1. Home
  2. Vol 11, No 3 (2025)
  3. Rifa’i

Landslide Susceptibility Assessment Using Combined TRIGRS and Flow-R

Ahmad Rifa’i, Ragil A. Yuniawan, Fikri Faris, Tiara R. Trisnawati, Byon Rezy Pradana Purba, Andy Subiyantoro, Eka Priangga Hari Suryana, Banata Wahid Ridwan

Abstract


Landslides were addressed as one of the natural hazards that can create extensive disasters. Effective assessment to locate potential landslide events is crucial for planning and risk mitigation. This study, which is located in the Sumitro watershed, Kulon Progo, Yogyakarta, presents a novel approach to landslide susceptibility assessment by integrating the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS) with the Flow-R model. Five key parameters, namely slope, soil properties, groundwater level, soil thickness, and rainfall, were used to create the landslide susceptibility zonation. TRIGRS was used to identify the landslide initiation, while Flow-R was used to create the run-out area. The result was then validated through statistical evaluation using Area Under Curve (AUC) based on the landslide inventory. Results show that landslide susceptibility zonation created from TRIGRS alone resulted in an AUC value of 0.679, while the combination of TRIGRS-Flow-R susceptibility zonation shows a better AUC value of 0.728. The increase of the AUC value of almost 0.05 has enhanced the correlation between the landslide susceptibility zonation and landslide inventory from “acceptable” to “excellent” correlation. This result demonstrates that integrating Flow-R with TRIGRS improves the performance of landslide susceptibility zonation. This study offers a new perspective on creating landslide susceptibility zonation by combining two methods, yielding more reliable results.

 

Doi: 10.28991/CEJ-2025-011-03-020

Full Text: PDF


Keywords


Landslide; Landslide Susceptibility Zonation; TRIGRS; Flow-R; AUC.

References


Park, D. W., Nikhil, N. V., & Lee, S. R. (2013). Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event. Natural Hazards and Earth System Sciences, 13(11), 2833–2849. doi:10.5194/nhess-13-2833-2013.

Pecoraro, G., Calvello, M., & Piciullo, L. (2019). Monitoring strategies for local landslide early warning systems. Landslides, 16(2), 213–231. doi:10.1007/s10346-018-1068-z.

Piciullo, L., Calvello, M., & Cepeda, J. M. (2018). Territorial early warning systems for rainfall-induced landslides. Earth-Science Reviews, 179, 228–247. doi:10.1016/j.earscirev.2018.02.013.

Rifai, A., Andika Yuniawan, R., Faris, F., Subiyantoro, A., Sidik, V., & Prayoga, H. (2022). Performance of rainfall satellite threshold to predict landslide events in Girimulyo District. 2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES), 1–6. doi:10.1109/icares56907.2022.9993592.

Yuniawan, R. A., Rifa’i, A., Faris, F., Subiyantoro, A., Satyaningsih, R., Hidayah, A. N., Hidayat, R., Mushthofa, A., Ridwan, B. W., Priangga, E., Muntohar, A. S., Jetten, V. G., Westen, C. J. van, Bout, B. V. den, & Sutanto, S. J. (2022). Revised Rainfall Threshold in the Indonesian Landslide Early Warning System. Geosciences, 12(3), 129. doi:10.3390/geosciences12030129.

Hadmoko, D. S., Lavigne, F., Sartohadi, J., Hadi, P., & Winaryo. (2010). Landslide hazard and risk assessment and their application in risk management and landuse planning in eastern flank of Menoreh Mountains, Yogyakarta Province, Indonesia. Natural Hazards, 54(3), 623–642. doi:10.1007/s11069-009-9490-0.

Abbaszadeh Shahri, A., Spross, J., Johansson, F., & Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183, 104225. doi:10.1016/j.catena.2019.104225.

