Articles | Volume 24, issue 3
https://doi.org/10.5194/nhess-24-823-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-823-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Mar 2024
Research article |
| 08 Mar 2024
| 08 Mar 2024
Space–time landslide hazard modeling via Ensemble Neural Networks
Ashok Dahal
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, AE 7500, the Netherlands
Hakan Tanyas
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, AE 7500, the Netherlands
Cees van Westen
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, AE 7500, the Netherlands
Mark van der Meijde
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede, AE 7500, the Netherlands
Paul Martin Mai
Physical Science and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Raphaël Huser
Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Luigi Lombardo
Physical Science and Engineering (PSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Natural hazards like floods, earthquakes, and landslides are often interconnected which may create bigger problems than when they occur alone. We studied expert discussions from an international conference to understand how scientists and policymakers can better prepare for these multi-hazards and use new technologies to protect its communities while contributing to dialogues about future international agreements beyond the Sendai Framework and supporting global sustainability goals.
Hunter N. Jimenez, Erkan Istanbulluoglu, Tolga Gorum, Thomas A. Stanley, Pukar M. Amatya, Hakan Tanyas, Mehmet C. Demirel, Aykut Akgun, and Deniz Bozkurt
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This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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After a major earthquake struck near the Türkiye/Syria border in February 2023, a powerful storm brought intense rainfall to the region, triggering additional landslides. We used satellite data and a physics-based model to map probabilistic landslide hazard using both coseismic and hydrologic drivers. We also explored how the sequence of these disasters affected landslide risk. Finally, we offer a method for seasonal forecasting of landslide hazard in at-risk areas using the historic climate.
Fabio Cammarano, Henrique Berger Roisenberg, Alessio Conclave, Islam Fadel, and Mark van der Meijde
Solid Earth, 16, 135–154, https://doi.org/10.5194/se-16-135-2025, https://doi.org/10.5194/se-16-135-2025, 2025
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Sardinia and Corsica separated and have been drifting in the Mediterranean Sea for 35 Myr due to the retreat of the Ionian plate beneath the Tyrrhenian Sea. Using in-house and public data, we measured and interpreted receiver functions based on prior geophysical and petrological studies. Our findings indicate that the islands' ancient continental structure remains mostly unchanged. Alpine orogenesis about 50 million years ago influenced Corsica's crust, enriching it with water-bearing minerals.
Ionut Cristi Nicu, Letizia Elia, Lena Rubensdotter, Hakan Tanyaş, and Luigi Lombardo
Earth Syst. Sci. Data, 15, 447–464, https://doi.org/10.5194/essd-15-447-2023, https://doi.org/10.5194/essd-15-447-2023, 2023
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Thaw slumps and thermo-erosion gullies are cryospheric hazards that are widely encountered in Nordenskiöld Land, the largest and most compact ice-free area of the Svalbard Archipelago. By statistically analysing the landscape characteristics of locations where these processes occurred, we can estimate where they may occur in the future. We mapped 562 thaw slumps and 908 thermo-erosion gullies and used them to create the first multi-hazard susceptibility map in a high-Arctic environment.
Bastian van den Bout, Chenxiao Tang, Cees van Westen, and Victor Jetten
Nat. Hazards Earth Syst. Sci., 22, 3183–3209, https://doi.org/10.5194/nhess-22-3183-2022, https://doi.org/10.5194/nhess-22-3183-2022, 2022
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Natural hazards such as earthquakes, landslides, and flooding do not always occur as stand-alone events. After the 2008 Wenchuan earthquake, a co-seismic landslide blocked a stream in Hongchun. Two years later, a debris flow breached the material, blocked the Min River, and resulted in flooding of a small town. We developed a multi-process model that captures the full cascade. Despite input and process uncertainties, probability of flooding was high due to topography and trigger intensities.
Robert Emberson, Dalia B. Kirschbaum, Pukar Amatya, Hakan Tanyas, and Odin Marc
Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, https://doi.org/10.5194/nhess-22-1129-2022, 2022
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Understanding where landslides occur in mountainous areas is critical to support hazard analysis as well as understand landscape evolution. In this study, we present a large compilation of inventories of landslides triggered by rainfall, including several that are described here for the first time. We analyze the topographic characteristics of the landslides, finding consistent relationships for landslide source and deposition areas, despite differences in the inventories' locations.
