The aim of our study was to derive a susceptibility model adaptable to climate changes, through the inclusion of variables summarizing intense rainfall and snowmelt processes. We selected the territory of the Mont-Emilius and Mont-Cervin Mountain Communities (northern Italy) as study area. To define the summary variables, we investigated the relationships between landslide occurrences and meteorological events (reference period 1991-2020). For landslide susceptibility mapping, we set up a Generalized Additive Model. For model training, we extracted from the local inventory 298 dated landslide points and we selected 300 random non-landslide points. We defined a reference model through variable penalization (relief, NDVI, land cover and geology predictors).
mergeVDA = quota [Digital Elevation Model (DEM) dal geoportale VDA]
slope = pendenza derivata dal DEM
aspect = esposizione del versante derivato dal DEM
curvatura =
sumSWEabs = somma dei file Snow Water Equivalent (SWE)
SWEmin = valori minimi di SWE entro bacini di accumulo
GEO = geologia classata
LAND = uso del suolo classato
Shape of x,y train=> (575, 7)
Shape of x,y test=> (144, 7)
RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 500, 'n_jobs': 4, 'oob_score': True, 'random_state': None, 'verbose': True, 'warm_start': False}
Training Set F1-Score=> 1.0
Testing Set F1-Score=> 0.797
Final OOB error:0.202
Best importance:
0.29, 'DEM 2'
0.24, 'sumSWEabs'
0.15, 'SLOPE'
0.11, 'SWEmin'
0.11, 'ASPECT'
0.07, 'land cover'
0.04, 'GEO'
-OOb error graph
-500x500 probability of landslide map-
RandomForestClassifier {'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 5000, 'n_jobs': 4, 'oob_score': True, 'random_state': None, 'verbose': True, 'warm_start': True}
estimators= 2000
Test accuracy: 1.0
Best importance scores:
0.31, 'mergeVDA'
0.24, 'sumSWEabs'
0.19, 'slope'
0.13, 'SWEmin'
0.12, 'aspect'
Last OOB error: 0.195
Raffa, M., Camera, C.A.S., Bajni, G., 2020. Il ruolo della neve nell’innesco di frane superficiali valutato con random forest: il caso del Mont Cervin e del Mont Emilius in Valle D’Aosta (Tesi di Laurea Magistrale in Geologia Applicata al Territorio, all’Ambiente e alle Risorse Idriche). Università degli Studi di Milano.
@phdthesis{raffa_il_2020,
type = {Tesi di Laurea Magistrale in Geologia Applicata al Territorio, all’Ambiente e alle Risorse Idriche},
title = {Il ruolo della neve nell'innesco di frane superficiali valutato con random forest: il caso del Mont Cervin e del Mont Emilius in Valle D'Aosta},
language = {it},
school = {Università degli Studi di Milano},
author = {Raffa, Mattia and Camera, Corrado A. S. and Bajni, Greta},
year = {2020}
}
Camera, C.A.S., Bajni, G., Corno, I., Raffa, M., Stevenazzi, S., Apuani, T., 2021. Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2021.147360
@article{camera_introducing_2021,
title = {Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis},
issn = {0048-9697},
url = {https://www.sciencedirect.com/science/article/pii/S0048969721024311},
doi = {https://doi.org/10.1016/j.scitotenv.2021.147360},
journal = {Science of The Total Environment},
author = {Camera, Corrado A. S. and Bajni, Greta and Corno, Irene and Raffa, Mattia and Stevenazzi, Stefania and Apuani, Tiziana},
year = {2021},
keywords = {Aosta Valley, Climate variables, Flowslides, Generalized Additive Models, Slides in soil, Snow Water Equivalent}
}
https://github.com/PAULGOYES/Landslide_RL_MLP_DNN
Para el uso debido de la información se recomienda usar la siguiente cita: P. Goyes-Peñafiel y A. Hernandez-Rojas (2020). Doble evaluación de la susceptibilidad por movimientos en masa basados en la solución del problema de clasificación con redes neuronales artificiales y Pesos de Evidencia. https://zenodo.org/badge/latestdoi/250913053
www.catastodissesti.partout.it/#
www.mappe.regione.vda.it/pub/geodissesti
www.geologiavda.partout.it/GeoCartaGeo?l=it