Environmental Modeling & Assessment

, Volume 23, Issue 4, pp 333–352 | Cite as

Soil Water Content Estimated by Support Vector Machine for the Assessment of Shallow Landslides Triggering: the Role of Antecedent Meteorological Conditions

  • Massimiliano BordoniEmail author
  • M. Bittelli
  • R. Valentino
  • S. Chersich
  • M. G. Persichillo
  • C. Meisina


Soil water content is a key parameter for representing water dynamics in soils. Its prediction is fundamental for different practical applications, such as identifying shallow landslides triggering. Support vector machine (SVM) is a machine learning technique, which can be used to predict the temporal trend of a quantity since training from past data. SVM was applied to a test slope of Oltrepò Pavese (northern Italy), where meteorological parameters coupled with soil water content at different depths (0.2, 0.4, 0.6, 1.0, 1.2, 1.4 m) were measured. Two SVM models were developed for water content assessment: (i) model 1, considering rainfall amount, air temperature, air humidity, net solar radiation, and wind speed; (ii) model 2, considering the same predictors of model 1 together with antecedent condition parameters (cumulated rainfall of 7, 30, and 60 days; mean air temperature of 7, 30, and 60 days). SVM model 2 showed significantly higher satisfactory results than model 1, for both training and test phases and for all the considered soil levels. SVM models trends were implemented in a methodology of slope safety factor assessment. For a real event occurred in the tested slope, the triggering time was correctly predicted using data estimated by SVM model based on antecedent meteorological conditions. This confirms the necessity of including these predictors for building a SVM technique able to estimate correctly soil moisture dynamics in time. The results of this paper show a promising potential application of the SVM methodologies for modeling soil moisture required in slope stability analysis.


Water content Support vector machines Shallow landslides Antecedent conditions 



We thank Marco Tumiati for the assistance on the executions of the laboratory tests on the studied soils. The authors wish to thank the anonymous reviewers for their suggestions and contribution to the work.