Nan Wang, Luigi Lombardo, Marj Tonini, Weiming Cheng, Liang Guo, and Junnan Xiong
Nat. Hazards Earth Syst. Sci., 21, 2109–2124, https://doi.org/10.5194/nhess-21-2109-2021, https://doi.org/10.5194/nhess-21-2109-2021, 2021
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This study exploits 66 years of flash flood disasters across China.
The conclusions are as follows. The clustering procedure highlights distinct spatial and temporal patterns of flash flood disasters at different scales. There are distinguished seasonal, yearly and even long-term persistent flash flood behaviors of flash flood disasters. Finally, the decreased duration of clusters in the recent period indicates a possible activation induced by short-duration extreme rainfall events.
Bastian van den Bout, Theo van Asch, Wei Hu, Chenxiao X. Tang, Olga Mavrouli, Victor G. Jetten, and Cees J. van Westen
Geosci. Model Dev., 14, 1841–1864, https://doi.org/10.5194/gmd-14-1841-2021, https://doi.org/10.5194/gmd-14-1841-2021, 2021
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Landslides, debris flows and other types of dense gravity-driven flows threaten livelihoods around the globe. Understanding the mechanics of these flows can be crucial for predicting their behaviour and reducing disaster risk. Numerical models assume that the solids and fluids of the flow are unstructured. The newly presented model captures the internal structure during movement. This important step can lead to more accurate predictions of landslide movement.
Lina Hao, Rajaneesh A., Cees van Westen, Sajinkumar K. S., Tapas Ranjan Martha, Pankaj Jaiswal, and Brian G. McAdoo
Earth Syst. Sci. Data, 12, 2899–2918, https://doi.org/10.5194/essd-12-2899-2020, https://doi.org/10.5194/essd-12-2899-2020, 2020
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Kerala in India was subjected to an extreme rainfall event in the monsoon season of 2018 which triggered extensive floods and landslides. In order to study whether the landslides were related to recent land use changes, we generated an accurate and almost complete landslide inventory based on two existing datasets and the detailed interpretation of images from the Google Earth platform. The final dataset contains 4728 landslides with attributes of land use in 2010 and land use in 2018.
Cited articles
Abraham, N. and Khan, N. M.: A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation, CoRR, abs/1810.07842, arXiv [preprint], https://doi.org/10.48550/arXiv.1810.07842, 2018. a, b
Alvioli, M., Marchesini, I., Reichenbach, P., Rossi, M., Ardizzone, F., Fiorucci, F., and Guzzetti, F.: Automatic delineation of geomorphological slope units with <tt>r.slopeunits v1.0</tt> and their optimization for landslide susceptibility modeling, Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, 2016. a
Amit, S. N. K. B. and Aoki, Y.: Disaster detection from aerial imagery with convolutional neural network, in: 2017 international electronics symposium on knowledge creation and intelligent computing (IES-KCIC), Surabaya, Indonesia, 26–27 September, IEEE, 239–245, https://doi.org/10.1109/KCIC.2017.8228593, 2017. a
Ardizzone, F., Cardinali, M., Carrara, A., Guzzetti, F., and Reichenbach, P.: Impact of mapping errors on the reliability of landslide hazard maps, Nat. Hazards Earth Syst. Sci., 2, 3–14, https://doi.org/10.5194/nhess-2-3-2002, 2002. a
Bout, B., Lombardo, L., van Westen, C., and Jetten, V.: Integration of two-phase solid fluid equations in a catchment model for flashfloods, debris flows and shallow slope failures, Environ. Modell. Softw., 105, 1–16, https://doi.org/10.1016/j.envsoft.2018.03.017, 2018. a
