Utilization of Frequency Ratio and Logistic Regression Model for Landslide Susceptibility Mapping in Bogor Area
How to cite (IJASEIT) :
The National Disaster Management Agency, “Statistics of landslide event in Bogor area 2005-2020,” BNPB, Mar. 2022. https://dibi.bnpb.go.id/ (accessed Mar. 11, 2023).
D. Ozturk and N. Uzel-Gunini, “Investigation of the effects of hybrid modeling approaches, factor standardization, and categorical mapping on the performance of landslide susceptibility mapping in Van, Turkey,” Nat. Hazards, vol. 114, no. 3, pp. 2571–2604, Dec. 2022, doi: 10.1007/s11069-022-05480-y.
Z. Qin, X. Zhou, M. Li, Y. Tong, and H. Luo, “Landslide Susceptibility Mapping Based on Resampling Method and FR-CNN: A Case Study of Changdu,” Land, vol. 12, no. 6, p. 1213, Jun. 2023, doi: 10.3390/land12061213.
W. Wu, S. Guo, and Z. Shao, “Landslide risk evaluation and its causative factors in typical mountain environment of China: a case study of Yunfu City,” Ecol. Indic., vol. 154, p. 110821, Oct. 2023, doi: 10.1016/j.ecolind.2023.110821.
L. Zhu et al., “Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network,” Sensors, vol. 20, no. 6, p. 1576, Mar. 2020, doi: 10.3390/s20061576.
Center for Volcanology and Geological Hazard Mitigation Geological Agency, “PVMBG Ungkap Pulau Jawa Rawan Longsor,” https://www.krjogja.com/peristiwa/read/492328/pvmbg-ungkap-pulau-jawa-rawan-longsor, 2022. [Online]. Available: https://www.krjogja.com/nasional/1242457473/-pvmbg-ungkap-pulau-jawa-rawan-longsor
Statistics Indonesia, “Statistical Yearbook of Indonesia 2023,” BPS-Statistics Indonesia, 2023. [Online]. Available: https://www.bps.go.id/publication/2023/02/28/18018f9896f09f03580a614b/statistik-indonesia-2023.html
Statistics of Jawa Barat Province, Jawa Barat Province in Figures 2023. 2023. [Online]. Available: https://jabar.bps.go.id/publication/2023/02/28/57231a828abbfdd50a21fe31/provinsi-jawa-barat-dalam-angka-2023.html
A. Addis, “GIS-Based Landslide Susceptibility Mapping Using Frequency Ratio and Shannon Entropy Models in Dejen District, Northwestern Ethiopia,” J. Eng., vol. 2023, pp. 1–14, Feb. 2023, doi: 10.1155/2023/1062388.
G. Berhane et al., “Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial data-sets in the Adwa-Adigrat mountain chains, northern Ethiopia,” J. African Earth Sci., vol. 164, p. 103795, Apr. 2020, doi: 10.1016/j.jafrearsci.2020.103795.
I. Cantarino, M. A. Carrion, V. Martínez-Ibáñez, and E. Gielen, “Improving Landslide Susceptibility Assessment through Frequency Ratio and Classification Methods—Case Study of Valencia Region (Spain),” Appl. Sci., vol. 13, no. 8, p. 5146, Apr. 2023, doi: 10.3390/app13085146.
W. He et al., “Landslide Susceptibility Evaluation of Machine Learning Based on Information Volume and Frequency Ratio: A Case Study of Weixin County, China,” Sensors, vol. 23, no. 5, p. 2549, Feb. 2023, doi: 10.3390/s23052549.
B. Li, N. Wang, and J. Chen, “GIS-Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China,” Adv. Civ. Eng., vol. 2021, pp. 1–14, Jun. 2021, doi: 10.1155/2021/4758062.
Y. Liu, A. Yuan, Z. Bai, and J. Zhu, “GIS-based landslide susceptibility mapping using frequency ratio and index of entropy models for She County of Anhui Province, China,” Appl. Rheol., vol. 32, no. 1, pp. 22–33, Jun. 2022, doi: 10.1515/arh-2022-0122.
