Modeling Landslide Hazard Using Machine Learning: A Case Study of Bogor, Indonesia
Abstract
Landslides occur in many parts of the world. Well-known drivers, such as geological activities, are often enhanced by violent precipitation in tropical regions, creating complex multi-hazard phenomena that complicate mitigation strategies. This research investigated the utility of spatial data, especially the digital elevation model of SRTM and Landsat 8 remotely sensed data, for the estimation of landslide distribution using a machine learning approach. Bogor Regency was chosen to demonstrate the approach considering its vast hilly/mountainous terrain and high rainfall. This study aimed to model landslide hazards in Sukajaya District using random forests and analyze the key variables contributing to the isolation of highly probable landslides. The initial model, using the default settings of random forest, demonstrated a notable accuracy of 93%, with an accuracy ranging from 91 to 94%. The three main predictors of landslides are rainfall, elevation, and slope inclination. Landslides were found to occur primarily in areas with high rainfall (2,668–3,228 mm),
elevations of 500 to 1,500 m, and steep slopes (25–45%). Approximately 4,536 ha were potentially prone to landslides, while the remaining area (> 12,000 ha) appeared relatively sound.
References
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Claessens L, Heuvelink GBM, Schoorl JM, Veldkamp A. 2005. DEM resolution effects on shallow landslide hazard and soil redistribution modelling. Earth Surf Proc Landforms. 30(4): 461-477.
Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S. 2008. DEM-based deterministic landslide hazard analysis in the Lesser Himalaya of Nepal. Georisk. 2(3): 161-178.
Hasegawa S, Dahal RK, Nishimura T, Nonomura A, Yamanaka M. 2009. DEM-Based analysis of earthquake-induced shallow landslide susceptibility. Geotech Geol Eng. 27(3): 419-430.
Hong Y, Adler RF, Huffman G. 2007. An experimental global prediction system for Rainfall-Triggered landslides using satellite remote sensing and geospatial datasets. IEEE Trans Geosci Remote Sens. 45(6): 1671-1680.
Hsieh HC, Chung CH, Huang CY. 2017. Using the NDVI and mean shift segmentation to extract landslide areas in the Lioukuei experimental forest region with multi-temporal FORMOSAT-2 images. Taiwan J For Sci. 32(3): 203-222.
Husein Malkawi AI, Saleh B, Al-Sheriadeh MS, Hamza MS. 2000. Mapping of landslide hazard zones in Jordan using remote sensing and GIS. J Urban Plan Dev. 126(1): 1-17.
Lee CF, Huang WK, Chang YL, Chi SY, Liao WC. 2018. Regional landslide susceptibility assessment using multi-stage remote sensing data along the coastal range highway in northeastern Taiwan. Geomorphology. 300: 113-127.
Mantovani F, Soeters R, Van Westen CJ. 1996. Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology. 15(3-4 SPEC. ISS.): 213-225.
Mukherjee S. 1999. Microzonation of seismic and landslide prone areas for alternate highway alignment in a part of western coast of India using remote sensing techniques. J Indian Soc Remote Sens. 27(2): 81-90.
Ohlmacher GC. 2007. Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol. 91(2-4): 117-134.
Pandey A, Dabral PP, Chowdary VM, Yadav NK. 2008. Landslide hazard zonation using remote sensing and GIS: A case study of Dikrong river basin, Arunachal Pradesh, India. Environ Geol. 54(7): 1517-1529.
Panuju DR, Paull DJ, Trisasongko BH. 2019. Combining binary and post-classification change analysis of augmented ALOS backscatter for identifying subtle land cover changes. Remote Sens. 11(1): 100.
Rouse JW, Haas RH, Scheel JA, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Proc. 3rd Earth Res Tech Sat (ERTS) Symp. NASA, Goddard Space Flight Center.
Rózycka M, Migoń P, Michniewicz A. 2017. Topographic Wetness Index and Terrain Ruggedness Index in geomorphic characterisation of landslide terrains, on examples from the Sudetes, SW Poland. Zeitsch Geomorphologie. 61: 61-80.
Sajadi P, Sang YF, Gholamnia M, Bonafoni S, Brocca L, Pradhan B, Singh A. 2021. Performance evaluation of long ndvi timeseries from avhrr, modis and landsat sensors over landslide-prone locations in qinghai-tibetan plateau. Remote Sens. 13(16).
Shafique M, van der Meijde M, Khan MA. 2016. A review of the 2005 Kashmir earthquake-induced landslides; from a remote sensing prospective. J Asian Earth Sci. 118: 68-80.
Tengtrairat N, Woo WL, Parathai P, Aryupong C, Jitsangiam P, Rinchumphu D. 2021. Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models. Sensors. 21(13).
Trisasongko BH, Panuju DR, Paull DJ, Jia X, Griffin AL. 2017. Comparing six pixel-wise classifiers for tropical rural land cover mapping using four forms of fully polarimetric SAR data. Int J Remote Sens. 38(11): 3274-3293.
Trisasongko BH, Paull DJ, Griffin AL, Jia X, Panuju DR. 2019. On the relationship between the circumference of rubber trees and L-band waves. Int J Remote Sens. 40(16): 6395-6417.
Tsangaratos P, Loupasakis C, Nikolakopoulos K, Angelitsa V, Ilia I. 2018. Developing a landslide susceptibility map based on remote sensing, fuzzy logic and expert knowledge of the Island of Lefkada, Greece. Environ Earth Sci. 77(10).
Yang W, Wang M, Shi P. 2013. Using MODIS NDVI time series to identify geographic patterns of landslides in vegetated regions. IEEE Geosci Remote Sens Lett. 10(4): 707-710.
Authors
TjahjonoB., FirdianaI. and TrisasongkoB. H. (2024) “Modeling Landslide Hazard Using Machine Learning: A Case Study of Bogor, Indonesia”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 14(2), p. 407. doi: 10.29244/jpsl.14.2.407.
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