Brenning, A.: Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models, Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie, 19, 23–32, 2008. a
Brock, J., Schratz, P., Petschko, H., Muenchow, J., Micu, M., and Brenning, A.: The performance of landslide susceptibility models critically depends on the quality of digital elevation models, Geomat. Nat. Haz. Risk, 11, 1075–1092, 2020. a
Burton, A. and Bathurst, J.: Physically based modelling of shallow landslide sediment yield at a catchment scale, Environ. Geol., 35, 89–99, 1998. a
Catani, F.: Landslide detection by deep learning of non-nadiral and crowdsourced optical images, Landslides, 18, 1025–1044, 2021. a
Catani, F., Casagli, N., Ermini, L., Righini, G., and Menduni, G.: Landslide hazard and risk mapping at catchment scale in the Arno River basin, Landslides, 2, 329–342, 2005. a
Catani, F., Tofani, V., and Lagomarsino, D.: Spatial patterns of landslide dimension: a tool for magnitude mapping, Geomorphology, 273, 361–373, 2016. a
Cisneros, D., Richards, J., Dahal, A., Lombardo, L., and Huser, R.: Deep graphical regression for jointly moderate and extreme Australian wildfires, arXiv [preprint], arXiv:2308.14547, 2023. a
Clinton, B. D.: Light, temperature, and soil moisture responses to elevation, evergreen understory, and small canopy gaps in the southern Appalachians, Forest Ecol. Manag., 186, 243–255, 2003. a
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.-P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F., Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V., Hervás, J., and Smith, J. T.: Recommendations for the quantitative analysis of landslide risk, B. Eng. Geol. Environ., 73, 209–263, 2014. a
Dahal, A., Castro-Cruz, D. A., Tanyaş, H., Fadel, I., Mai, P. M., van der Meijde, M., van Westen, C., Huser, R., and Lombardo, L.: From ground motion simulations to landslide occurrence prediction, Geomorphology, 441, 108898, 2023. a
Dahal, A.: ashokdahal/LandslideHazard: v1.0.0, Zenodo [data set and code], https://doi.org/10.5281/zenodo.10765925, 2024.
Davison, A. and Huser, R.: Statistics of Extremes, Annu. Rev. Stat. Appl., 2, 203–235, https://doi.org/10.1146/annurev-statistics-010814-020133, 2015. a
Dhital, M. R.: An overview of landslide hazard mapping and rating systems in Nepal, Journal of Nepal Geological Society, 22, 533–538, 2000. a
Di Napoli, M., Tanyas, H., Castro-Camilo, D., Calcaterra, D., Cevasco, A., Di Martire, D., Pepe, G., Brandolini, P., and Lombardo, L.: On the estimation of landslide intensity, hazard and density via data-driven models, Nat. Hazards, 119, 1513–1530, 2023. a
Fan, X., Scaringi, G., Korup, O., West, A. J., van Westen, C. J., Tanyas, H., Hovius, N., Hales, T. C., Jibson, R. W., Allstadt, K. E., and Zhang, L.: Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts, Rev. Geophys., 57, 421–503, 2019. a
Fang, Z., Wang, Y., van Westen, C., and Lombardo, L.: Space–Time Landslide Susceptibility Modeling Based on Data-Driven Methods, Math. Geosci., 55, 1–20, 2023. a
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006. a
Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., and Savage, W. Z.: Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning, Eng. Geol., 102, 99–111, 2008. a
Formetta, G., Capparelli, G., and Versace, P.: Evaluating performance of simplified physically based models for shallow landslide susceptibility, Hydrol. Earth Syst. Sci., 20, 4585–4603, https://doi.org/10.5194/hess-20-4585-2016, 2016. a
Frattini, P., Crosta, G., and Carrara, A.: Techniques for evaluating the performance of landslide susceptibility models, Eng. Geol., 111, 62–72, https://doi.org/10.1016/j.enggeo.2009.12.004, 2010. a