I. Sonker, J. N. Tripathi, and Swarnim, “Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio method in Sikkim Himalaya,” Quat. Sci. Adv., vol. 8, p. 100067, Oct. 2022, doi: 10.1016/j.qsa.2022.100067.
S. Panchal and A. K. Shrivastava, “Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India,” Ain Shams Eng. J., vol. 13, no. 3, p. 101626, May 2022, doi: 10.1016/J.ASEJ.2021.10.021.
A. H. Alsabhan et al., “Landslide susceptibility assessment in the Himalayan range based along Kasauli – Parwanoo road corridor using weight of evidence, information value, and frequency ratio,” J. King Saud Univ. - Sci., vol. 34, no. 2, p. 101759, Feb. 2022, doi: 10.1016/j.jksus.2021.101759.
A. Es-smairi, B. Elmoutchou, R. A. Mir, A. El Ouazani Touhami, and M. Namous, “Delineation of landslide susceptible zones using Frequency Ratio (FR) and Shannon Entropy (SE) models in northern Rif, Morocco,” Geosystems and Geoenvironment, vol. 2, no. 4, p. 100195, Nov. 2023, doi: 10.1016/j.geogeo.2023.100195.
A. Ozdemir, “A Comparative Study of the Frequency Ratio, Analytical Hierarchy Process, Artificial Neural Networks and Fuzzy Logic Methods for Landslide Susceptibility Mapping: Taşkent (Konya), Turkey,” Geotech. Geol. Eng., vol. 38, no. 4, pp. 4129–4157, Aug. 2020, doi: 10.1007/s10706-020-01284-8.
B. Hafsa, M. S. Chowdhury, and M. N. Rahman, “Landslide susceptibility mapping of Rangamati District of Bangladesh using statistical and machine intelligence model,” Arab. J. Geosci., vol. 15, no. 15, p. 1367, Aug. 2022, doi: 10.1007/s12517-022-10607-3.
A. R. Rasyid, N. P. Bhandary, and R. Yatabe, “Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia,” Geoenvironmental Disasters, vol. 3, no. 1, p. 19, Dec. 2016, doi: 10.1186/s40677-016-0053-x.
R.-X. Tang, E.-C. Yan, T. Wen, X.-M. Yin, and W. Tang, “Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping,” Sustainability, vol. 13, no. 7, p. 3803, Mar. 2021, doi: 10.3390/su13073803.
P. Kainthura and N. Sharma, “Hybrid machine learning approach for landslide prediction, Uttarakhand, India,” Sci. Rep., vol. 12, no. 1, p. 20101, Nov. 2022, doi: 10.1038/s41598-022-22814-9.
F. E. S. Silalahi, Pamela, Y. Arifianti, and F. Hidayat, “Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia,” Geosci. Lett., vol. 6, no. 1, p. 10, Dec. 2019, doi: 10.1186/s40562-019-0140-4.
N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,” Remote Sens. Environ., vol. 202, pp. 18–27, Dec. 2017, doi: 10.1016/j.rse.2017.06.031.
D. N. Melati, Astisiasari, and Trinugroho, “An Assessment of Object-based Classification Compared to Pixel-based Classification in Google Earth Engine Using Random Forest,” in 2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), IEEE, Dec. 2022, pp. 73–78. doi: 10.1109/AGERS56232.2022.10093267.
Y. Xing, J. Yue, Z. Guo, Y. Chen, J. Hu, and A. Travé, “Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China,” Front. Earth Sci., vol. 9, Aug. 2021, doi: 10.3389/feart.2021.722491.
M. Sheng, J. Zhou, X. Chen, Y. Teng, A. Hong, and G. Liu, “Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree Model,” Front. Earth Sci., vol. 10, May 2022, doi: 10.3389/feart.2022.918386.