Broeckx, J., Vanmaercke, M., Duchateau, R., & Poesen, J. (2018). A data-based landslide susceptibility map of Africa. Earth-Science Reviews, 185, 102–121. doi:10.1016/j.earscirev.2018.05.002.

Bălteanu, D., Micu, M., Jurchescu, M., Malet, J. P., Sima, M., Kucsicsa, G., Dumitrică, C., Petrea, D., Mărgărint, M. C., Bilaşco, Ş., Dobrescu, C. F., Călăraşu, E. A., Olinic, E., Boți, I., & Senzaconi, F. (2020). National-scale landslide susceptibility map of Romania in a European methodological framework. Geomorphology, 371, 107432. doi:10.1016/j.geomorph.2020.107432.

Polykretis, C., Ferentinou, M., & Chalkias, C. (2014). A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece). Bulletin of Engineering Geology and the Environment, 74(1), 27–45. doi:10.1007/s10064-014-0607-7.

Do, H. M., Yin, K. L., & Guo, Z. Z. (2020). A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the Flow-R model for landslide susceptibility assessment. Geomatics, Natural Hazards and Risk, 11(1), 2449–2485. doi:10.1080/19475705.2020.1846086.

Stanley, T., & Kirschbaum, D. B. (2017). A heuristic approach to global landslide susceptibility mapping. Natural Hazards, 87(1), 145–164. doi:10.1007/s11069-017-2757-y.

Hasekioğulları, G. D., & Ercanoglu, M. (2012). A new approach to use AHP in landslide susceptibility mapping: A case study at Yenice (Karabuk, NW Turkey). Natural Hazards, 63(2), 1157–1179. doi:10.1007/s11069-012-0218-1.

Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63(2), 965–996. doi:10.1007/s11069-012-0217-2.

Sonker, I., Tripathi, J. N., & Singh, A. K. (2021). Landslide susceptibility zonation using geospatial technique and analytical hierarchy process in Sikkim Himalaya. Quaternary Science Advances, 4, 100039. doi:10.1016/j.qsa.2021.100039.

Rahman, M. S., Ahmed, B., & Di, L. (2017). Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria. Journal of Mountain Science, 14(10), 1919–1937. doi:10.1007/s11629-016-4220-z.

Goyes-Peñafiel, P., & Hernandez-Rojas, A. (2021). Landslide susceptibility index based on the integration of logistic regression and weights of evidence: A case study in Popayan, Colombia. Engineering Geology, 280, 105958. doi:10.1016/j.enggeo.2020.105958.

Conforti, M., & Ietto, F. (2021). Modeling shallow landslide susceptibility and assessment of the relative importance of predisposing factors, through a GIS-based statistical analysis. Geosciences (Switzerland), 11(8), 333. doi:10.3390/geosciences11080333.

Huang, S., & Chen, L. (2024). Landslide susceptibility mapping using an integration of different statistical models for the 2015 Nepal earthquake in Tibet. Geomatics, Natural Hazards and Risk, 15(1), 2396908. doi:10.1080/19475705.2024.2396908.

Wang, Y., Wang, L., Liu, S., Liu, P., Zhu, Z., & Zhang, W. (2023). A comparative study of regional landslide susceptibility mapping with multiple machine learning models. Geological Journal, 59(9), 2383–2400. doi:10.1002/gj.4902.

Aditian, A., Kubota, T., & Shinohara, Y. (2018). Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101–111. doi:10.1016/j.geomorph.2018.06.006.

Choi, J., Oh, H. J., Lee, H. J., Lee, C., & Lee, S. (2012). Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering Geology, 124(1), 12–23. doi:10.1016/j.enggeo.2011.09.011.

Tang, R. X., Kulatilake, P. H. S. W., Yan, E. C., & Cai, J. Sen. (2020). Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks. Bulletin of Engineering Geology and the Environment, 79(5), 2235–2254. doi:10.1007/s10064-019-01684-y.

