Articles | Volume 27, issue 22
https://doi.org/10.5194/hess-27-4151-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-4151-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Nov 2023
Research article |
| 16 Nov 2023
| 16 Nov 2023
Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data
Daniel Camilo Roman Quintero
CORRESPONDING AUTHOR
Dipartimento di ingegneria, Università degli Studi della Campania “Luigi Vanvitelli”, via Roma 9, 81031 Aversa (CE), Italy
Pasquale Marino
Dipartimento di ingegneria, Università degli Studi della Campania “Luigi Vanvitelli”, via Roma 9, 81031 Aversa (CE), Italy
Giovanni Francesco Santonastaso
Dipartimento di ingegneria, Università degli Studi della Campania “Luigi Vanvitelli”, via Roma 9, 81031 Aversa (CE), Italy
Roberto Greco
Dipartimento di ingegneria, Università degli Studi della Campania “Luigi Vanvitelli”, via Roma 9, 81031 Aversa (CE), Italy
Related authors
Daniel Camilo Roman Quintero, Pasquale Marino, Abdullah Abdullah, Giovanni Francesco Santonastaso, and Roberto Greco
Nat. Hazards Earth Syst. Sci., 25, 2679–2698, https://doi.org/10.5194/nhess-25-2679-2025, https://doi.org/10.5194/nhess-25-2679-2025, 2025
Short summary
Short summary
Local thresholds for landslide forecasting, combining hydrologic predisposing factors and rainfall features, are developed from a physically based model of a slope. To extend their application to a wide area, uncertainty due to the spatial variability of geomorphological and hydrologic variables is introduced. The obtained hydrometeorological thresholds, integrating root-zone soil moisture and aquifer water level with rainfall depth, outperform thresholds based on rain intensity and duration.
Daniel Camilo Roman Quintero, Pasquale Marino, Abdullah Abdullah, Giovanni Francesco Santonastaso, and Roberto Greco
Nat. Hazards Earth Syst. Sci., 25, 2679–2698, https://doi.org/10.5194/nhess-25-2679-2025, https://doi.org/10.5194/nhess-25-2679-2025, 2025
Short summary
Short summary
Local thresholds for landslide forecasting, combining hydrologic predisposing factors and rainfall features, are developed from a physically based model of a slope. To extend their application to a wide area, uncertainty due to the spatial variability of geomorphological and hydrologic variables is introduced. The obtained hydrometeorological thresholds, integrating root-zone soil moisture and aquifer water level with rainfall depth, outperform thresholds based on rain intensity and duration.
Francesco Marra, Eleonora Dallan, Marco Borga, Roberto Greco, and Thom Bogaard
EGUsphere, https://doi.org/10.5194/egusphere-2025-3378, https://doi.org/10.5194/egusphere-2025-3378, 2025
Short summary
Short summary
We highlight an important conceptual difference between the duration used in intensity-duration thresholds and the duration used in the intensity-duration-frequency curves that has been overlooked by the landslide literature so far.
Benjamin B. Mirus, Thom Bogaard, Roberto Greco, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 25, 169–182, https://doi.org/10.5194/nhess-25-169-2025, https://doi.org/10.5194/nhess-25-169-2025, 2025
Short summary
Short summary
Early warning of increased landslide potential provides situational awareness to reduce landslide-related losses from major storm events. For decades, landslide forecasts relied on rainfall data alone, but recent research points to the value of hydrologic information for improving predictions. In this paper, we provide our perspectives on the value and limitations of integrating subsurface hillslope hydrologic monitoring data and mathematical modeling for more accurate landslide forecasts.