  1. 1.
    Liu, H., Xie, D., & Wu, W. (2008). Soil water content forecasting by ANN and SVM hybrid architecture. Environmental Monitoring and Assessment, 143, 187–193.Google Scholar
  2. 2.
    Bittelli, M. (2011). Measuring soil water content: a review. Hortechnology, 21(3), 293–300.Google Scholar
  3. 3.
    Ahmad, S., Kalra, A. F., & Stephen, H. (2010). Estimating soil moisture using remote sensing data: a machine learning approach. Advances in Water Resources, 33, 69–80.Google Scholar
  4. 4.
    Godt, J. W., Baum, R. L., & Lu, N. (2009). Landsliding in partially saturated materials. Geophysical Research Letters.
  5. 5.
    Montrasio, L., Valentino, R., & Losi, G. L. (2011). Towards a real-time susceptibility assessment of rainfall induced shallow landslides on a regional scale. Natural Hazards and Earth System Sciences, 11, 1927–1947.Google Scholar
  6. 6.
    Porporato, A., & Rodriguez-Iturbe, I. (2002). Ecohydrology—a challenging multidisciplinary research perspective. Hydrological Sciences Journal, 47(5), 811–821.Google Scholar
  7. 7.
    Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., Gordon, C. T., Kanae, S., Kowalczyk, E., Lawrence, D., Liu, P., Lu, C. H., Malyshev, S., McAvaney, B., Mitchell, K., Mocko, D., Oki, T., Oleson, K., Pitman, A., Sud, Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y., & Yamada, T. (2004). Regions of strong coupling between soil moisture and precipitation. Science, 305(5687), 1138–1140.Google Scholar
  8. 8.
    Ray, R. L., & Jacobs, J. M. (2007). Relationships among remotely sensed soil moisture, precipitation and landslide events. Natural Hazards, 43, 211–222.Google Scholar
  9. 9.
    Brocca, L., Melone, F., & Moramarco, T. (2008). On the estimation of antecedent wetness conditions in rainfall–runoff modelling. Hydrological Processes, 22, 629–642.Google Scholar
  10. 10.
    Brocca, L., Ponziani, F., Moramarco, T., Melone, F., Berni, N., & Wagner, W. (2012). Improving landslide forecasting using ASCAT-derived soil moisture data: a case study of the Torgiovannetto landslide in Central Italy. Remote Sensing, 4, 1232–1244.Google Scholar
  11. 11.
    Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., & Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128–5141.Google Scholar
  12. 12.
    Wagner, W., Pathe, C., Doubkova, M., Sabel, D., Bartsch, A., Hasenauer, S., Bloschl, G., Scipal, K., Fernzandez, J. M., & Low, A. (2008). Temporal stability of soil moisture and radar backscatter observed by the advanced aperture radar (ASAR). Sensors, 8, 1174–1197.Google Scholar
  13. 13.
    Ray, R. L., Jacobs, J. M., & Ballestero, T. P. (2011). Regional landslide susceptibility: spatiotemporal variations under dynamic soil moisture conditions. Natural Hazards, 39, 1317–1337.Google Scholar
  14. 14.
    Mittelbach, H., & Seneviratne, S. I. (2012). A new perspective on the spatio-temporal variability of soil moisture: temporal dynamics versus time-invariant contributions. Hydrology and Earth System Sciences, 16, 2169–2179.Google Scholar
  15. 15.
    Ponziani, F., Pandolfo, C., Stelluti, M., Berni, N., Brocca, L., & Moramarco, T. (2012). Assessment of rainfall thresholds and soil moisture modeling for operational hydrogeological risk prevention in the Umbria region (central Italy). Landslides, 9, 229–237.Google Scholar
  16. 16.
    Vauchad, G., Passerat De Silans, A., Balabanis, P., & Vauclin, M. (1985). Temporal stability of spatially measured soil water probability density function. Soil Science Society of America Journal, 49(4), 822–828.Google Scholar
  17. 17.
    Montrasio, L., & Valentino, R. (2008). A model for triggering mechanisms of shallow landslides. Natural Hazards and Earth System Sciences, 8, 1149–1159.Google Scholar
  18. 18.
    Deng, J., Chen, X., Du, Z., & Zhang, Y. (2011). Soil water simulation and predication using stochastic models based on LS-SVM for red soil region of China. Water Resources Management, 25, 2823–2836.Google Scholar
  19. 19.
    Chopart, J. L., & Vauclin, M. (1990). Water balance estimation model: field test and sensitivity analysis. Soil Science Society of America Journal, 54(5), 1377–1384.Google Scholar
  20. 20.
    Cameira, M. R., Fernando, R. M., & Pereira, L. S. (2003). Monitoring water and NO3-N in irrigated maize fields in the Sorraia Watershed, Portugal. Agricultural Water Management, 60(3), 199–216.Google Scholar
  21. 21.
    Panigrahi, B., & Panda, S. N. (2003). Field test of a soil water balance simulation model. Agricultural Water Management, 58(3), 223–240.Google Scholar
  22. 22.
    Valentino, R., Montrasio, L., Losi, G. L., & Bittelli, M. (2011). An empirical model for the evaluation of the degree of saturation of shallow soils in relation to rainfalls. Canadian Geotechnical Journal, 48, 795–809.Google Scholar
  23. 23.
    Van Dam, J.C., Huygen, J., Wesseling, J.G., Feddes, R.A., Kabat, P., Van Walsum, P.E.V., Groenendijk, P. & Van Diepen, C.A. (1997). Theory of SWAP, version 2.0. Simulation of water flow, solute transport and plant growth in the soil-water-atmosphere-plant environment. Tech. Rep. Dep. Water Resources, DLO Winand Staring Centre, Wageningen, the Netherlands.Google Scholar
