Journal of Mountain Science

, Volume 16, Issue 2, pp 383–401 | Cite as

Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed, South Sulawesi Indonesia

  • Andang Suryana SomaEmail author
  • Tetsuya Kubota
  • Hideaki Mizuno


Landslide susceptibility maps (LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression (LR) and an artificial neural network (ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR (FSLR), ANN, and their combination (FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher (92.59%) than LR (82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve (AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR -ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.


Optimized causative factor Landslide Logistic Regression Artificial neural network Indonesia Notation 



probability of landslide occurrence


value of landslide causative factor



C1, C2, Cn

coefficient, which measures the contribution of independent factors

CF1, CF2,..., CFn

variation of landslide causative factor


normalized input

xi, xmin, xmax

the actual input data, minimum and maximum input data




weight of input


result of artificial neuron network


activation functions


bias vector 1


weight matrices 1


bias vector 2


weight matrices 2


value of landslide causative factor on test seventh


the final landslide susceptibility map calculated for each pixel


weight of each causative factor


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The authors thank Esri Indonesia for supporting the ARC GIS 10.3 in collaboration with Hasanuddin University, Indonesia.


  1. Akgun A, Sezer EA, Nefeslioglu HA, et al. (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Computers & Geosciences 38(1): 23–34. Google Scholar
  2. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment 58(1): 21–44. Google Scholar
  3. 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. Google Scholar
  4. Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology 81(4): 432–445. Google Scholar
  5. Bahsan E, Liao HJ, Ching J, et al. (2014) Statistics for the calculated safety factors of undrained failure slopes. Engineering Geology 172: 85–94. Google Scholar
  6. Ballabio C, Sterlacchini S (2012) Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy. Mathematical Geosciences 44 (1):47–70. Google Scholar
  7. Cardinali M, Reichenbach P, Guzzetti F, et al (2002) A geomorphological approach to the estimation of landslide hazards and risks in Umbria, Central Italy. Natural Hazards and Earth Syst Science 2 (1-2):57-72. Google Scholar
  8. Chau KT, Chan JE (2005) Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island. Landslides 2(4): 280–290. Google Scholar
  9. Chauhan S, Sharma M, Arora MK, Gupta NK (2010) Landslide susceptibility zonation through ratings derived from artificial neural network. International Journal of Applied Earth Observation and Geoinformation 12(5): 340–350. Google Scholar
  10. Chen W, Pourghasemi HR, Naghibi SA (2018) A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment 77(2): 647–664. Google Scholar
  11. Chung CJF, Fabbri AG (2003) Validation of Spatial Prediction Models for Landslide Hazard Mapping. Natural Hazards 30 (3): 451–472. Google Scholar
  12. Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113: 236–250. Google Scholar
  13. Demicco RV, Klir GJ (2004) Fuzzy Logic in Geology. Elsevier Science (USA), California.Google Scholar
  14. Dou J, Bui DT, Yunus AP, et al. (2015) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS One 10(7): e0133262. Google Scholar
  15. Ercanoglu M, Gokceoglu C (2004) Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology 75(3-4): 229–250. Google Scholar
  16. Ermini L, Catani F, Casagli N (2005) Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology 66(1-4): 327–343. Google Scholar
  17. Glade T (2003) Landslide occurrence as a response to land use change: A review of evidence from New Zealand. Catena 51(3-4): 297–314. Google Scholar
  18. Gorsevski PV, Gessler PE, Boll J, et al (2006) Spatially and temporally distributed modeling of landslide susceptibility. Geomorphology 80(3-4): 178–198. Google Scholar
  19. Hasnawir, Kubota T, Sanchez-castillo L, Soma AS (2015) Root Strength of Understory Vegetation For Erosion Control on Forest Slopes of Kelara Watershed, Indonesia. In: Proceedings of the 2nd Makassar International Conference on Civil Engineering(MICCE 2015) Makassar, Indonesia August 11–12, 2015.Google Scholar
  20. Jamsawang P, Voottipruex P, Boathong P, et al. (2015) Threedimensional numerical investigation on lateral movement and factor of safety of slopes stabilized with deep cement mixing column rows. Engineering Geology 188: 159–167. Google Scholar
  21. Kanungo DP, Arora MK, Sarkar S, Gupta RP (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. Google Scholar
  22. Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology 47(7): 982–990. Google Scholar
  23. Lin G, Chang M, Huang Y, Ho J (2017) Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression. Engineering Geology 224: 62–74. Google Scholar
  24. Meinhardt M, Fink M, Tünschel H (2015) Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology 234: 80–97. Google Scholar
  25. Meten M, Bhandary NP, Yatabe R (2015) GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in central Ethiopia. Journal of Mountain Science 12(6): 1355–1372. Google Scholar
  26. Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology 110 (1-2): 11–20. Google Scholar
  27. Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology (3-4): 171–191. Google Scholar
  28. Pham BT, Tien Bui D, Prakash I, Dholakia MB (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 149 (1): 52–63. Google Scholar
  29. Pourghasemi HR, Jirandeh AG, Pradhan B, et al (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science 122 (2): 349–369. Google Scholar
  30. Pradhan B, Lee S (2010a) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software 25(6): 747–759. Google Scholar
  31. Pradhan B, Lee S, Buchroithner MF (2010b) A GIS-based backpropagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems 34(3): 216–235. Google Scholar
  32. Rasyid AR, Bhandary NP, 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: 19. Google Scholar
  33. Shahabi H, Ahmad BB, Khezri S (2013) Evaluation and comparison of bivariate and multivariate statistical methods for landslide susceptibility mapping (case study: Zab basin). Arabian Journal of Geosciences 6(10): 3885–3907. Google Scholar
  34. Sheela KG, Deepa SN (2013) Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering 2013. Google Scholar
  35. Shirzadi A, Saro L, Hyun Joo O, Chapi K (2012) A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural Hazards 64(2): 1639–1656. Google Scholar
  36. Soma AS, Kubota T (2017a) The Performance of Land Use Change Causative Factor on Landslide Susceptibility Map in Upper Ujung-Loe Watersheds South Sulawesi, Indonesia. Geoplanning: Journal of Geomatics and Planning 4(2): 157–170. Google Scholar
  37. Soma AS, Kubota T (2017b) Land Use Changes on the Slopes and the Implications for the Landslide Occurrences in Ujung- Loe Watersheds South Sulawesi Indonesia. International Journal of Ecology & Development 32(2): 33–42.Google Scholar
  38. Tien Bui D, Nguyen QP, Hoang ND, Klempe H (2017) A novel fuzzy K-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS. Landslides 14(1): 1–17. Google Scholar
  39. Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China. Geomorphology 101(4): 572–582. Google Scholar
  40. Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology 79(3-4):251–266. Google Scholar
  41. Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey). Computers & Geosciences 35(6): 1125–1138. Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of ForestryHasanuddin UniversityMakassarIndonesia
  2. 2.Faculty of AgricultureKyushu UniversityFukuokaJapan

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