GIS-Based Landslide Susceptibility Mapping Using Frequency Ratio and Shannon Entropy Models in Kulawi District, Indonesia

Year 2025, Volume: 11 Issue: 2, 115 - 128

Septianto Aldiansyah , Ilyas Madani Duwi Setiyo Wigati Ningsih

https://doi.org/10.33904/ejfe.1541146

Abstract

A landslide susceptibility area mapping using bivariate statistical models Frequency ratio (FR) and Shannon entropy (SE) was conducted using the Geographic Information Systems (GIS) platform in Kulawi District in Indonesia. Landslides often occur with high intensity in Kulawi District and cause road and bridge access to be cut off. There were 718 landslides identified covering a total area of 2.10 km2. Twelve landslide conditioning factors such as elevation, slope, curvature, aspect, topographic wetness index, lithology, distance from fault, distance from road, distance from river, land cover, normalized difference vegetation index, and precipitation were integrated with past landslide event data to determine the weight of each landslide conditioning factor and factor class using FR and SE models. In the solution process, landslide event data were grouped into training data and testing data. The area under the curve (AUC) of the receiver operating characteristic was used to evaluate the model performance. The results of this study indicated that the FR and SE models each produced the accuracy of 74.86% and 72.25%, while the prediction rate was 73.65% and 72.78%, respectively. The landslide susceptibility map represents the predicted landslide area, therefore the results of this study can be used to reduce the potential for landslide-related hazards in the study area.

Keywords

GIS , Landslide susceptibility , Frequency ratio , Shannon entropy , Kulawi

Thanks

We thank the anonymous reviewers for their comments and suggestions. We also thank the USGS and other data sources for providing satellite and other data.