Yi Luo, Jiaming Zhang, Zhi Zhou, Juan P. Aguilar-Lopez, Roberto Greco, and Thom Bogaard
Hydrol. Earth Syst. Sci., 27, 783–808, https://doi.org/10.5194/hess-27-783-2023, https://doi.org/10.5194/hess-27-783-2023, 2023
Short summary
Short summary
This paper describes an experiment and modeling of the hydrological response of desiccation cracks under long-term wetting–drying cycles. We developed a new dynamic dual-permeability model to quantify the dynamic evolution of desiccation cracks and associated preferential flow and moisture distribution. Compared to other models, the dynamic dual-permeability model could describe the experimental data much better, but it also provided an improved description of the underlying physics.
Luca Comegna, Emilia Damiano, Roberto Greco, Lucio Olivares, and Luciano Picarelli
Earth Syst. Sci. Data, 13, 2541–2553, https://doi.org/10.5194/essd-13-2541-2021, https://doi.org/10.5194/essd-13-2541-2021, 2021
Short summary
Short summary
The set-up of an automatic field station allowed for the monitoring of the annual cyclic hydrological response of a deposit in pyroclastic air-fall soils covering a steep mountainous area in Campania region (Italy), which in 1999 was involved in a rainfall-induced flowslide. Data highlight the influence of the initial conditions, governed by the antecedent wetting/drying history, on the weather-induced hydraulic paths, allowing us to estimate their influence on the local stability conditions.
Cited articles
Allocca, V., Manna, F., and De Vita, P.: Estimating annual groundwater recharge coefficient for karst aquifers of the southern Apennines (Italy), Hydrol. Earth Syst. Sci., 18, 803–817, https://doi.org/10.5194/hess-18-803-2014, 2014.
Arthur, D. and Vassilvitskii, S.: k-means
Bogaard, T. A. and Greco, R.: Landslide hydrology: from hydrology to pore pressure, WIRES Water, 3, 439–459, https://doi.org/10.1002/wat2.1126, 2016.
Bogaard, T. and Greco, R.: Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds, Nat. Hazards Earth Syst. Sci., 18, 31–39, https://doi.org/10.5194/nhess-18-31-2018, 2018.
Bordoni, M., Meisina, C., Valentino, R., Lu, N., Bittelli, M., and Chersich, S.: Hydrological factors affecting rainfall-induced shallow landslides: From the field monitoring to a simplified slope stability analysis, Eng. Geol., 19–37, https://doi.org/10.1016/j.enggeo.2015.04.006, 2015.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Capretti, P. and Battisti, A.: Water stress and insect defoliation promote the colonization of Quercus cerris by the fungus Biscogniauxia mediterranea, Forest Pathol., 37, 129–135, https://doi.org/10.1111/J.1439-0329.2007.00489.X, 2007.
Cascini, L., Cuomo, S., and Guida, D.: Typical source areas of May 1998 flow-like mass movements in the Campania region, Southern Italy, Eng. Geol., 96, 107–125, https://doi.org/10.1016/j.enggeo.2007.10.003, 2008.
Cascini, L., Sorbino, G., Cuomo, S., and Ferlisi, S.: Seasonal effects of rainfall on the shallow pyroclastic deposits of the Campania region (southern Italy), Landslides, 11, 779–792, https://doi.org/10.1007/s10346-013-0395-3, 2014.
Celico, F., Naclerio, G., Bucci, A., Nerone, V., Capuano, P., Carcione, M., Allocca, V., and Celico, P.: Influence of pyroclastic soil on epikarst formation: A test study in southern Italy, Terra Nova, 22, 110–115, https://doi.org/10.1111/J.1365-3121.2009.00923.X, 2010.
Chitu, Z., Bogaard, T. A., Busuioc, A., Burcea, S., Sandric, I., and Adler, M.-J.: Identifying hydrological pre-conditions and rainfall triggers of slope failures at catchment scale for 2014 storm events in the Ialomita Subcarpathians, Romania, Landslides, 14, 419–434, https://doi.org/10.1007/s10346-016-0740-4, 2017.
Comegna, L., Damiano, E., Greco, R., Guida, A., Olivares, L., and Picarelli, L.: Field hydrological monitoring of a sloping shallow pyroclastic deposit, Can. Geotech. J., 53, 1125–1137, https://doi.org/10.1139/cgj-2015-0344, 2016.