  24. 24.
    Neitsch, S.L., Arnold, J.G., Kiniry, J.R. & Williams, J.R. (2005). Soil and water assessment tool (SWAT), theoretical documentation. Blackland Research Center, Grassland, Soil and Water Research Laboratory, Agricultural Research Service, Temple.Google Scholar
  25. 25.
    Šimůnek, J., & Van Genuchten, M. T. (2008a). Modeling nonequilibrium flow and transport with HYDRUS. Vadose Zone Journal, 7, 782–797.Google Scholar
  26. 26.
    Šimůnek, J., Van Genuchten, M. T., & Šejna, M. (2008b). The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media. Version 4.0. Riverside: Dep. Environ. Sci., Univ. of California.Google Scholar
  27. 27.
    Lamorski, K., Pastuszka, T., Krzyszczak, J., Sławiński, C., & Witkowska-Walczak, B. (2013). Soil water dynamic modeling using the physical and support vector machine methods. Vadose Zone Journal. Scholar
  28. 28.
    ASCE Task Committee. (2000a). Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering, 5(2), 115–123.Google Scholar
  29. 29.
    ASCE Task Committee. (2000b). Artificial neural networks in hydrology. II: hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124–137.Google Scholar
  30. 30.
    Jiang, H., & Cotton, W. R. (2004). Soil moisture estimation using an artificial neural network: a feasibility study. Canadian Journal of Remote Sensing, 30, 827–839.Google Scholar
  31. 31.
    Zou, P. J., Yang, J., Fu, J., Liu, G., & Li, D. (2010). Artificial neural network and time series models for predicting soil salt and water content. Agricultural Water Management, 97, 2009–2019.Google Scholar
  32. 32.
    Dai, X., Huo, Z., & Wang, H. (2011). Simulation for response of crop yield to soil moisture and salinity with artificial neural network. Field Crops Research, 121, 441–449.Google Scholar
  33. 33.
    Gill, M. K., Kemblowski, M. W., & McKee, M. (2007). Soil moisture data assimilation using support vector machines and ensemble Kalman filter. Journal of the American Water Resources Association, 43(4), 1004–1015.Google Scholar
  34. 34.
    Liu, D., Yu, Z. B., & Hai-she, L. (2010). Data assimilation using support vector machines and ensemble Kalman filter for multi-layer soil moisture prediction. Water Science and Engineering, 3(4), 361–377.Google Scholar
  35. 35.
    Yu, Z., Liu, D., Lu, H., Fu, X., Xiang, L., & Zhu, Y. (2012). A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter. Journal of Hydrology, 475, 53–64.Google Scholar
  36. 36.
    Raghavendra, S., & Deka, P. C. (2014). Support vector machine applications in the field of hydrology: a review. Applied Soft Computing, 19, 372–386.Google Scholar
  37. 37.
    Asefa, T., Kemblowski, M., McKee, M., & Khalil, A. (2006). Multi-time scale stream flow predictions: the support vector machines approach. Journal of Hydrology, 318, 7–16.Google Scholar
  38. 38.
    Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.Google Scholar
  39. 39.
    Li, C., Ma, T., Zhu, X., & Li, W. (2011). The power-law relationship between landslide occurrence and rainfall level. Geomorphology, 130, 221–229.Google Scholar
  40. 40.
    Arnone, E., Caracciolo, D., Noto, L. V., Preti, F., & Bras, R. L. (2016b). Modeling the hydrological and mechanical effect of roots on shallow landslides. Water Resources Research, 52(11), 8590–8612.Google Scholar
  41. 41.
    Glade, T., Crozier, M., & Smith, P. (2000). Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “antecedent daily rainfall model”. Pure and Applied Geophysics, 157, 1059–1079.Google Scholar
  42. 42.
    Aleotti, P. (2004). A warning system for rainfall-induced shallow failures. Engineering Geology, 73, 247–265.Google Scholar
  43. 43.
    Dahal, R. K., & Hasegawa, S. (2008). Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology, 100, 429–443.Google Scholar
  44. 44.
    Giannecchini, R., Galanti, Y., & D’Amato Avanzi, G. (2012). Critical rainfall thresholds for triggering shallow landslides in the Serchio River Valley (Tuscany, Italy). Natural Hazards and Earth System Sciences, 12, 829–842.Google Scholar
  45. 45.
    Martelloni, G., Segoni, S., Fanti, R., & Catani, F. (2012). Rainfall thresholds for the forecasting of landslide occurrence at regional scale. Landslides, 9, 485–495.Google Scholar
  46. 46.
    Lagomarsino, D., Segoni, S., Fanti, R., & Catani, F. (2013). Updating and tuning a regional-scale landslide early warning system. Landslides, 10, 91–97.Google Scholar
  47. 47.
    Ma, T., Li, C., Lu, Z., & Wang, B. (2014). An effective antecedent precipitation model derived from the power-law relationship between landslide occurrence and rainfall level. Geomorphology, 216, 187–192.Google Scholar
  48. 48.
    Mathew, J., Giri Babu, D., Kundu, S., Vinod Kumar, K., & Pant, C. C. (2014). Integrating intensity–duration-based rainfall threshold and antecedent rainfall-based probability estimate towards generating early warning for rainfall-induced landslides in parts of the Garhwal Himalaya, India. Landslides, 11, 575–588.Google Scholar