Ghorbanzadeh, O., Meena, S. R., Abadi, H. S. S., Piralilou, S. T., Zhiyong, L., and Blaschke, T.: Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model, IEEE J. Sel. Top. Appl., 14, 452–463, 2020. a
Glenn, N. F., Streutker, D. R., Chadwick, D. J., Thackray, G. D., and Dorsch, S. J.: Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity, Geomorphology, 73, 131–148, 2006. a
Glorot, X. and Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings, Sardinia, Italy, 13–15 May 2010, 249–256, https://proceedings.mlr.press/v9/glorot10a.html (last access: 2 March 2024), 2010. a
Gomez, H. and Kavzoglu, T.: Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela, Eng. Geol., 78, 11–27, 2005. a
Grelle, G., Soriano, M., Revellino, P., Guerriero, L., Anderson, M., Diambra, A., Fiorillo, F., Esposito, L., Diodato, N., and Guadagno, F.: Space–time prediction of rainfall-induced shallow landslides through a combined probabilistic/deterministic approach, optimized for initial water table conditions, Bull. Eng. Geol. Environ., 73, 877–890, 2014. a
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide Hazard Evaluation: A Review of Current Techniques and Their Application in a Multi-Scale Study, Central Italy, Geomorphology, 31, 181–216, https://doi.org/10.1016/S0169-555X(99)00078-1, 1999. a, b, c, d
Guzzetti, F., Cardinali, M., Reichenbach, P., and Carrara, A.: Comparing Landslide Maps: A Case Study in the Upper Tiber River Basin, Central Italy, Environ. Manage., 25, 247–263, https://doi.org/10.1007/s002679910020, 00000, 2000. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, CoRR, abs/1512.03385, arXiv [preprint], http://arxiv.org/abs/1512.03385 (last access: 2 March 2024), 2015. a
Horton, J. B.: Parametric insurance as an alternative to liability for compensating climate harms, Carbon & Climate Law Review, 12, 285–296, 2018. a
Hosmer, D. W. and Lemeshow, S.: Applied Logistic Regression, 2nd ed. edn., Wiley, New York, https://doi.org/10.1002/9781118548387, 2000. a, b
Hough, S. E., Martin, S. S., Gahalaut, V., Joshi, A., Landes, M., and Bossu, R.: A comparison of observed and predicted ground motions from the 2015 Mw 7.8 Gorkha, Nepal, earthquake, Nat. Hazards, 84, 1661–1684, 2016. a
Huang, Y., Tang, Z., Chen, D., Su, K., and Chen, C.: Batching soft IoU for training semantic segmentation networks, IEEE Signal Proc. Let., 27, 66–70, 2019. a
Intrieri, E., Gigli, G., Mugnai, F., Fanti, R., and Casagli, N.: Design and implementation of a landslide early warning system, Eng. Geol., 147, 124–136, 2012. a
Ioffe, S. and Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International conference on machine learning, Lille, France, 7–9 July 2015, pmlr, 448–456, https://doi.org/10.48550/arXiv.1502.03167, 2015b. a, b, c, d
Johnson, J. M. and Khoshgoftaar, T. M.: Survey on deep learning with class imbalance, Journal of Big Data, 6, 1–54, 2019. a
Jones, J. N., Boulton, S. J., Bennett, G. L., Stokes, M., and Whitworth, M. R.: Temporal variations in landslide distributions following extreme events: Implications for landslide susceptibility modeling, J. Geophys. Res.-Earth, 126, e2021JF006067, https://doi.org/10.1029/2021JF006067, 2021. a
Ju, N., Huang, J., He, C., Van Asch, T., Huang, R., Fan, X., Xu, Q., Xiao, Y., and Wang, J.: Landslide early warning, case studies from Southwest China, Eng. Geol., 279, 105917, https://doi.org/10.1016/j.enggeo.2020.105917, 2020. a
Kargel, J. S., Leonard, G. J., Shugar, D. H., Haritashya, U. K., Bevington, A., Fielding, E., Fujita, K., Geertsema, M., Miles, E., Steiner, J., and Anderson, E.: Geomorphic and geologic controls of geohazards induced by Nepal's 2015 Gorkha earthquake, Science, 351, aac8353, https://doi.org/10.1126/science.aac8353, 2016. a