Marin, R. J., Velásquez, M. F., & Sánchez, O. (2021). Applicability and performance of deterministic and probabilistic physically based landslide modeling in a data-scarce environment of the Colombian Andes. Journal of South American Earth Sciences, 108, 103175. doi:10.1016/j.jsames.2021.103175.

Blais-Stevens, A., & Behnia, P. (2016). Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada. Natural Hazards and Earth System Sciences, 16(2), 449–462. doi:10.5194/nhess-16-449-2016.

Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60–91. doi:10.1016/j.earscirev.2018.03.001.

Hong, H., Wang, D., Zhu, A. X., & Wang, Y. (2024). Landslide susceptibility mapping based on the reliability of landslide and non-landslide sample. Expert Systems with Applications, 243, 122933. doi:10.1016/j.eswa.2023.122933.

Agboola, G., Beni, L. H., Elbayoumi, T., & Thompson, G. (2024). Optimizing landslide susceptibility mapping using machine learning and geospatial techniques. Ecological Informatics, 81, 102583. doi:10.1016/j.ecoinf.2024.102583.

Lee, S., Jang, J., Kim, Y., Cho, N., & Lee, M. J. (2020). Susceptibility analysis of the Mt. Umyeon landslide area using a physical slope model and probabilistic method. Remote Sensing, 12(16), 2663. doi:10.3390/RS12162663.

Youssef, A. M., El‑Haddad, B. A., Skilodimou, H. D., Bathrellos, G. D., Golkar, F., & Pourghasemi, H. R. (2024). Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping. Natural Hazards, 120(15), 14227–14258. doi:10.1007/s11069-024-06769-w.

Doski, J. A. H. (2024). Landslide susceptibility evaluation in the mountainous terrain of the Zagros fold-thrust belt: a case study from Kurdistan, Northern Iraq. Natural Hazards. doi:10.1007/s11069-024-07069-z.

Berenguer, M., Sempere-Torres, D., & Hürlimann, M. (2015). Debris-flow forecasting at regional scale by combining susceptibility mapping and radar rainfall. Natural Hazards and Earth System Sciences, 15(3), 587–602. doi:10.5194/nhess-15-587-2015.

de Luiz Rosito Listo, F., Villaça Gomes, M. C., & Ferreira, F. S. (2021). Evaluation of shallow landslide susceptibility and Factor of Safety variation using the TRIGRS model, Serra do Mar Mountain Range, Brazil. Journal of South American Earth Sciences, 107, 103011. doi:10.1016/j.jsames.2020.103011.

Baum, R. L., & Godt, J. W. (2010). Early warning of rainfall-induced shallow landslides and debris flows in the USA. Landslides, 7(3), 259–272. doi:10.1007/s10346-009-0177-0.

Van Den Bout, B., Tang, C., Van Westen, C., & Jetten, V. (2022). Physically based modeling of co-seismic landslide, debris flow, and flood cascade. Natural Hazards and Earth System Sciences, 22(10), 3183–3209. doi:10.5194/nhess-22-3183-2022.

Baum, R. L., Savage, W. Z., & Godt, J. W. (2008). TRIGRS - A Fortran Program for Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis, Version 2.0. Open-File Report. U.S. Geological Survey, Reston, United States. doi:10.3133/ofr20081159.

Muntohar, A. S., Prasetyaningtiyas, G. A., & Hidayat, R. (2021). The Spatial Model using TRIGRS to determine Rainfall-Induced Landslides in Banjarnegara, Central Java, Indonesia. Journal of the Civil Engineering Forum, 7(3), 289. doi:10.22146/jcef.55282.

Alvioli, M., & Baum, R. L. (2016). Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface. Environmental Modelling & Software, 81, 122–135. doi:10.1016/j.envsoft.2016.04.002.

Horton, P., Jaboyedoff, M., Rudaz, B., & Zimmermann, M. (2013). Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale. Natural Hazards and Earth System Sciences, 13(4), 869–885. doi:10.5194/nhess-13-869-2013.