References

• Abidi, A., Demehati, A., El Qandil, M. 2019. Landslide susceptibility assessment using evidence belief function and frequency ratio models in Taounate city (North of Morocco). Geotechnical and Geological Engineering, 37: 5457-5471. DOI: 10.1007/s10706-019-00992-0
• Aldiansyah, S., Ningsih, D.S.W., Saputra, R.A. 2023. Evaluation of Regional Spatial Development on Landslide and Flood Prone with Actual Site Conditions in Kendari City. Jurnal Wilayah dan Lingkungan, 11(1): 92-107. DOI: 10.14710/jwl.11.1.92-107
• Aldiansyah, S., Madani, I. 2024. Spatial model of wildfire susceptibility using machine learning approaches on Rawa Aopa Watumohai National Park, Indonesia. GeoScape, 18(1): 1-20. DOI: 10.2478/geosc-2024-0001
• Aldiansyah, S., Wardani, F. 2024a. Assessment of resampling methods on performance of landslide susceptibility predictions using machine learning in Kendari City, Indonesia. Water Practice & Technology, 19(1): 52-81. DOI: 10.2166/wpt.2024.002
• Aldiansyah, S., Wardani, F. 2024b. Spatiotemporal dynamic of soil erosion in the Roraya River Basin based on RUSLE model and Google Earth Engine. Journal of Hydroinformatics, 26(6): 1273-1294. DOI: 10.2166/hydro.2024.219
• Anis, Z., Wissem, G., Vali, V., Smida, H., Mohamed Essghaier, G. 2019. GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia. Open Geosciences, 11(1): 708-726. DOI: 10.1515/geo-2019-0056
• Ayalew, L., Yamagishi, H. 2005. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1-2): 15-31. DOI: 10.1016/j.geomorph.2004.06.010
• Bhardwaj, D., Sarkar, R. 2024. Landslide susceptibility mapping using probabilistic frequency ratio and Shannon entropy for Chamoli, Uttarakhand Himalayas. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 48(1): 377-395. DOI: 10.3390/su16052092
• BNPB, 2021. Kajian Risiko Bencana Nasional Provinsi Sulawesi Tengah 2022 – 2026. Palu: Badan Nasional Penanggulangan Bencana.
• BPS, 2015. Banyaknya Kejadian Bencana Alam menurut Jenis Bencana, 2014.
• BNPB, 2022. Indeks Risiko Bencana Indonesia. Jakarta: Badan Nasional Penanggulangan Bencana. Cantarino, I., Carrion, M.A., Martínez-Ibáñez, V., Gielen, E. 2023. Improving landslide susceptibility assessment through frequency ratio and classification methods—case study of valencia region (Spain). Applied Sciences, 13(8): 5146. DOI: 10.3390/app13085146
• Cao, Y., Wei, X., Fan, W., Nan, Y., Xiong, W., Zhang, S. 2021. Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China. PLoS one, 16(1): e0245668. DOI: 10.1371/journal.pone.0245668
• Chen, C.Y., Yu, F.C. 2011. Morphometric analysis of debris flows and their source areas using GIS. Geomorphology, 129(3-4): 387-397. DOI: 10.1016/j.geomorph.2011.03.002
• Congalton, R.G., Green, K. 2019. Assessing the accuracy of remotely sensed data: principles and practices. CRC press.
• Cruden, D. 1991. A simple definition of a landslide. Bulletin of Engineering Geology & the Environment, 43(1): 27-29. DOI: 10.1007/BF02590167
• Crozier, M.J. 1984. Field assessment of slope instability. Slope Instability, 103–142.
• Dam, N.D., Amiri, M., Al-Ansari, N., Prakash, I., Le, V., Nguyen, H.B.T., Pham, B.T. 2022. Evaluation of shannon entropy and weights of evidence models in landslide susceptibility mapping for the pithoragarh district of uttarakhand state, India. Advances in Civil Engineering, 2022(1): 1-16. DOI: 10.1155/2022/6645007
• Das, G., Lepcha, K. 2019. Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India. SN Applied Sciences, 1:1453. DOI: 10.1007/s42452-019-1499-8
• Du, G.L., Zhang, Y.S., Iqbal, J., Yang, Z.H., Yao, X. 2017. Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. Journal of Mountain Science, 14: 249-268. DOI: 10.1007/s11629-016-4126-9
• Ermini, L., Catani, F., Casagli, N. 2005. Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66(1-4): 327-343. DOI: 10.1016/j.geomorph.2004.09.025
• Es-smairi, A., El Moutchou, B., Touhami, A.E.O. 2021. Landslide susceptibility assessment using analytic hierarchy process and weight of evidence methods in parts of the Rif chain (northernmost Morocco). Arabian Journal of Geosciences, 14:1346. DOI: 10.1007/s12517-021-07660-9.
• Es-smairi, A., Elmoutchou, B., Mir, R.A., Touhami, A.E.O., Namous, M. 2023. Delineation of landslide susceptible zones using Frequency Ratio (FR) and Shannon Entropy (SE) models in northern Rif, Morocco. Geosystems and Geoenvironment, 2(4): 100195. DOI: 10.1016/j.geogeo.2023.100195
• Gentilucci, M., Pelagagge, N., Rossi, A., Domenico, A., Pambianchi, G. 2023. Landslide susceptibility using climatic–environmental factors using the weight-of-evidence method—a study area in Central Italy. Applied Sciences, 13(15): 8617. DOI: 10.3390/app13158617
• Getachew, N., Meten, M. 2021. Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia. Geoenvironmental Disasters, 8: 1-22. DOI: 10.1186/s40677-021-00177-z
• Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P., Galli, M., Ardizzone, F. 1999. Landslide hazard evaluation: an aid to a sustainable development. Geomorphology, 31(1-4): 181-216.
• Gyawali, P., Aryal, Y.M., Tiwari, A., Prajwol, K.C., Ansari, K. 2021. Landslide susceptibility assessment using bivariate statistical methods: a case study of Gulmi District, western Nepal. Engineering International, 3(2): 29-40. DOI: 10.36297/vw.jei.v3i2.60
• Haghizadeh, A., Siahkamari, S., Haghiabi, A.H., Rahmati, O. 2017. Forecasting flood-prone areas using Shannon’s entropy model. Journal of Earth System Science, 126: 39. DOI: 10.1007/s12040-017-0819-x
• Hamed, Y., Ahmadi, R., Hadji, R., Mokadem, N., Dhia, H. B., Ali, W. 2014. Groundwater evolution of the Continental Intercalaire aquifer of Southern Tunisia and a part of Southern Algeria: use of geochemical and isotopic indicators. Desalination and Water Treatment, 52(10-12): 1990-1996. DOI: 10.1080/19443994.2013.806221
• He, S., Pan, P., Dai, L., Wang, H., Liu, J. 2012. Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology, 171: 30-41. DOI: 10.1016/j.geomorph.2012.04.024
• He, N., Gao, X., Zhong, W., Xu, L., Gurkalo, F. 2024. A method for rapidly assessing landslide hazard—taking the landslide in Yongxing town, Mingshan area as an example. Frontiers in Earth Science, 12: 1429346. DOI: 10.3389/feart.2024.1429346
• Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.X., Chen, W. 2018. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Science of the total environment, 625: 575-588. DOI: 10.1016/j.scitotenv.2017.12.256
• Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J., Sattarian, A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11: 909-926. DOI: 10.1007/s13762-013-0464-0
• Kanungo, D.P., Arora, M.K., Sarkar, S., Gupta, R.P. 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering geology, 85(3-4): 347-366. DOI: 10.1016/j.enggeo.2006.03.004
• Kerekes, A.H., Poszet, S.L., Andrea, G.Á.L. 2018. Landslide susceptibility assessment using the maximum entropy model in a sector of the Cluj–Napoca Municipality, Romania. Revista de geomorfologie, 20(1): 130-146. DOI: 10.21094/rg.2018.039
• Khan, H., Shafique, M., Khan, M.A., Bacha, M.A., Shah, S.U., Calligaris, C. 2019. Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 22(1): 11-24. DOI: 10.1016/j.ejrs.2018.03.004
• Lee, S., Choi, J. 2004. Landslide susceptibility mapping using GIS and the weight-of-evidence model. International Journal of Geographical Information Science, 18(8): 789-814.
• Lee, S., Talib, J.A. 2005. Probabilistic landslide susceptibility and factor effect analysis. Environmental geology, 47: 982-990. DOI: 10.1007/s00254-005-1228-z
• Lee, S., Pradhan, B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1): 33-41. DOI: 10.1007/s10346-006-0047-y
• Li, B., Wang, N., Chen, J. 2021. GIS‐Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China. Advances in Civil Engineering, 2021(1): 4758062. DOI: 10.1155/2021/4758062
• Li, L., Lan, H., Guo, C., Zhang, Y., Li, Q., Wu, Y. 2017. A modified frequency ratio method for landslide susceptibility assessment. Landslides, 14: 727-741.
• Mandal, S.P., Chakrabarty, A., Maity, P. 2018. Comparative evaluation of information value and frequency ratio in landslide susceptibility analysis along national highways of Sikkim Himalaya. Spatial Information Research, 26: 127-141. DOI: 10.1007/s41324-017-0160-0
• Mansour, Z., Yanick, T., Aissa, S., Soraya, R., Abderahmane, H., Abdelkader, A., ... Kacem, M. 2021. The susceptibility analysis of landslide using bivariate and multivariate modeling techniques in western Algeria: case of Fergoug watershed (Beni-Chougrane Mountains). Arabian Journal of Geosciences, 14: 1-19. DOI: 10.1007/s12517-021-07919-1
• Melese, T., Belay, T., Andemo, A. 2022. Application of analytical hierarchal process, frequency ratio, and Shannon entropy approaches for landslide susceptibility mapping using geospatial technology: The case of Dejen district, Ethiopia. Arabian Journal of Geosciences, 15(5): 424. DOI: 10.1007/s12517-022-09672-5
• Mersha, T., Meten, M. 2020. GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenvironmental disasters, 7: 1-22. DOI: 10.1186/s40677-020-00155-x
• Oh, H. J., Lee, S., Hong, S.M. 2017. Landslide susceptibility assessment using frequency ratio technique with iterative random sampling. Journal of Sensors, 2017(1): 3730913. DOI: 10.1155/2017/ 3730913
• Pal, S.C., Chowdhuri, I. 2019. GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Applied Sciences, 1: 416. DOI: 10.1007/s42452-019-0422-7
• Panchal, S., Shrivastava, A.K. 2021. A comparative study of frequency ratio, Shannon’s entropy and analytic hierarchy process (AHP) models for landslide susceptibility assessment. ISPRS International Journal of Geo-Information, 10(9): 603. DOI: 10.3390/ijgi10090603
• Pham, B.T., Bui, D.T., Dholakia, M. B., Prakash, I., Pham, H.V., Mehmood, K., Le, H.Q. 2017. A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS. Geomatics, Natural Hazards and Risk, 8(2): 649-671. DOI: 10.1080/19475705.2016.1255667