Cowpertwait, P. S. P., O'Connell, P. E., Metcalfe, A. V., and Mawdsley, J. A.: Stochastic point process modelling of rainfall. I. Single-site fitting and validation, J. Hydrol., 17–46, https://doi.org/10.1016/S0022-1694(96)80004-7, 1996.
Dal Soglio, L., Danquigny, C., Mazzilli, N., Emblanch, C., and Massonnat, G.: Taking into Account both Explicit Conduits and the Unsaturated Zone in Karst Reservoir Hybrid Models: Impact on the Outlet Hydrograph, Water, 12, 3221, https://doi.org/10.3390/w12113221, 2020.
Damiano, E. and Olivares, L.: The role of infiltration processes in steep slope stability of pyroclastic granular soils: laboratory and numerical investigation, Nat. Hazards, 52, 329–350, https://doi.org/10.1007/s11069-009-9374-3, 2010.
Damiano, E., Olivares, L., and Picarelli, L.: Steep-slope monitoring in unsaturated pyroclastic soils, Eng. Geol., 137–138, 1–12, https://doi.org/10.1016/j.enggeo.2012.03.002, 2012.
Damiano, E., Greco, R., Guida, A., Olivares, L., and Picarelli, L.: Investigation on rainwater infiltration into layered shallow covers in pyroclastic soils and its effect on slope stability, Eng. Geol., 220, 208–218, https://doi.org/10.1016/j.enggeo.2017.02.006, 2017.
de Amorim, R. C. and Hennig, C.: Recovering the number of clusters in data sets with noise features using feature rescaling factors, Inf. Sci., 324, 126–145, https://doi.org/10.1016/J.INS.2015.06.039, 2015.
De Vita, P., Agrello, D., and Ambrosino, F.: Landslide susceptibility assessment in ash-fall pyroclastic deposits surrounding Mount Somma-Vesuvius: Application of geophysical surveys for soil thickness mapping, J. Appl. Geophys., 59, 126–139, https://doi.org/10.1016/j.jappgeo.2005.09.001, 2006.
Di Crescenzo, G. and Santo, A.: Debris slides–rapid earth flows in the carbonate massifs of the Campania region (Southern Italy): morphological and morphometric data for evaluating triggering susceptibility, Geomorphology, 66, 255–276, https://doi.org/10.1016/j.geomorph.2004.09.015, 2005.
Efron, B. and Tibshirani, R. J.: An Introduction to the Bootstrap, Chapman and Hall, New York, https://doi.org/10.1007/978-1-4899-4541-9, 1993.
Feddes, R. A., Kowalik, P., Kolinska-Malinka, K., and Zaradny, H.: Simulation of field water uptake by plants using a soil water dependent root extraction function, J. Hydrol., 31, 13–26, https://doi.org/10.1016/0022-1694(76)90017-2, 1976.
Fiorillo, F., Guadagno, F., Aquino, S., and De Blasio, A.: The December 1999 Cervinara landslides: Further debris flows in the pyroclastic deposits of Campania (Southern Italy), B. Eng. Geol. Environ., 171–184, https://doi.org/10.1007/s100640000093, 2001.
Forestieri, A., Caracciolo, D., Arnone, E., and Noto, L. V.: Derivation of Rainfall Thresholds for Flash Flood Warning in a Sicilian Basin Using a Hydrological Model, Procedia Engineer., 154, 818–825, https://doi.org/10.1016/j.proeng.2016.07.413, 2016.
Gao, S. and Shain, L.: Effects of water stress on chestnut blight, Can. J. Forest Res., 25, 1030–1035, 1995.
Greco, R. and Gargano, R.: A novel equation for determining the suction stress of unsaturated soils from the water retention curve based on wetted surface area in pores, Water Resour. Res., 51, 6143–6155, https://doi.org/10.1002/2014WR016541, 2015.