Susilo, A., Zulaikah, S., Pohan, A. F., Hasan, M. F. R., Hisyam, F., Rohmah, S., & Adhi, M. A. (2024). Vulnerability Index Assessment for Mapping Ground Movements Using the Microtremor Method as Geological Hazard Mitigation. Civil Engineering Journal, 10(5), 1616-1626. doi:10.28991/CEJ-2024-010-05-017.

Benmakhlouf, M., El Kharim, Y., Galindo-Zaldivar, J., & Sahrane, R. (2023). Landslide Susceptibility Assessment in Western External Rif Chain using Machine Learning Methods. Civil Engineering Journal (Iran), 9(12), 3218–3232. doi:10.28991/CEJ-2023-09-12-018.

He, J., Qiu, H., Qu, F., Hu, S., Yang, D., Shen, Y., Zhang, Y., Sun, H., & Cao, M. (2021). Prediction of spatiotemporal stability and rainfall threshold of shallow landslides using the TRIGRS and Scoops3D models. Catena, 197, 104999. doi:10.1016/j.catena.2020.104999.

Marin, R. J., & Velásquez, M. F. (2020). Influence of hydraulic properties on physically modelling slope stability and the definition of rainfall thresholds for shallow landslides. Geomorphology, 351, 106976. doi:10.1016/j.geomorph.2019.106976.

Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. (2007). Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorology and Atmospheric Physics, 98(3–4), 239–267. doi:10.1007/s00703-007-0262-7.

Segoni, S., Piciullo, L., & Gariano, S. L. (2018). A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides, 15(8), 1483–1501. doi:10.1007/s10346-018-0966-4.

Samodra, G., Chen, G., Sartohadi, J., & Kasama, K. (2018). Generating landslide inventory by participatory mapping: an example in Purwosari Area, Yogyakarta, Java. Geomorphology, 306, 306–313. doi:10.1016/j.geomorph.2015.07.035.

Iverson, R. M. (2000). Landslide triggering by rain infiltration. Water Resources Research, 36(7), 1897–1910. doi:10.1029/2000WR900090.

Pastorello, R., Michelini, T., & D’Agostino, V. (2017). On the criteria to create a susceptibility map to debris flow at a regional scale using Flow-R. Journal of Mountain Science, 14(4), 621–635. doi:10.1007/s11629-016-4077-1.

Pascal, H., Michel, J., & Eric, B. (2008). Debris flow susceptibility mapping at a regional scale. Proceedings of the 4th Canadian Conference on Geohazards: From Causes to Management. Presse de l’Université Laval, Quebec, Canada.

Ávila, F. F., Alvalá, R. C., Mendes, R. M., & Amore, D. J. (2021). The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models. Natural Hazards, 105(1), 1139–1161. doi:10.1007/s11069-020-04324-x.

Cantarino, I., Carrion, M. A., Goerlich, F., & Martinez Ibañez, V. (2019). A ROC analysis-based classification method for landslide susceptibility maps. Landslides, 16(2), 265–282. doi:10.1007/s10346-018-1063-4.

Yang, S., & Berdine, G. (2017). The receiver operating characteristic (ROC) curve. The Southwest Respiratory and Critical Care Chronicles, 5(19), 34. doi:10.12746/swrccc.v5i19.391.

SNI 8291. (2016). Determination and Compilation of Landslide Zonation. Badan Standardisasi Nasional, Jakarta, Indonesia. (In Indonesian).


Full Text: PDF

DOI: 10.28991/CEJ-2025-011-03-020

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2025 Ahmad Rifai, Ragil Andika Yuniawan, Fikri Faris, Tiara Ramadhani Trisnawati, Byon Rezy Pradana Purba, Andy Subiyantoro, Eka Priangga Hari Nasution, Banata Wahid Ridwan

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message