  49. 49.
    Kirschbaum, D. B., Stanley, T., & Simmons, J. (2015). A dynamic landslide hazard assessment system for Central America and Hispaniola. Natural Hazards and Earth System Sciences, 15, 2257–2272.Google Scholar
  50. 50.
    Caracciolo, D., Arnone, E., Lo Conti, F., & Noto, L. V. (2017). Exploiting historical rainfall and landslide data in a spatial database for the derivation of critical rainfall thresholds. Environmental Earth Sciences, 76, 222. Scholar
  51. 51.
    Lu, N., & Godt, J. W. (2008). Infinite slope stability under steady unsaturated seepage conditions. Water Resources Research.
  52. 52.
    Lu, N., & Godt, J. W. (2013). Hillslope hydrology and stability. Cambridge: Cambridge University Press.Google Scholar
  53. 53.
    Zizioli, D., Meisina, C., Valentino, R., & Montrasio, L. (2013). Comparison between different approaches to modelling shallow landslide susceptibility: a case history in Oltrepò Pavese, Northern Italy. Natural Hazards and Earth System Sciences, 13, 559–573.Google Scholar
  54. 54.
    Bordoni, M., Meisina, C., Valentino, R., Lu, N., Bittelli, M., & Chersich, S. (2015). Hydrological factors affecting rainfall-induced shallow landslides: from the field monitoring to a simplified slope stability analysis. Engineering Geology, 193, 19–37.Google Scholar
  55. 55.
    IUSS Working Group WRB. (2014). World Reference Base for Soil Resources 2014. International soil classification system for naming soils and creating legends for soil maps. Rome: World Soil Resources Reports No. 106, FAO.Google Scholar
  56. 56.
    Bordoni, M., Bittelli, M., Valentino, R., Chersich, S., & Meisina, C. (2017). Improving the estimation of complete field soil water characteristic curves through field monitoring data. Journal of Hydrology, 552, 283–305.Google Scholar
  57. 57.
    Bittelli, M., Valentino, R., Salvatorelli, F., & Rossi Pisa, P. (2012). Monitoring soil–water and displacement conditions leading to landslide occurrence in partially saturated clays. Geomorphology, 173–174, 161–173.Google Scholar
  58. 58.
    Cortes, C., & Vapnik, V. N. (1995). Support vector networks. Machine Learning, 20, 273–297.Google Scholar
  59. 59.
    Cristianini, N., & Shaw-Taylor, J. (2000). An introduction to support vector machines and other kernel based learning methods. Cambridge: Cambridge University Press.Google Scholar
  60. 60.
    Vapnik, V. N. (1998). Statistical learning theory. New York: John Wiley & Sons.Google Scholar
  61. 61.
    Yu, X., & Liong, S. Y. (2007). Forecasting of hydrologic time series with ridge regression in feature space. Journal of Hydrology, 332, 290–302.Google Scholar
  62. 62.
    Mehrotra, R., & Sharma, A. (2009). Evaluating spatio-temporal representation in daily rainfall sequences from three stochastic multi-site weather generation approaches. Advances in Water Resources, 32(6), 948–962.Google Scholar
  63. 63.
    Khalil, A. F., McKee, M., Kemblowski, M., Asefa, T., & Bastidas, L. (2006). Multiobjective analysis of chaotic dynamic systems with sparse learning machines. Advances in Water Resources, 29, 72–88.Google Scholar
  64. 64.
    Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C.C. & Lin, C.C. (2015). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. Resource Document. R package 1.6–7. Accessed 2 Feb 2017.
  65. 65.
    Lu, N., Godt, J. W., & Wu, D. T. (2010). A closed-form equation for effective stress in unsaturated soil. Water Resources Research.
  66. 66.
    Terzaghi, K. (1943). Theoretical soil mechanics. New York: John Wiley.Google Scholar
  67. 67.
    Bishop, A. W. (1954). The use of pore water coefficients in practice. Geotechnique, 4, 148–152.Google Scholar
  68. 68.
    Lu, N., & Likos, W. J. (2004). Unsaturated soil mechanics. Hoboken: Wiley.Google Scholar
  69. 69.
    Lu, N., & Likos, W. J. (2006). Suction stress characteristic curve for unsaturated soil. Journal of Geotechnical and Geoenvironmental Engineering, 132(2), 131–142.Google Scholar
  70. 70.
    Lu, N., Wu, B., & Tan, C. P. (2007). Tensile strength characteristics of unsaturated sands. Journal of Geotechnical and Geoenvironmental Engineering, 133(2), 144–154.Google Scholar
  71. 71.
    Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44, 892–898.Google Scholar
  72. 72.
    Godt, J. W., Baum, R. L., & Lu, N. (2009). Landsliding in partially saturated materials. Geophysical Research Letters, 36, L02403. Scholar
  73. 73.
    Goetz, J. N., Brenning, A., Petschko, H., & Leopold, P. (2015). Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers and Geosciences, 81, 1–11.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Earth and Environmental SciencesUniversity of PaviaPaviaItaly
  2. 2.Department of Agricultural ScienceUniversity of BolognaBolognaItaly
  3. 3.Department of Engineering and ArchitectureUniversity of ParmaParmaItaly

Personalised recommendations