Kincey, M. E., Rosser, N. J., Robinson, T. R., Densmore, A. L., Shrestha, R., Pujara, D. S., Oven, K. J., Williams, J. G., and Swirad, Z. M.: Evolution of coseismic and post-seismic landsliding after the 2015 Mw 7.8 Gorkha earthquake, Nepal, J. Geophys. Res.-Earth, 126, e2020JF005803, https://doi.org/10.1029/2020JF005803, 2021. a, b, c, d, e, f, g, h, i, j, k, l
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv [preprint], https://doi.org/10.48550/ARXIV.1412.6980 (last access: 2 March 2024), 2014. a
Kirschbaum, D. and Stanley, T.: Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness, Earths Future, 6, 505–523, 2018. a
Lee, S., Ryu, J.-H., Won, J.-S., and Park, H.-J.: Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Eng. Geol., 71, 289–302, 2004. a
Li, M., Zhang, X., Thrampoulidis, C., Chen, J., and Oymak, S.: AutoBalance: Optimized Loss Functions for Imbalanced Data, CoRR, abs/2201.01212, arXiv [preprint], https://arxiv.org/abs/2201.01212 (last access: 2 March 2024), 2022. a
Loche, M., Scaringi, G., Yunus, A. P., Catani, F., Tanyaş, H., Frodella, W., Fan, X., and Lombardo, L.: Surface temperature controls the pattern of post-earthquake landslide activity, Sci. Rep.-UK, 12, 1–11, 2022. a
Lombardo, L. and Tanyas, H.: Chrono-validation of near-real-time landslide susceptibility models via plug-in statistical simulations, Eng. Geol., 278, 105818, https://doi.org/10.1016/j.enggeo.2020.105818, 2020. a
Lombardo, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R.: Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides, J. Geophys. Res.-Earth, 124, 1958–1980, https://doi.org/10.1029/2019JF005056, 2019. a
Lombardo, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R.: Space–time landslide predictive modelling, Earth-Sci. Rev., 209, 103318, https://doi.org/10.1016/j.earscirev.2020.103318, 2020. a
Lombardo, L., Tanyas, H., Huser, R., Guzzetti, F., and Castro-Camilo, D.: Landslide size matters: A new data-driven, spatial prototype, Eng. Geol., 293, 106288, https://doi.org/10.1016/j.enggeo.2021.106288, 2021. a, b, c
Maufroy, E., Cruz-Atienza, V. M., Cotton, F., and Gaffet, S.: Frequency-scaled curvature as a proxy for topographic site-effect amplification and ground-motion variability, B. Seismol. Soc. Am., 105, 354–367, 2015. a
McAdoo, B. G., Quak, M., Gnyawali, K. R., Adhikari, B. R., Devkota, S., Rajbhandari, P. L., and Sudmeier-Rieux, K.: Roads and landslides in Nepal: how development affects environmental risk, Nat. Hazards Earth Syst. Sci., 18, 3203–3210, https://doi.org/10.5194/nhess-18-3203-2018, 2018. a
Monaco, S., Pasini, A., Apiletti, D., Colomba, L., Garza, P., and Baralis, E.: Improving wildfire severity classification of deep learning U-nets from satellite images, in: 2020 IEEE International Conference on Big Data (Big Data), Atlanta, Georgia, USA and Virtual Conference, 10–13 December 2020, IEEE, 5786–5788, https://doi.org/10.1109/BigData50022.2020.9377867, 2020. a
Montrasio, L., Valentino, R., Corina, A., Rossi, L., and Rudari, R.: A prototype system for space–time assessment of rainfall-induced shallow landslides in Italy, Nat. Hazards, 74, 1263–1290, 2014. a
Nava, L., Bhuyan, K., Meena, S. R., Monserrat, O., and Catani, F.: Rapid Mapping of Landslides on SAR Data by Attention U-Net, Remote Sens.-Basel, 14, 1449, https://doi.org/10.3390/rs14061449, 2022. a
Neaupane, K. M. and Achet, S. H.: Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya, Eng. Geol., 74, 213–226, 2004. a
Neaupane, K. M. and Piantanakulchai, M.: Analytic network process model for landslide hazard zonation, Eng. Geol., 85, 281–294, 2006. a