H. Shu, Z. Guo, S. Qi, D. Song, H. Pourghasemi, and J. Ma, “Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China,” Remote Sens., vol. 13, no. 18, p. 3623, Sep. 2021, doi: 10.3390/rs13183623.
Y. Wang, D. Sun, H. Wen, H. Zhang, and F. Zhang, “Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China),” Int. J. Environ. Res. Public Health, vol. 17, no. 12, p. 4206, Jun. 2020, doi: 10.3390/ijerph17124206.
S. Sukristiyanti, K. Wikantika, I. A. Sadisun, L. F. Yayusman, A. Tohari, and M. H. Zaenal Putra, “Evaluation of Parameter Selection in the Bivariate Statistical-based Landslide Susceptibility Modeling (Case Study: the Citarik Sub-watershed, Indonesia),” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 1, p. 244, Jan. 2022, doi: 10.18517/ijaseit.12.1.14737.
A. Wubalem, “Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia,” Geoenvironmental Disasters, vol. 8, no. 1, p. 1, Dec. 2021, doi: 10.1186/s40677-020-00170-y.
H. Wang, L. Zhang, K. Yin, H. Luo, and J. Li, “Landslide identification using machine learning,” Geosci. Front., vol. 12, no. 1, pp. 351–364, Jan. 2021, doi: 10.1016/j.gsf.2020.02.012.
T. Zhang et al., “Evaluation of different machine learning models and novel deep learning-based algorithm for landslide susceptibility mapping,” Geosci. Lett., vol. 9, no. 1, p. 26, Dec. 2022, doi: 10.1186/s40562-022-00236-9.
X. Zhou, W. Wu, Y. Qin, and X. Fu, “Geoinformation-based landslide susceptibility mapping in subtropical area,” Sci. Rep., vol. 11, no. 1, p. 24325, Dec. 2021, doi: 10.1038/s41598-021-03743-5.
X. Chen and W. Chen, “GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods,” CATENA, vol. 196, p. 104833, Jan. 2021, doi: 10.1016/j.catena.2020.104833.
L.-L. Liu, C. Yang, F.-M. Huang, and X.-M. Wang, “Landslide susceptibility mapping by attentional factorization machines considering feature interactions,” Geomatics, Nat. Hazards Risk, vol. 12, no. 1, pp. 1837–1861, Jan. 2021, doi: 10.1080/19475705.2021.1950217.
M. Bordbar, H. Aghamohammadi, H. R. Pourghasemi, and Z. Azizi, “Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques,” Sci. Rep., vol. 12, no. 1, p. 1451, Jan. 2022, doi: 10.1038/s41598-022-05364-y.
Q. B. Pham et al., “A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping,” Geomatics, Nat. Hazards Risk, vol. 12, no. 1, pp. 1741–1777, Jan. 2021, doi: 10.1080/19475705.2021.1944330.
F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, no. 85, pp. 2825–2830, 2011, [Online]. Available: http://jmlr.org/papers/v12/pedregosa11a.html
K. Gaidzik and M. T. Ramírez-Herrera, “The importance of input data on landslide susceptibility mapping,” Sci. Rep., vol. 11, no. 1, p. 19334, Sep. 2021, doi: 10.1038/s41598-021-98830-y.
A. Merghadi et al., “Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance,” Earth-Science Rev., vol. 207, p. 103225, Aug. 2020, doi: 10.1016/j.earscirev.2020.103225.
W. Wu et al., “A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau,” Remote Sens., vol. 14, no. 18, p. 4662, Sep. 2022, doi: 10.3390/rs14184662.
J. Dou et al., “Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan,” Landslides, vol. 17, no. 3, pp. 641–658, Mar. 2020, doi: 10.1007/s10346-019-01286-5.
A. Wubalem and M. Meten, “Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia,” SN Appl. Sci., vol. 2, no. 5, p. 807, May 2020, doi: 10.1007/s42452-020-2563-0.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).