Abstract
The Alpine region of Aosta Valley has an early warning system to issue hydrogeological alerts up to 36 h in advance based on the output of hydrological models and rainfall thresholds. However, those thresholds generally do not apply to the debris flows triggered by local summer thunderstorms, which typically are intense rainfalls of short duration, with cumulative precipitation lower than 20 mm. Therefore, it is necessary to formulate a specific predictive debris-flow model, which takes into account other possible triggering factors. In this study, we have developed a predictive model for debris flows with machine learning techniques, using a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the 91 drainage basins in which at least one debris-flow event was generated during the time period considered in this study (2009–2019). The performance of the model, using different machine learning techniques, was evaluated, and the most suitable model was chosen to be experimentally implemented in the existing early warning system of the region. The output of the model provides a debris-flow probability (DFP) for individual basins computed from the geomorphological and hydro-meteorological input variables.
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References
Abancó C, Hürlimann M (2014) Estimate of the debris-flow entrainment using field and topographical data. Nat Hazards 81:363–383
Agrawal K, Baweja Y, Dwivedi D, Saha R, Prasad P, Agrawal S, Kapoor S, Chaturvedi P, Mali N, Kala VU, Dutt V (2017) A comparison of class imbalance techniques for real-world landslide predictions. In: International conference on machine learning and data science (MLDS) 2018:1–8.https://doi.org/10.1109/MLDS.2017.21
Angillieri MYE (2015) Application of logistic regression and frequency ratio in the spatial distribution of debris-rockslides: precordillera of San Juan, Argentina. Quart Int 355:202–208. https://doi.org/10.1016/j.quaint.2014.11.002
Angillieri MYE (2020) Debris flow susceptibility mapping using frequency ratio and seed cells, in a portion of a mountain international route, Dry Central Andes of Argentina. CATENA 189:104504. https://doi.org/10.1016/j.catena.2020.104504
Beason SR, Legg NT, Kenyon TR, Jost RP (2021) Forecasting and seismic detection of proglacial debris flows at Mount Rainier National Park, Washington, USA. Environ Eng Geosci 27(1):57–72
Bertrand M, Liébault F, Piégay H (2013) Debris-flow susceptibility of upland catchments. Nat Hazards 67:497–511. https://doi.org/10.1007/s11069-013-0575-4
Bertrand M, Liébault F, Piégay H (2017) Regional scale mapping of debris-flow susceptibility in the Southern French Alps. J Alpine Res 105(4):17
Bornaetxea T, Rossi M, Marchesini I, Alvioli M (2018) Effective surveyed area and its role in statistical landslide susceptibility assessments. Nat Hazard 18:2455–2469. https://doi.org/10.5194/nhess-18-2455-2018
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
D’Amico ME, Pintaldi E, Sapino E, Colombo N, Quaglino E, Stanchi S, Navillod E, Rocco R, Freppaz M (2002) Soil types of Aosta Valley. J Maps 16(2):755–765
Dal Piaz GV, Bistacchi A, Massironi M (2003) Geological outline of the Alos. J Int Geosci 26(3):175–180
Di B, Zhang H, Liu Y, Li J, Chen N, Stamatopoulos CA, Luo Y, Zhan Y (2020) Assessing susceptibility of debris flow in southwest China using gradient boosting machine. Sci Rep 9:12532. https://doi.org/10.1038/s41598-019-48986-5
Elkadiri R, Sultan M, Youssef AM, Elbayoumi T, Chase R, Bulkhi AB, Al-Katheeri MM (2014) A remote sensing-based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling. J Sel Top Appl Earth Observ Remote Sens 7:4818–4835. https://doi.org/10.1109/JSTARS.2014.2337273
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874
Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272–299
Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6(5):429–449
Jiang W, Rao P, Cao R, Tang Z, Chen K (2017) Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation. J Geog Sci 27:439–462. https://doi.org/10.1007/s11442-017-1386-4
Jin T, Hu X, Liu B, Xi C, He K, Cao X, Luo G, Han M, Ma G, Yang Y, Wang Y (2022) Susceptibility prediction of post-fire debris flows in Xichang, China, using a logistic regression model from a spatiotemporal perspective. Remote Sens 14(6):1306. https://doi.org/10.3390/rs14061306
Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(5):1–26
Laiolo P, Gabellani S, Campo L, Silvestro F, Delogu F, Rudari R, Pulvirenti L, Boni G, Fascetti F, Pierdicca N, Crapolicchio R, Hasenauer S, Puca S (2016) Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model. Int J Appl Earth Observ Geoinf 48:131–145
Leonarduzzi E, Molnar P, McArdell BW (2017) Predictive performance of rainfall thresholds for shallow landslides in Switzerland from gridded daily data. Water Resour Res 53:6612–6625. https://doi.org/10.1002/2017WR021044
Liang WJ, Zhuang DF, Jiang D, Pan JJ, Ren HY (2012) Assessment of debris-flow hazards using a Bayesian network. Geomorphology 171–172:94–100. https://doi.org/10.1016/j.geomorph.2012.05.008
Liang Z, Wang CM, Zhang ZM, Khan KUJ (2020) A comparison of statistical and machine learning methods for debris flow susceptibility mapping. Stoch Environ Res Risk Assess 34:1887–1907. https://doi.org/10.1007/s00477-020-01851-8
Lunardon N, Menardi G, Torelli N (2014) ROSE: a package for binary imbalanced learning. Comput Sci 6:79–89