Nocentini, N., Rosi, A., Segoni, S., and Fanti, R.: Towards landslide space–time forecasting through machine learning: the influence of rainfall parameters and model setting, Front. Earth Sci., 11, 1152130, https://doi.org/10.3389/feart.2023.1152130, 2023. a
Nowicki Jessee, M., Hamburger, M., Allstadt, K., Wald, D., Robeson, S., Tanyas, H., Hearne, M., and Thompson, E.: A Global Empirical Model for Near-Real-Time Assessment of Seismically Induced Landslides, J. Geophys. Res.-Earth, 123, 1835–1859, 2018. a
Ohlmacher, G. C.: Plan curvature and landslide probability in regions dominated by earth flows and earth slides, Eng. Geol., 91, 117–134, https://doi.org/10.1016/j.enggeo.2007.01.005, 2007. a
Ozturk, U.: Geohazards explained 10: Time-dependent landslide susceptibility, Geology Today, 38, 117–120, 2022. a
Ozturk, U., Pittore, M., Behling, R., Roessner, S., Andreani, L., and Korup, O.: How robust are landslide susceptibility estimates?, Landslides, 18, 681–695, 2021. a
Ozturk, U., Bozzolan, E., Holcombe, E. A., Shukla, R., Pianosi, F., and Wagener, T.: How climate change and unplanned urban sprawl bring more landslides, Nature, 608, 262–265, https://doi.org/10.1038/d41586-022-02141-9, 2022. a
Pearson, K.: Note on Regression and Inheritance in the Case of Two Parents, P. R. Soc. London, 58, 240–242, http://www.jstor.org/stable/115794 (last access: 2 March 2024), 1895. a
Prabhakar, S., Srinivasan, A., and Shaw, R.: Climate change and local level disaster risk reduction planning: need, opportunities and challenges, Mitig. Adapt. Strat. Gl., 14, 7–33, 2009. a
Prakash, N., Manconi, A., and Loew, S.: A new strategy to map landslides with a generalized convolutional neural network, Sci. Rep.-UK, 11, 9722, https://doi.org/10.1038/s41598-021-89015-8, 2021. a
Qi, J., Du, J., Siniscalchi, S. M., Ma, X., and Lee, C.-H.: On mean absolute error for deep neural network based vector-to-vector regression, IEEE Signal Proc. Let., 27, 1485–1489, 2020. a
Rana, K., Ozturk, U., and Malik, N.: Landslide geometry reveals its trigger, Geophys. Res. Lett., 48, e2020GL090848, https://doi.org/10.1029/2020GL090848, 2021. a
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A Review of Statistically-Based Landslide Susceptibility Models, Earth-Sci. Rev., 180, 60–91, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018. a
Richards, J., Huser, R., Bevacqua, E., and Zscheischler, J.: Insights into the drivers and spatiotemporal trends of extreme mediterranean wildfires with statistical deep learning, Artificial Intelligence for the Earth Systems, 2, e220095, https://doi.org/10.1175/AIES-D-22-0095.1, 2023. a
Roback, K., Clark, M. K., West, A. J., Zekkos, D., Li, G., Gallen, S. F., Chamlagain, D., and Godt, J. W.: The size, distribution, and mobility of landslides caused by the 2015 Mw7.8 Gorkha earthquake, Nepal, Geomorphology, 301, 121–138, 2018. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, CoRR, abs/1505.04597, arXiv [preprint], http://arxiv.org/abs/1505.04597 (last access: 2 March 2024), 2015. a
Rosser, N., Kincey, M., Oven, K., Densmore, A., Robinson, T., Pujara, D. S., Shrestha, R., Smutny, J., Gurung, K., Lama, S., and Dhital, M. R.: Changing significance of landslide Hazard and risk after the 2015 Mw 7.8 Gorkha, Nepal Earthquake, Progress in Disaster Science, 10, 100159, https://doi.org/10.1016/j.pdisas.2021.100159, 2021. a
Samia, J., Temme, A. J., Bregt, A., Wallinga, J., Fausto Guzzetti, Ardizzone, F., and Rossi, M.: Characterization and Quantification of Path Dependency in Landslide Susceptibility, Geomorphology, 292, 16–24, https://doi.org/10.1016/j.geomorph.2017.04.039, 2017. a
Samia, J., Temme, A., Bregt, A., Wallinga, J., Guzzetti, F., and Ardizzone, F.: Dynamic path-dependent landslide susceptibility modelling, Nat. Hazards Earth Syst. Sci., 20, 271–285, https://doi.org/10.5194/nhess-20-271-2020, 2020. a
