Spatial Model of Landslide Hazard in Tarusan Watershed
Keywords:
Key words: hazard, landslide, spatial model
Abstract
Spatial modeling of landslide hazards in the Tarusan watershed is an effort to reduce losses due to landslide disasters. The purpose of this article is; determine the frequency ratio value of each parameter that causes landslides, and perform spatial modeling of landslide hazards using the frequency ratio method. The method used is a quantitative method with a modeling approach to determine the pixel value based on the frequency ratio. The results of the research show that the largest frequency value is found in the land cover parameter in the form of mixed gardens with an FR value of 2, 10, and rainfall with an FR value of 2.06. Thus, the triggering factors for landslides in the Tarusan watershed are changes in land cover and rainfall. The results of landslide hazard modeling in the Tarusan watershed show a high hazard area of 2095.41 ha or 7.39%, a medium hazard area of 4148.73 ha or 14.63%, and a low hazard area of 22117.46 ha or 77.98%.
Key words: hazard, landslide, spatial model
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Reference
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1. Achour, Y., & Pourghasemi, H. R. (2019). How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers. doi:10.1016/j.gsf.2019.10.001
2. Battistini, A., Rosi, A., Segoni, S., Lagomarsino, D., Catani, F., & Casagli, N. (2017). Validation of landslide hazard models using a semantic engine on online news. Applied Geography, 82, 59–65. doi:10.1016/j.apgeog.2017.03.003
3. BPBD Pesisir Selatan, (2020). 4 Units of South Coast Residents' Houses Hit by Landslide. Editor, Saturday 15 February, 2020.
4. BPBD West Sumatra, (2019). This is a Natural Disaster Prone Area in West Sumatra, West Sumatra BPBD
5. Carlà, T., Tofani, V., Lombardi, L., Raspini, F., Bianchini, S., Bertolo, D., Casagli, N. (2019). Combination of GNSS, satellite InSAR, and GBInSAR remote sensing monitoring to improve the understanding of a large landslide in high alpine environment. Geomorphology. doi:10.1016/j.geomorph.2019.03.014
6. Casagli, N., Cigna, F., Bianchini, S., Hölbling, D., Füreder, P., Righini, G., Bianchi, M. (2016). Landslide Mapping and Monitoring by Using Radar and Optical Remote Sensing: Examples from the EC-FP7 Project SAFER. Remote Sensing Applications: Society and Environment, 4, 92–108. doi:10.1016/j.rsase.2016.07.001
7. Celia dos Santos Alvalá, R., Carvalho de Assis Dias, M., Saito, S. M., Stenner, C., Franco, C., Amadeu, P., Nobre, C. A. (2019). Mapping Characteristics of at-risk Population to Disasters in the Context of Brazilian Early Warning System. International Journal of Disaster Risk Reduction, 101326. doi:10.1016/j.ijdrr.2019.101326
8. Chen, W., Panahi, M., Tsangaratos, P., Shahabi, H., Ilia, I., Panahi, S., Ahmad, B. B. (2019). Applying Population-Based Evolutionary Algorithms and a Neuro-Fuzzy System for Modeling Landslide Susceptibility. CATENA, 172, 212–231. doi:10.1016/j.catena.2018.08.025
9. Chen, W., Xie, X., Peng, J., Shahabi, H., Hong, H., Bui, D. T., Zhu, A.-X. (2018). GIS-Based Landslide Susceptibility Evaluation Using a Novel Hybrid Integration Approach Of Bivariate Statistical Based Random Forest Method. CATENA, 164, 135–149. doi:10.1016/j.catena.2018.01.012
10. Confuorto, P., Di Martire, D., Infante, D., Novellino, A., Papa, R., Calcaterra, D., & Ramondini, M. (2019). Monitoring of Remedial Works Performance on Landslide-Affected Areas Through Ground- and Satellite-Based Techniques. CATENA, 178, 77–89
11. Depina, I., Oguz, E. A., & Thakur, V. (2020). Novel Bayesian Framework for Calibration of Spatially Distributed Physical-Based Landslide Prediction Models. Computers and Geotechnics, 125, 103660. doi:10.1016/j.compgeo.2020.103660
12. Drazba, M. C., Yan-Richards, A., & Wilkinson, S. (2018). Landslide Hazards in Fiji, Managing the Risk and not the Disaster, a Literature Review. Procedia Engineering, 212, 1334–1338. doi:10.1016/j.proeng.2018.01.172
13. Gadrani, L., Lominadze, G., & Tsitsagi, M. (2018). Assessment of Landuse/Landcover (LULC) Change of Tbilisi and Surrounding Area Using Remote Sensing (RS) and GIS. Annals of Agrarian Science, 16(2), 163–169. doi:10.1016/j.aasci.2018.02.005
14. Hawas Khan, Muhammad Shafique, Muhammad A. Khan, Mian A. Bacha, Safeer U. Shah, Chiara Calligaris. (2019). Landslide Susceptibility Assessment Using Frequency Ratio, a Case Study of Northern Pakistan. The Egyptian Journal of Remote Sensing and Space Sciences, 22 (2019) .do/10.1016/j.ejrs.2018.03.004
15. Kaku, K. (2018). Satellite Remote Sensing for Disaster Management Support: A Holistic and Staged Approach Based on case Studies in Sentinel Asia. International Journal of Disaster Risk Reduction. doi:10.1016/j.ijdrr.2018.09.015
16. Lu, P., Qin, Y., Li, Z., Mondini, A. C., & Casagli, N. (2019). Landslide Mapping From Multi-Sensor Data Through Improved Change Detection-Based Markov Random Field. Remote Sensing of Environment, 231, 111235. doi:10.1016/j.rse.2019.111235
17. Nepal, N., Chen, J., Chen, H., Wang, X., & Sharma, T. P. P. (2019). Assessment of Landslide Susceptibility along the Araniko Highway in Poiqu/Bhote Koshi/Sun Koshi watershed Nepal Himalaya .Progress in Disaster Science 100037.doi:10.1016/j.pdisas.2019.100037
18. Pollock, W., Grant, A., Wartman, J., & Abou-Jaoude, G. (2019). Multimodal Method for Landslide Risk Analysis. MethodsX, 6, 827–836. doi:10.1016/j.mex.2019.04.012
19. Thi Ngo, P. T., Panahi, M., Khosravi, K., Ghorbanzadeh, O., Karimnejad, N., Cerda, A., & Lee, S. (2020). Evaluation of Deep Learning Algorithms for National Scale Landslide Susceptibility Mapping of Iran. Geoscience Frontiers. doi:10.1016/j.gsf.2020.06.013
20. Youssef, A. M., & Pourghasemi, H. R. (2020). Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geoscience Frontiers. doi:10.1016/j.gsf.2020.05.010
Published
2022-12-23
