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The Compatibility of a GIS Map of Landslide-Prone Areas in Kendari City Southeast Sulawesi with Actual Site Conditions
Andri Estining Sejati(1*), Ahmad Tarmizi Abd Karim(2), Akbar Tanjung(3)(1) Universitas Sembilanbelas November Kolaka
(2) Universiti Tun Hussein Onn Malaysia
(3) Universitas Halu Oleo
(*) Corresponding Author
Abstract
Kendari is the capital of the Indonesian province of Southeast Sulawesi. It is located on mainly the karst hills region with high rainfall and there were numerous human activities on the karst hills. Many landslides have occurred in these areas. Natural and human factors may contribute to the landslide. The purpose of this study was to determine whether the present GIS map of landslide-prone areas was in agreement or compatible when compared to the actual site conditions in Kendari City. This research is mainly a regional survey. Data was collected through direct interview and observation at the sites. Data were analyzed quantitatively with percentages. The results showed that 87.4% of the area in Kendari City as shown in the map of landslide-prone distribution using GIS was included in the low risk or slightly vulnerable category. The category of landslide-prone areas was divided into; Very low risk, Low, Medium, High risk and Very high risk which represents the less vulnerable areas to the very vulnerable areas. The level of compatibility of landslide-prone maps in Kendari City, when compared with actual site conditions, reaches 75%. This shows that the map of the GIS spatial analysis can be used as a guide in mapping the level of landslide vulnerability in Kendari City. Landslide-prone map may be used as a guideline for engineers, designers, planners, and city officials in planning to reduce the risk of potential disaster.
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References
Amaluddin, L. O., Sejati, A. E., Ihsan, F. A., & Mutiana, M. (2019). Identification of huntete beach tourism object in Kulati village East Tomia sub-district Wakatobi regency. Geosfera Indonesia. Vol. 3, No. 3, pp. 43–49. https://doi.org/10.19184/geosi.v3i3.8688
Atmojo, S. E., Rusilowati, A., Dwiningrum, S. I. A., & Skotnicka, M. (2018). The reconstruction of disaster knowledge through thematic learning of science, environment, technology, and society integrated with local wisdom. Jurnal Pendidikan IPA Indonesia. Vol 7, No. 2, pp. 204–213. https://doi.org/10.15294/jpii.v7i2.14273
Bartelletti, C., Giannecchini, R., D’Amato Avanzi, G., Galanti, Y., & Mazzali, A. (2017). The influence of geological-morphological and land use settings on shallow landslides in the Pogliaschina T. Basin (northern apennines, Italy). Journal of Maps. Vol. 13., No. 2, pp. 142–152. https://doi.org/10.1080/17445647.2017.1279082
BNPB. (2019). Data Informasi Bencana Indonesia (DIBI) 2019. Retrieved from https://bnpb.cloud/dibi/laporan5a
Dawood, H. (2011). Theories of interval arithmetic: mathematical foundations and applications. Lambert Academic.
Dawood, H., & Dawood, Y. (2019). Parametric Intervals: More Reliable or Foundationally Problematic? October. Retrieved from https://doi.org/10.5281/zenodo.3234186
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. In Earth-Science Reviews. Vol. 112, No. 1–2, pp. 42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
Hadmoko, D. S., Lavigne, F., Sartohadi, J., Gomez, C., & Daryono, D. (2017). Spatio-Temporal Distribution of Landslides in Java and the Triggering Factors. Forum Geografi. Vol. 31, No. 1, pp. 1-15. https://doi.org/10.23917/forgeo.v31i1.3790
Hartono, R., & Nasikh. (2017). Applying remote sensing technology and geographic information system in Batu, East Java. Indonesian Journal of Geography. Vol. 49, No. 2, pp. 118–124. https://doi.org/10.22146/ijg.12842
Hilman, I., & Sunaedi, N. (2018). Revitalization of local wisdom for the environmental education. Geosfera Indonesia. Vol 2, No. 1, pp. 19. https://doi.org/10.19184/geosi.v2i1.7459