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G.: The graph neural network model, IEEE T. Neural Networ., 20, 61–80, 2008. a
Schlögel, R., Marchesini, I., Alvioli, M., Reichenbach, P., Rossi, M., and Malet, J.-P.: Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models, Geomorphology, 301, 10–20, https://doi.org/10.1016/j.geomorph.2017.10.018, 2018. a
Shou, K.-J. and Lin, J.-F.: Evaluation of the extreme rainfall predictions and their impact on landslide susceptibility in a sub-catchment scale, Eng. Geol., 265, 105434, https://doi.org/10.1016/j.enggeo.2019.105434, 2020. a
Sørensen, R., Zinko, U., and Seibert, J.: On the calculation of the topographic wetness index: evaluation of different methods based on field observations, Hydrol. Earth Syst. Sci., 10, 101–112, https://doi.org/10.5194/hess-10-101-2006, 2006. a
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn. Res., 15, 1929–1958, http://jmlr.org/papers/v15/srivastava14a.html (last access: 2 March 2024), 2014a. a
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014b. a
Steger, S., Brenning, A., Bell, R., and Glade, T.: The propagation of inventory-based positional errors into statistical landslide susceptibility models, Nat. Hazards Earth Syst. Sci., 16, 2729–2745, https://doi.org/10.5194/nhess-16-2729-2016, 2016. a, b
Steger, S., Mair, V., Kofler, C., Pittore, M., Zebisch, M., and Schneiderbauer, S.: Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling–Benefits of exploring landslide data collection effects, Sci. Total Environ., 776, 145935, https://doi.org/10.1016/j.scitotenv.2021.145935, 2021. a
Tanyaş, H., Allstadt, K. E., and van Westen, C. J.: An updated method for estimating landslide-event magnitude, Earth Surf. Proc. Land., 43, 1836–1847, https://doi.org/10.1002/esp.4359, 2018. a
Tanyaş, H., Hill, K., Mahoney, L., Fadel, I., and Lombardo, L.: The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake, Eng. Geol., 297, 106504, https://doi.org/10.1016/j.enggeo.2021.106504, 2022. a
Tanyaş, H., Kirschbaum, D., Görüm, T., van Westen, C. J., Tang, C., and Lombardo, L.: A closer look at factors governing landslide recovery time in post-seismic periods, Geomorphology, 391, 107912, https://doi.org/10.1016/j.geomorph.2021.107912, 2021a. a
Tanyaş, H., Kirschbaum, D., and Lombardo, L.: Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides, B. Eng. Geol. Environ., 80, 4323–4345, 2021b. a
Tanyaş, H., Görüm, T., Kirschbaum, D., and Lombardo, L.: Could road constructions be more hazardous than an earthquake in terms of mass movement?, Nat. Hazards, 112, 639–663, https://doi.org/10.1007/s11069-021-05199-2, 2022a. a
Tanyaş, H., Hill, K., Mahoney, L., Fadel, I., and Lombardo, L.: The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake, Eng. Geol., 297, 106504, 2022b. a
Taylor, D. W.: Fundamentals of Soil Mechanics, John Wiley & Sons, Wisconsin, USA, ISBN 978-1258768928, 1948. a
Titti, G., van Westen, C., Borgatti, L., Pasuto, A., and Lombardo, L.: When Enough Is Really Enough? On the Minimum Number of Landslides to Build Reliable Susceptibility Models, Geosciences, 11, 469, https://doi.org/10.3390/geosciences11110469, 2021. a
Titti, G., Sarretta, A., Lombardo, L., Crema, S., Pasuto, A., and Borgatti, L.: Mapping susceptibility with open-source tools: a new plugin for QGIS, Front. Earth Sci., 229, 842425, https://doi.org/10.3389/feart.2022.842425, 2022. a
Upreti, B. N: The Physiographic and Geology of Nepal and Their Bearing on the Landslide Problem, in: Landslide Hazard Mitigation in the Hindu Kush-Himalaya, edited by: Upreti, B. N., Tianchi, L., and Chalise, S. R. Kathmandu, International Centre for Integrated Mountain Development, 31–49, https://doi.org/10.53055/ICIMOD.374, 2001. a, b