Mirus BB, Morphew MD, Smith JB (2018) Developing hydro-meteorological thresholds for shallow landslide initiation and early warning. Water 10(9):1274
Paranunzio R, Chiarle M, Laio F, Nigrelli G, Turconi L, Luino F (2018) New insights in the relation between climate and slope failures at high-elevation sites. Theoret Appl Climatol 137:1765–1784
Pignone F, Rebora N, Silvestro F, Castelli F (2010) GRISO: Generatore Random di Interpolazioni Spaziali da Osservazioni incerte. Piogge, Technical Report
Ponziani M, Pogliotti P, Stevenin H, Ratto SM (2020) Debris-flow indicator for an early warning system in the Aosta valley region. Nat Hazards 104(2):1819–1839
Prenner D, Kaitna R, Mostbauer K, Hrachowitz M (2018) The value of using multiple hydrometeorological variables to predict temporal debris flow susceptibility in an Alpine environment. Water Resour Res 54(9):6822–6843
Puca S, Porcu F, Rinollo A, Vulpiani G, Baguis P, Balabanova S, Campione E, Erturk A, Gabellani S, Iwanski R, Jurasek M, Kanak J, Kerenyi J, Koshinchanov G, Kozinarova G, Krahe P, Lapeta B, Labo E, Milani L, Okon L, Oztopal A, Pagliara P, Pignone F, Rachimow C, Rebora N, Roulin E, Sonmez I, Toniazzo A, Biron D, Casella D, Cattani E, Dietrich S, Di Paola F, Laviola S, Levizzani V, Melfi D, Mugnai A, Panegrossi G, Petracca M, Sanò P, Zauli F, Rosci P, de Leonibus L, Agosta E, Gattari F (2014) The Validation service of the hydrological SAF geostationary and polar satellite precipitation products. Nat Hazards Earth Syst Sci 14:871–889
RAVdA (2019) Catasto Dissesti Regionale—SCT. http://catastodissesti.partout.it/
RAVdA (2022) Carta Geologica della Valle d’Aosta—SCT. https://mappe.regione.vda.it/pub/geoCartoSCT/
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91
Samia J, Temme A, Bregt A, Wallinga J, Guzzetti F, Ardizzone F (2019) Dynamic path-dependent landslide susceptibility modelling. Nat Hazards Earth Syst Sci 20(1):271–285
Shirzadi A, Shahabi H, Chapi K, Bui DT, Pham BT, Shahedi K, Ahmad BB (2017) A comparative study between popular statistical and machine learning methods for simulating volume of landslides. CATENA 157:213–226. https://doi.org/10.1016/j.catena.2017.05.016
Silvestro F, Gabellani S, Delogu F, Rudari R, Boni G (2013) Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the continuum model. Hydrol Earth Syst Sci 17:39–62
Silvestro F, Gabellani S, Rudari R, Delogu F, Laiolo P, Boni G (2015) Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data. Hydrol Earth Syst Sci 19:1727–1751
Su C, Wang L, Wang X, Huang Z, Zhang X (2015) Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine. Nat Hazards 76:1759–1779. https://doi.org/10.1007/s11069-014-1562
Su TJ, Pan TS, Chang YL, Lin SS, Hao MJ (2022) A hybrid fuzzy and K-nearest neighbor approach for debris flow disaster prevention. Access 10:21787–21797. https://doi.org/10.1109/ACCESS.2022.3152906
Tehrany MS, Jones S (2017) Evaluating the variations in the flood susceptibility maps accuracies due to the alterations in the type and extent of the flood inventory. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-4/W5:209–214. https://doi.org/10.5194/isprs-archives-XLII-4-W5-209-2017
CF VdA (2022) http://cf.regione.vda.it/home.php
Wang LI, Guo M, Sawada K, Lin J, Zhang J (2016) A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci J 20:117–136. https://doi.org/10.1007/s12303-015-0026-1
Wang S, Meng X, Chen G, Guo P, Xiong M, Zeng R (2017) Effects of vegetation on debris flow mitigation: a case study from Gansu province, China. Geomorphology 282:64–73. https://doi.org/10.1016/j.geomorph.2016.12.024
Wang H, Zhang L, Yin K, Luo H, Li J (2021) Landslide identification using machine learning. Geosci Front 12:351–364. https://doi.org/10.1016/j.gsf.2020.02.012
Wicki A, Lehmann P, Hauck C, Seneviratne SI, Waldner P, Stähli M (2020) Assessing the potential of soil moisture measurements for regional landslide early warning. Landslides 17:1881–1896
Xiong K, Adhikari BR, Stamatopoulos CA, Zhan Y, Wu S, Dong Z, Di B (2020) Comparison of different machine learning methods for debris-flow susceptibility mapping: a case study in the Sichuan province, China. Remote Sens 12(2):295. https://doi.org/10.3390/rs12020295
Xu W, Jing S, Yu W, Wang Z, Zhang G, Huang J (2013) A comparison between Bayes discriminant analysis and logistic regression for prediction of debris flow in southwest Sichuan, China. Geomorphology 201:45–51. https://doi.org/10.1016/j.geomorph.2013.06.003
Yu X, Wang Y, Niu R, Hu Y (2016) A combination of geographically weighted regression, particle swarm optimization and support vector machine for landslide susceptibility mapping: a case study at Wanzhou in the Three Gorges area, China. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph13050487
Zhang Y, Ge T, Tian W, Liou Y (2019) Debris flow susceptibility mapping using machine-learning techniques in Shigatse area. China Remote Sens 11(23):2801. https://doi.org/10.3390/rs11232801
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by MP, DP, and AG. The first draft of the manuscript was written by MP. All authors read and approved the final manuscript.
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Ponziani, M., Ponziani, D., Giorgi, A. et al. The use of machine learning techniques for a predictive model of debris flows triggered by short intense rainfall. Nat Hazards 117, 143–162 (2023). https://doi.org/10.1007/s11069-023-05853-x
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DOI: https://doi.org/10.1007/s11069-023-05853-x