Abstract
Initial assessment of landslide susceptible areas is important in designing landslide mitigation measures. This study, a part of our study on the developing a landslide spatial model, aims to identify landslide susceptible areas using hard soil depth. In here, hard soil depth, defined as the depth interpreted from cone penetration test where the tip resistance reaches up to 250 kg/cm2, was used to identify landslide susceptible areas in a relatively small mountainous region in the middle western Central Java where landslides frequently occur. To this end, hard soil depth was interpolated using two different methods: inverse distance weighting and ordinary kriging (OK). The method producing the least errors and the most similar data distribution was selected. The result shows that OK is the best fitting model and exhibits clear pattern related to the recorded landslide sites. From interpolated hard soil depth in the landslide sites, it can be surmised that landslide susceptible areas are places possessing hard soil depth of 2.6–13.4 m. This finding is advantageous for policy makers in planning and designing efforts for landslide mitigation in middle western Central Java and should be applicable for other regions.
This is a preview of subscription content, log in via an institution to check access.
References
Adhikary SK, Muttil N, Yilmaz AG (2017) Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments. Hydrol Process 31:2143–2161. https://doi.org/10.1002/hyp.11163
Al-Zoubi B, al Rawi M (2008) An efficient approach for computing silhouette coefficients. In: Journal of Computer Science. pp 252–255
Annelies G, Vervoort A (2010) Geostatistical interpolation of soil properties in boom clay in flanders. In: Atkinson PM, Lloyd CD (eds) geoENV VII—Geostatistics for Environmental applications. Springer, Netherlands, pp 219–230
Apriyono A, Yanto, Santoso PB, Sumiyanto (2018) Soil classification based on cone penetration test (CPT) data in Western Central Java. AIP Conf Proc 1941:20004. https://doi.org/10.1063/1.5028062
Aytaç E (2020) Unsupervised learning approach in defining the similarity of catchments: hydrological response unit based k-means clustering, a demonstration on Western Black Sea Region of Turkey. Int Soil Water Conserv Res. https://doi.org/10.1016/j.iswcr.2020.05.002
Banholzer S, Kossin J, Donner S (2014) The impact of climate change on natural disasters. In: Zommer Z, Singh A (eds) Reducing disaster: early warning systems for climate change. Springer, New York, pp 21–49
Baskan O, Erpul G, Dengiz O (2009) Comparing the efficiency of ordinary kriging and cokriging to estimate the Atterberg limits spatially using some soil physical properties. Clay Miner 44:181–193. https://doi.org/10.1180/claymin.2009.044.2.181
Boyle C (2010) Kriging neighbourhood analysis by slope of regression and weight of mean—evaluation with the Jura data set. Min Technol 119:49–58. https://doi.org/10.1179/037178410X12741755140804
Buchwalder M, Bühlmann H, Merz M, Wüthrich MV (2006) The mean square error of prediction in the chain ladder reserving method (Mack and Murphy Revisited). ASTIN Bull 36:521–542. https://doi.org/10.1017/s0515036100014628
Cepeda J, Smebye H, Vangelsten B et al. (2010) Landslide risk in Indonesia. Global assessment report on disaster risk reduction
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?—arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250
Das BM (1994) Principle of foundation engineering. PWS-KENT Publishing Company
Fan L, Lehmann P, Or D (2016) Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall-induced landslides. Water Resour Res 52:1781–1799. https://doi.org/10.1002/2015WR017758
Formetta G, Capparelli G, Versace P (2016) 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
Fritsch C, Coeurdassier M, Giraudoux P et al. (2011) Spatially explicit analysis of metal transfer to biota: influence of soil contamination and landscape. PLoS One 6
Gräler B, Pebesma E, Heuvelink G (2016) Spatio-Temporal Interpolation using gstat. R J 8:
Hidayat R (2018) Prosiding Seminar Nasional Teknik Sipil 2018