van den Bout, B., Lombardo, L., Chiyang, M., van Westen, C., and Jetten, V.: Physically-based catchment-scale prediction of slope failure volume and geometry, Eng. Geol., 284, 105942, https://doi.org/10.1016/j.enggeo.2020.105942, 2021a. a
van den Bout, B., van Asch, T., Hu, W., Tang, C. X., Mavrouli, O., Jetten, V. G., and van Westen, C. J.: Towards a model for structured mass movements: the OpenLISEM hazard model 2.0a, Geosci. Model Dev., 14, 1841–1864, https://doi.org/10.5194/gmd-14-1841-2021, 2021b. a
Van Westen, C., Van Asch, T. W., and Soeters, R.: Landslide hazard and risk zonation—why is it still so difficult?, B. Eng. Geol. Environ., 65, 167–184, 2006. a
Van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview, Eng. Geol., 102, 112–131, 2008. a
Vasiliev, I. R.: Visualization of spatial dependence: an elementary view of spatial autocorrelation, in: Practical handbook of spatial statistics, edited by: Arlinghaus, S., CRC Press, Boca Raton, 17–30, https://doi.org/10.1201/9781003067689, 2020. a
von Specht, S., Ozturk, U., Veh, G., Cotton, F., and Korup, O.: Effects of finite source rupture on landslide triggering: the 2016 Mw 7.1 Kumamoto earthquake, Solid Earth, 10, 463–486, https://doi.org/10.5194/se-10-463-2019, 2019. a
Wang, H., Xu, W., and Xu, R.: Slope stability evaluation using back propagation neural networks, Eng. Geol., 80, 302–315, 2005. a
Wang, N., Cheng, W., Marconcini, M., Bachofer, F., Liu, C., Xiong, J., and Lombardo, L.: Space–time susceptibility modeling of hydro-morphological processes at the Chinese national scale, Eng. Geol., 301, 106586, https://doi.org/10.1016/j.enggeo.2022.106586, 2022. a
Wang, T., Dahal, A., Fang, Z., van Westen, C., Yin, K., and Lombardo, L.: From spatio-temporal landslide susceptibility to landslide risk forecast, Geosci. Front., 15, 101765, https://doi.org/10.1016/j.gsf.2023.101765, 2023. a
Wang, X., Chen, Y., and Zhu, W:. A survey on curriculum learning, IEEE T. Pattern Anal., 44, 4555–4576, 2021. a
Weng, T.-W., Zhang, H., Chen, P.-Y., Yi, J., Su, D., Gao, Y., Hsieh, C.-J., and Daniel, L.: Evaluating the robustness of neural networks: An extreme value theory approach, arXiv [preprint], arXiv:1801.10578, 2018. a
Whiteley, J., Chambers, J., Uhlemann, S., Wilkinson, P. B., and Kendall, J.: Geophysical monitoring of moisture-induced landslides: a review, Rev. Geophys., 57, 106–145, 2019. a
Worden, C. and Wald, D.: ShakeMap manual online: Technical manual, user's guide, and software guide, US Geol. Surv., Reston, Virginia, USA, 1–156, https://doi.org/10.3133/tm12A1, 2016. a, b, c
Yeon, Y.-K., Han, J.-G., and Ryu, K. H.: Landslide susceptibility mapping in Injae, Korea, using a decision tree, Eng. Geol., 116, 274–283, 2010. a
Yesilnacar, E. and Topal, T.: Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey), Eng. Geol., 79, 251–266, 2005. a
Zapata, M. M., Steger, S., Tanyas, H., and Lombardo, L.: Modeling the area of co-seismic landslides via data-driven models: The Kaikōura example, Eng. Geol., 320, 107121, https://doi.org/10.1016/j.enggeo.2023.107121, 2023. a, b
Zevenbergen, L. W. and Thorne, C. R.: Quantitative analysis of land surface topography, Earth Surf. Proc. Land., 12, 47–56, 1987. a
Zhang, Y., Chen, G., Zheng, L., Li, Y., and Wu, J.: Effects of near-fault seismic loadings on run-out of large-scale landslide: a case study, Eng. Geol., 166, 216–236, 2013. a
Short summary
We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
We propose a modeling approach capable of recognizing slopes that may generate landslides, as...
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