Greco, R., Comegna, L., Damiano, E., Guida, A., Olivares, L., and Picarelli, L.: Hydrological modelling of a slope covered with shallow pyroclastic deposits from field monitoring data, Hydrol. Earth Syst. Sci., 17, 4001–4013, https://doi.org/10.5194/hess-17-4001-2013, 2013.
Greco, R., Comegna, L., Damiano, E., Guida, A., Olivares, L., and Picarelli, L.: Conceptual Hydrological Modeling of the Soil-bedrock Interface at the Bottom of the Pyroclastic Cover of Cervinara (Italy), Proced. Earth Plan. Sc., 122–131, https://doi.org/10.1016/j.proeps.2014.06.007, 2014.
Greco, R., Marino, P., Santonastaso, G. F., and Damiano, E.: Interaction between Perched Epikarst Aquifer and Unsaturated Soil Cover in the Initiation of Shallow Landslides in Pyroclastic Soils, Water, 10, 948, https://doi.org/10.3390/w10070948, 2018.
Greco, R., Comegna, L., Damiano, E., Marino, P., Olivares, L., and Santonastaso, G. F.: Recurrent rainfall-induced landslides on the slopes with pyroclastic cover of Partenio Mountains (Campania, Italy): Comparison of 1999 and 2019 events, Eng. Geol., 288, 106160, https://doi.org/10.1016/j.enggeo.2021.106160, 2021.
Greco, R., Marino, P., and Bogaard, T. A.: Recent Advancements of Landslide Hydrology, WIRES Water, 10, e1675, https://doi.org/10.1002/wat2.1675, 2023.
Hartmann, A., Goldscheider, N., Wagener, T., Lange, J., and Weiler, M.: Karst water resources in a changing world: Review of hydrological modeling approaches, Rev. Geophys., 52, 218–242, https://doi.org/10.1002/2013RG000443, 2014.
Herman, J., and Usher, W.: SALib: an open-source Python library for Sensitivity Analysis, The Journal of Open Source Software, 2, 97, https://doi.org/10.21105/joss.00097, 2017.
Iwanaga, T., Usher, W., and Herman, J.: Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses, Socio-Environmental Systems Modelling, 4, 18155–18155, https://doi.org/10.18174/SESMO.18155, 2022.
Lloyd, S. P.: Least Squares Quantization in PCM, IEEE T. Inform. Theory, 28, 129–137, https://doi.org/10.1109/TIT.1982.1056489, 1982.
Lu, N. and Likos, W. J.: Suction Stress Characteristic Curve for Unsaturated Soil, J. Geotech. Geoenviron., 131–142, https://doi.org/10.1061/(asce)1090-0241(2006)132:2(131), 2006.
Marino, P., Comegna, L., Damiano, E., Olivares, L., and Greco, R.: Monitoring the Hydrological Balance of a Landslide-Prone Slope Covered by Pyroclastic Deposits over Limestone Fractured Bedrock, Water, 12, 3309, https://doi.org/10.3390/w12123309, 2020a.
Marino, P., Peres, D. J., Cancelliere, A., Greco, R., and Bogaard, T. A.: Soil moisture information can improve shallow landslide forecasting using the hydrometeorological threshold approach, Landslides, 17, 2041–2054, https://doi.org/10.1007/s10346-020-01420-8, 2020b.
Marino, P., Santonastaso, G. F., Fan, X., and Greco, R.: Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow, Landslides, 31–41, https://doi.org/10.1007/s10346-020-01484-6, 2021.
McDowell, N., Pockman, W. T., Allen, C. D., Breshears, D. D., Cobb, N., Kolb, T., Plaut, J., Sperry, J., West, A., Williams, D. G., and Yepez, E. A.: Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought?, New Phytologist, 178, 719–739, https://doi.org/10.1111/J.1469-8137.2008.02436.X, 2008.