Jesus C, Oliveira S, Sena C, Marques F (2017) Understanding constraints and triggering factors of landslides: regional and local perspectives on a drainage basin. Geosciences 8:2. https://doi.org/10.3390/geosciences8010002
Lesch SM, Corwin DL (2008) Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests. Geoderma 148:130–140. https://doi.org/10.1016/j.geoderma.2008.09.014
Mälicke M, Hassler S, Weiler M et al (2018) Exploring hydrological similarity during soil moisture recession periods using time dependent variograms. Hydrol Earth Syst Sci Discuss. https://doi.org/10.5194/hess-2018-396
Muller JR, Martel SJ (2000) numerical models of translational landslide rupture surface growth. Pure Appl Geophys 157:1009–1038. https://doi.org/10.1007/s000240050015
Olea RA, Olea RA (1999) Ordinary Kriging. Geostatistics for Engineers and Earth Scientists. Springer, US, pp 39–65
Pebesma E, Gräler B (2021) Introduction to Spatio-Temporal variography
Postance B, Hillier J, Dijkstra T, Dixon N (2018) Comparing threshold definition techniques for rainfall-induced landslides: a national assessment using radar rainfall. Earth Surf Process. Landforms
Roslee R, Talip TAJ& MA, (2012) Integration of GIS using GEOSTAtistical Interpolation Techniques (Kriging) (GEOSTAINT-K) in deterministic models for landslide susceptibility analysis (LSA) at Kota Kinabali, Sabah, Malaysia. J Geogr Geol 4:18–32
Tabari Hosseinand Sabziparvar A-A and AM (2011) Comparison of artificial neural network and multivariate linear regression methods for estCimation of daily soil temperature in an arid region. Meteorol Atmos Phys 110:135–142. https://doi.org/10.1007/s00703-010-0110-z
Santoso PB, Yanto, Apriyono A, Suryani R (2018) Inverse distance weighting interpolated soil properties and their related landslide occurrences. MATEC Web Conf 195
Sari AP, Maulidya M, Butarbutar RN et al. (2007) Executive summary: indonesia and climate change Working Paper on Current Status and Policies
Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. As Comput Mach 23rd Annu Conf
Šiljeg A, Lozić S, Šiljeg S (2015) A comparison of interpolation methods on the basis of data obtained from a bathymetric survey of Lake Vrana, Croatia. Hydrol Earth Syst Sci 19:3653–3666. https://doi.org/10.5194/hess-19-3653-2015
Taharin M, Roslee R, Amaludin A (2018) Geotechnical characterization in hilly area of Kundasang, Sabah, Malaysia. ASM Sci J 11:124–131
Wang S, Huang GH, Lin QG et al (2014) Research article. Int J Climatol 34:3745–3751. https://doi.org/10.1002/joc.3941
Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82
Wong DW, Yuan L, Perlin SA (2004) Comparison of spatial interpolation methods for the estimation of air quality data. J Expo Anal Environ Epidemiol 14:404
Yanto, Livneh B, Rajagopalan B (2017) Development of a gridded meteorological dataset over Java island, Indonesia 1985–2014. Sci Data 4:170072. https://doi.org/10.1038/sdata.2017.72
Yao X, Fu B, Lu Y et al. (2013) Comparison of four spatial interpolation methods for estimating soil moisture in a complex terrain catchment. PLoS One 8:e54660. https://doi.org/10.1371/journal.pone.0054660
Zêzere JL, Trigo RM, Trigo IF (2005) Shallow and deep landslides induced by rainfall in the Lisbon region (Portugal): assessment of relationships with the North Atlantic Oscillation. Nat Hazards Earth Syst Sci 5:331–344. https://doi.org/10.5194/nhess-5-331-2005
Acknowledgements
This research is funded by Jenderal Soedirman University. We thank to the Soil Mechanics Laboratory of Jenderal Soedirman University for substantial data of soil properties. In addition, we also thank to Regional Disaster Countermeasure Agency of Banjarnegara, Purbalingga and Banyumas district for the landslide events data.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Yanto, Apriyono, A., Santoso, P.B. et al. Landslide susceptible areas identification using IDW and Ordinary Kriging interpolation techniques from hard soil depth at middle western Central Java, Indonesia. Nat Hazards 110, 1405–1416 (2022). https://doi.org/10.1007/s11069-021-04982-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-021-04982-5