Huang, A. Bin, Lee, J. T., Ho, Y. Te, Chiu, Y. F., & Cheng, S. Y. (2012). Stability monitoring of rainfall-induced deep landslides through pore pressure profile measurements. Soils and Foundations. Vol. 52, No. 4, pp. 737–747. https://doi.org/10.1016/j.sandf.2012.07.013
Juang, C. S., Stanley, T. A., & Kirschbaum, D. B. (2019). Using citizen science to expand the global map of landslides: Introducing the cooperative open online landslide repository (coolr). PLOS ONE. Vo. 14, No. 7, pp. 1-28. https://doi.org/10.1371/journal.pone.0218657
Kovács, I. P., Czigány, S., Dobre, B., Fábián, S., Sobucki, M., Varga, G., & Bugya, T. (2019). A field survey–based method to characterise landslide development: A case study at the high bluff of the
Danube, South-central Hungary. Landslides. Vol. 16, No. 8, pp. 1567–1581. https://doi.org/10.1007/s10346-019-01205-8
Kurnianto, F. A., Apriyanto, B., Nurdin, E. A., Ikhsan, F. A., & Fauzi, R. Bin. (2018). Geographic information system (gis) application to analyze landslide prone disaster zone in Jember regency East Java. Geosfera Indonesia. Vol. 2, No. 1, pp. 45-53. https://doi.org/10.19184/geosi.v2i1.7524
Mueller, C. S., Briggs, R. W., Wesson, R. L., & Petersen, M. D. (2015). Updating the usgs seismic hazard maps for Alaska. Quaternary Science Reviews. Vol. 113, pp. 39–47. https://doi.org/10.1016/j.quascirev.2014.10.006
Najib, N., Karnawati, D., & Sudarno, I. (2015). Influence of geological condition towards slope stability on landslide: case study in Tengklik village, Tawangmangu district, Karanganyar regency, Central Java Province, Indonesia. Journal of Applied Geology. Vol 2, No. 3. pp. 217-224 https://doi.org/10.22146/jag.7265
Nurdin, P. F., & Kubota, T. (2018). Gis-based landslide susceptibility assessment and factor effect analysis by certainty factor in upstream of Jeneberang river, Indonesia. Geoplanning: Journal of Geomatics and Planning. Vol. 5, No. 1, pp. 75-90. https://doi.org/10.14710/geoplanning.5.1.75-90
Nursalam, L. O., Arisona, A., Ramli, R., Harudu, L., Kasmiati, S., Harianto, E., Ikhsan, F. A., & Sejati, A. E. (2019). Mapping of subsurface geological structure and land cover using microgravity techniques for geography and geophysic surveys: A case study of Maluri Park, Malaysia. Geosfera Indonesia, 4(3), 280–290. https://doi.org/10.19184/geosi.v4i3.13738
Purba, J. O., Subiyanto, S., & Sasmito, B. (2014). Pembuatan peta zona rawan tanah longsor di kota semarang dengan melakukan pembobotan parameter. Jurnal Geodesi Undip, 3(2), 40–52. Retrieved from https://ejournal3.undip.ac.id/index.php/geodesi/article/view/5205
Qarinur, M. (2015). Landslide runout distance prediction based on mechanism and cause of soil or rock mass movement. Journal of the Civil Engineering Forum. Vol 1, No. 1, pp. 29-36. https://doi.org/10.22146/jcef.22728
Rahman, A. ur, Khan, A. N., & Collins, A. E. (2014). Analysis of landslide causes and associated damages in the Kashmir Himalayas of Pakistan. Natural Hazards. Vol. 71, No. 1, pp. 803–821. https://doi.org/10.1007/s11069-013-0918-1
Ruiz-Villanueva, V., Allen, S., Arora, M., Goel, N. K., & Stoffel, M. (2017). Recent catastrophic landslide lake outburst floods in the Himalayan mountain range. Progress in Physical Geography: Earth and Environment. Vol. 41, No. 1, pp. 3–28. https://doi.org/10.1177/0309133316658614
Shahabi, H., & Hashim, M. (2015). Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Scientific Reports. Vol. 5, No. 1, pp. 1–15. https://doi.org/10.1038/srep09899
Suwarno, S., Sartohadi, J., Sunarto, S., & Sadharto, D. (2016). A Review of Society’s Behaviour Towards Land Management of Susceptible Area to Landslide in Pekuncen, Banyumas. Forum Geografi. Vol 30, No. 1, pp. 99-105. https://doi.org/10.23917/forgeo.v30i1.1704
Yi, X., Chen, C., Liu, L., Huang, L., Chen, J., & Zhou, W. (2017). Prediction of GNSS TEC based on improved BP neural network. Fifth Recent Advances in Quantitative Remote Sensing, pp. 443-448
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