• Pradhan, B. 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51: 350-365.
• Rasyid, A.R., Bhandary, N.P., Yatabe, R. 2016. Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters, 3: 1-16. DOI: 10.1186/s40677-016-0053-x
• Regmi, N.R., Giardino, J.R., Vitek, J.D. 2010. Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115(1-2): 172-187. DOI: 10.1016/j.geomorph.2009.10.002
• Regmi, A.D., Devkota, K.C., Yoshida, K., Pradhan, B., Pourghasemi, H.R., Kumamoto, T., Akgun, A. 2014a. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, 7: 725-742. DOI: 10.1007/s12517-012-0807-z
• Regmi, A. D., Yoshida, K., Pourghasemi, H. R., DhitaL, M. R., Pradhan, B. 2014b. Landslide susceptibility mapping along Bhalubang—Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. Journal of Mountain Science, 11: 1266-1285. DOI: 10.1007/s11629-013-2847-6
• Sadisun, I.A., Telaumbanua, J.A., Kartiko, R.D., Dinata, I.A. 2021. Weight of evidence method for landslide susceptibility mapping in sigi biromaru, central sulawesi. In IOP Conference Series: Earth and Environmental Science, 830(1): 012029. DOI: 10.1088/1755-1315/830/1/012029
• Saha, S., Saha, A., Hembram, T.K., Pradhan, B., Alamri, A.M. 2020. Evaluating the performance of individual and novel ensemble of machine learning and statistical models for landslide susceptibility assessment at Rudraprayag District of Garhwal Himalaya. Applied Sciences, 10(11): 3772.
• Sarkar, D., Saha, S., Mondal, P. 2022. GIS-based frequency ratio and Shannon's entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India. International Journal of Environmental Science and Technology, 19(9): 8911-8932. DOI: 10.1007/s13762-021-03627-1
• Shu, H., Guo, Z., Qi, S., Song, D., Pourghasemi, H.R., Ma, J. 2021. Integrating landslide typology with weighted frequency ratio model for landslide susceptibility mapping: A case study from Lanzhou city of northwestern China. Remote Sensing, 13(18): 3623. DOI: 10.3390/rs13183623
• Solaimani, K., Mousavi, S.Z., Kavian, A. 2013. Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arabian Journal of Geosciences, 6: 2557-2569. DOI: 10.1007/s12517-012-0526-5
• Song, K.Y., Oh, H. J., Choi, J., Park, I., Lee, C., Lee, S. 2012. Prediction of landslides using ASTER imagery and data mining models. Advances in Space Research, 49(5): 978-993. DOI: 10.1016/j.asr.2011.11.035
• Subiyantoro, A., Van Westen, C.J., Den Bout, B.V., Yuniawan, R.A., Mulyana, A.R. 2022. Semi-automatic landslide detection using google earth engine, a case study in poi village, central sulawesi. In 2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) (pp. 1-4). IEEE. DOI: 10.1109/ ICARES56907.2022.9993507
• Suni, M.A., Muis, H., Arianingsih, I. 2023. Spatial Modeling of Changes in Land Cover of Limited Production Forests in Kulawi Subdistrict Sigi Regency Central Sulawesi Province. Geomatika, 29(1): 55-66.
• Thapa, D., Bhandari, B.P. 2019. GIS-Based frequency ratio method for identification of potential landslide susceptible area in the Siwalik zone of Chatara-Barahakshetra section, Nepal. Open Journal of Geology, 9(12): 873. DOI: 10.4236/ojg.2019.912096
• Tien Bui, D., Tuan, T.A., Klempe, H., Pradhan, B., Revhaug, I. 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13: 361-378.
• Trigila, A., Iadanza, C., Esposito, C., Scarascia-Mugnozza, G. 2015. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology, 249: 119-136. DOI: 10.1016/j.geomorph.2015.06.001
• Van Westen, C.J., Van Asch, T.W., Soeters, R. 2006. Landslide hazard and risk zonation—why is it still so difficult? Bulletin of Engineering geology and the Environment, 65: 167-184.
• Wang, L.J., Guo, M., Sawada, K., Lin, J., Zhang, J. 2016. A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosciences Journal, 20: 117-136. DOI: 10.1007/s12303-015-0026-1
• Watkinson, I.M., Hall, R. 2019. Impact of communal irrigation on the 2018 Palu earthquake-triggered landslides. Nature Geoscience, 12(11): 940-945. DOI: 10.1038/s41561-019-0448-x
• Yilmaz, I. 2010. Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environmental Earth Sciences, 61(4): 821-836. DOI: 10.1007/s12665-009-0394-9
• Yuvaraj, R.M., Dolui, B. 2021. Statistical and machine intelligence based model for landslide susceptibility mapping of Nilgiri district in India. Environmental Challenges, 5: 100211. DOI: 10.1016/j.envc.2021.100211
• Zhang, Y.X., Lan, H.X., Li, L.P., Wu, Y.M., Chen, J.H., Tian, N.M. 2020. Optimizing the frequency ratio method for landslide susceptibility assessment: A case study of the Caiyuan Basin in the southeast mountainous area of China. Journal of Mountain Science, 17(2): 340-357. DOI: 10.1007/s11629-019-5702-6
• Zhang, J., Qian, J., Lu, Y., Li, X., Song, Z. 2024. Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China. Sustainability, 16(16): 6803. DOI: 10.3390/su16166803