Napolitano, E., Fusco, F., Baum, R .L., Godt, J. W., and de Vita, P.: Effect of antecedent-hydrological conditions on rainfall triggering of debris flows in ash-fall pyroclastic mantled slopes of Campania (southern Italy), Landslides, 13, 967–983, https://doi.org/10.1007/s10346-015-0647-5, 2016.
Neyman, J. and Scott, E. L.: Statistical Approach to Problems of Cosmology, J. Roy. Stat. Soc. Ser. B, 20, 1–29, https://doi.org/10.1111/j.2517-6161.1958.tb00272.x, 1958.
Nieber, J. L. and Sidle, R. C.: How do disconnected macropores in sloping soils facilitate preferential flow?, Hydrol. Process., 24, 1582–1594, https://doi.org/10.1002/hyp.7633, 2010.
Olivares, L. and Picarelli, L.: Shallow flowslides triggered by intense rainfalls on natural slopes covered by loose unsaturated pyroclastic soils, Geotechnique, 283–287, https://doi.org/10.1680/geot.2003.53.2.283, 2003.
Pagano, L., Picarelli, L., Rianna, G., and Urciuoli, G.: A simple numerical procedure for timely prediction of precipitation-induced landslides in unsaturated pyroclastic soils, Landslides, 7, 273–289, https://doi.org/10.1007/s10346-010-0216-x, 2010.
Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., Lienert, S., Lombardozzi, D., Nabel, J. E. M. S., Ottlé, C., Poulter, B., Zaehle, S., and Running, S. W.: Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling, Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, 2020.
Paulik, C., Dorigo, W., Wagner, W., and Kidd, R.: Validation of the ASCAT Soil Water Index using in situ data from the International Soil Moisture Network, Int. J. Appl. Earth Obs., 30, 1–8, https://doi.org/10.1016/J.JAG.2014.01.007, 2014.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M. Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Peres, D. J. and Cancelliere, A.: Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach, Hydrol. Earth Syst. Sci., 18, 4913–4931, https://doi.org/10.5194/hess-18-4913-2014, 2014.
Peres, D. J., Cancelliere, A., Greco, R., and Bogaard, T. A.: Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds, Nat. Hazards Earth Syst. Sci., 18, 633–646, https://doi.org/10.5194/nhess-18-633-2018, 2018.
Perrin, J., Jeannin, P. Y., and Zwahlen, F.: Epikarst storage in a karst aquifer: A conceptual model based on isotopic data, Milandre test site, Switzerland. J. Hydrol., 279, 106–124, https://doi.org/10.1016/S0022-1694(03)00171-9, 2003.
Pirone, M., Papa, R., Nicotera, M. V., and Urciuoli, G.: Soil water balance in an unsaturated pyroclastic slope for evaluation of soil hydraulic behaviour and boundary conditions, J. Hydrol., 63–83, https://doi.org/10.1016/j.jhydrol.2015.06.005, 2015.
Ponce, V. M. and Hawkins, R. H.: Runoff Curve Number: Has It Reached Maturity?, J. Hydrol. Eng., 1, 11–19, https://doi.org/10.1061/(ASCE)1084-0699(1996)1:1(11), 1996.
Revellino, P., Guerriero, L., Gerardo, G., Hungr, O., Fiorillo, F., Esposito, L., and Guadagno, F. M.: Initiation and propagation of the 2005 debris avalanche at Nocera Inferiore (Southern Italy), Ital. J. Geosci., 366–379, https://doi.org/10.3301/IJG.2013.02, 2013.
Reichenbach, P., Cardinali, M., De Vita, P., and Guzzetti, F.: Regional hydrological thresholds for landslides and floods in the Tiber River Basin (central Italy), Environ. Geol., 35, 146–159, https://doi.org/10.1007/s002540050301, 1998.
Richards, L. A.: Capillary conduction of liquids through porous mediums, J. Appl. Phys., 318–333, https://doi.org/10.1063/1.1745010, 1931.
