a githubsec repository in the hope that we would stimulate others to replicate similar analyses.
KW - Hierarchical models
KW - Landslide Hazard
KW - Landslide susceptibility
KW - Neural networks
KW - Slope units
KW - UT-Hybrid-D
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-HYBRID
UR - http://www.scopus.com/inward/record.url?scp=85127606557&partnerID=8YFLogxK
U2 - 10.1007/s00477-022-02215-0
DO - 10.1007/s00477-022-02215-0
M3 - Article
AN - SCOPUS:85127606557
SN - 1436-3240
VL - 36
SP - 2031
EP - 2048
JO - Stochastic environmental research and risk assessment
JF - Stochastic environmental research and risk assessment
IS - 8
ER -