Rodriguez-Iturbe, I., Febres De Power, B., and Valdes, J. B.: Rectangular pulses point process models for rainfall: analysis of empirical data, J. Geophys. Res., 92, 9645–9656, https://doi.org/10.1029/JD092iD08p09645, 1987.
Rolandi, G., Bellucci, F., Heizler, M. T., Belkin, H. E., and De Vivo, B.: Tectonic controls on the genesis of ignimbrites from the Campanian Volcanic Zone, southern Italy, Miner. Petrol., 79, 3–31, https://doi.org/10.1007/s00710-003-0014-4, 2003.
Roman Quintero, D. C., Marino, P., Santonastaso, G. F., and Greco, R.: Hydrological controls of slope response to precipitation – Code and Data (Hydrology), Zenodo [code and data set], https://doi.org/10.5281/zenodo.10107351, 2023.
Rousseeuw, P. J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987.
Saltelli, A.: Making best use of model evaluations to compute sensitivity indices, Comput. Phys. Commun., 145, 280–297, https://doi.org/10.1016/S0010-4655(02)00280-1, 2002.
Segoni, S., Piciullo, L., and Gariano, S. L.: A review of the recent literature on rainfall thresholds for landslide occurrence, Landslides, 15, 1483–1501, https://doi.org/10.1007/s10346-018-0966-4, 2018.
Shuttleworth, W. J.: Evaporation, in: Handbook of Hydrology, edited by: Maidment, D. R., McGraw-Hill, New York, NY, USA, ISBN: 9780070397323, 1993.
Sobol, I. M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Math. Comput. Simulat., 55, 271–280, https://doi.org/10.1016/S0378-4754(00)00270-6, 2001.
Stone, M.: Cross-validatory choice and assessment of statistical predictions, J. Royal Stat. Soc. Ser. B, 36, 111–147, 1974.
Tromp-Van Meerveld, H. J. and McDonnell, J. J.: Threshold relations in subsurface stormflow: 1. A 147-storm analysis of the Panola hillslope, Water Resour. Res., 42, 2410, https://doi.org/10.1029/2004WR003778, 2006a.
Tromp-Van Meerveld, H. J. and McDonnell, J. J.: Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis, Water Resour. Res., W02411, https://doi.org/10.1029/2004WR003800, 2006b.
Tufano, R., Formetta, G., Calcaterra, D., and De Vita, P.: Hydrological control of soil thickness spatial variability on the initiation of rainfall-induced shallow landslides using a three-dimensional model, Landslides, 18, 3367–3380, https://doi.org/10.1007/s10346-021-01681-x, 2021.
Twarakavi, N. K. C., Sakai, M., and Šimůnek, J.: An objective analysis of the dynamic nature of field capacity, Water Resour. Res., 45, W10410, https://doi.org/10.1029/2009WR007944, 2009.
van Genuchten, M. Th.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1, Soil Sci. Soc. Am. J., 44, 892, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Wicki, A., Lehmann, P., Hauck, C., Seneviratne, S. I., Waldner, P., and Stähli, M.: Assessing the potential of soil moisture measurements for regional landslide early warning, Landslides, 17, 1881–1896, https://doi.org/10.1007/S10346-020-01400-Y, 2020.
Williams, P. W.: The role of the epikarst in karst and cave hydrogeology: a review, Int. J. Speleol., 37, 1–10, https://doi.org/10.5038/1827-806X.37.1.1, 2008.
Short summary
This study shows a methodological approach using machine learning techniques to disentangle the relationships among the variables in a synthetic dataset to identify suitable variables that control the hydrologic response of the slopes. It has been found that not only is the rainfall responsible for the water accumulation in the slope; the ground conditions (soil water content and aquifer water level) also indicate the activation of natural slope drainage mechanisms.
This study shows a methodological approach using machine learning techniques to disentangle the...
