CFP last date
22 April 2024
Reseach Article

Landslide Hazard Maplies using Anbalagan Method and TOPSIS

by Muh Joko Umbaran H. B., R. Rizal Isnanto, Oky Dwi Nurhayati
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 52
Year of Publication: 2018
Authors: Muh Joko Umbaran H. B., R. Rizal Isnanto, Oky Dwi Nurhayati
10.5120/ijca2018917376

Muh Joko Umbaran H. B., R. Rizal Isnanto, Oky Dwi Nurhayati . Landslide Hazard Maplies using Anbalagan Method and TOPSIS. International Journal of Computer Applications. 180, 52 ( Jun 2018), 42-46. DOI=10.5120/ijca2018917376

@article{ 10.5120/ijca2018917376,
author = { Muh Joko Umbaran H. B., R. Rizal Isnanto, Oky Dwi Nurhayati },
title = { Landslide Hazard Maplies using Anbalagan Method and TOPSIS },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2018 },
volume = { 180 },
number = { 52 },
month = { Jun },
year = { 2018 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number52/29597-2018917376/ },
doi = { 10.5120/ijca2018917376 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:04:21.523396+05:30
%A Muh Joko Umbaran H. B.
%A R. Rizal Isnanto
%A Oky Dwi Nurhayati
%T Landslide Hazard Maplies using Anbalagan Method and TOPSIS
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 52
%P 42-46
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Landslide disaster is the biggest disaster that can cause many casualties. to anticipate the lending land disaster that often occur in Indonesia made a public web bebasis application that can inform the area prone to landslides. The problem often faced is the identification of landslide-prone areas so that when landslide disaster occurs there are still many casualties. The purpose of this application is to determine the location of landslide-prone areas by using Anbalagan method and ranking based on TOPSIS as well as providing information to the public about areas prone to landslides. The method used in this research is anbalgan method with tehkni overlay and ranking method for TOPSIS. Making this Application using PHP programming language (Hypertext Prepocessor) and MySQL database. The results of this study is a public web that is useful to provide information to the public about avalanches that are prone to landslides..

References
  1. Aronica., Biondi., Brigandì., Cascone., Lanza, R., (2012), Assessment and mapping of debris-flow risk in a small catchment in eastern Sicily through integrated numerical simulations and GIS. Journal of Physics and Chemistry of the Earth, 49, 52–63.
  2. Bozorgi, A., Asvadi, S., (2015), A prioritization model for locating relief logistic centers using analytic hierarchy process with interval comparison matrix. Knowledge-Based Systems, 86, 173–181.
  3. J. Chu and Y. Su, “The Application of TOPSIS Method in Selecting Fixed Seismic Shelter for Evacuation in Cities,” Syst. Eng. Procedia, vol. 3, no. 2011, pp. 391–397, 2012.
  4. Coutinho, R., Simao., Antunes., (2011), A GIS-based multicriteria spatial decision support system for planning urban infrastructures. Journal of Decision Support Systems, 51, 720–726.
  5. Perpina., dan Pérez, N., (2013), Multicriteria assessment in GIS environments for siting biomass plants. Land Use Policy, Journal of Renewable and Sustainable Energy Reviews, 31, 326–335.
  6. Ameri, H. R. Pourghasemi, and A. Cerda, “Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models,” Sci. Total Environ., vol. 613–614, pp. 1385–1400, 2018.
  7. Hamza., dan Raghuvanshi., (2017), GIS based landslide hazard evaluation and zonation a case from Jeldu District, Central Ethiopia. Journal of King Saud University - Science, vol. 29, 151–165.
  8. Jianyu, C., Youpo, S., (2012),"The application of TOPSIS method in selecting fixed seismic shelter for evacuation in cities",Science Direct.
  9. Rossi, P., Amadio, R., dan Soliani., (2008), Coupling indicators of ecological value and ecological sensitivity with indicators of demographic pressure in the demarcation of new areas to be protected: The case of the Oltrep Pavese and the Ligurian-Emilian Apennine area (Italy). Journal of Landscape and Urban Planning, 85, 12–26.
  10. Turkey, C., Eren, O., Mehmet, E., Mehmet, K., (2016)., “GIS-based Fuzzy MCDA Approach for Siting Refugee Camp: A Case Study for Southeastern” International Journal of Disaster Risk Reduction.
  11. Zyoud, S.H., Fuchs, H.D., (2017), A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, Journal of Renewable and Sustainable Energy Reviews, 78, 158–181.
  12. W. Chen et al., “GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method,” Catena, vol. 164, no. April 2017, pp. 135–149, 2018.
  13. T. K. Raghuvanshi, L. Negassa, and P. M. Kala, “GIS based Grid overlay method versus modeling approach - A comparative study for landslide hazard zonation (LHZ) in Meta Robi District of West Showa Zone in Ethiopia,” Egypt. J. Remote Sens. Sp. Sci., vol. 18, no. 2, pp. 235–250, 2015.
  14. S. Chauhan, M. Sharma, M. K. Arora, and N. K. Gupta, “Landslide susceptibility zonation through ratings derived from artificial neural network,” Int. J. Appl. Earth Obs. Geoinf., vol. 12, no. 5, pp. 340–350, 2010.
  15. M. Torkashvand, A. Irani, and J. Sorur, “The preparation of landslide map by Landslide Numerical Risk Factor (LNRF) model and Geographic Information System (GIS),” Egypt. J. Remote Sens. Sp. Sci., vol. 17, no. 2, pp. 159–170, 2014.
  16. P. Reichenbach, M. Rossi, B. D. Malamud, M. Mihir, and F. Guzzetti, “A review of statistically-based landslide susceptibility models,” Earth-Science Rev., vol. 180, no. March, pp. 60–91, 2018.
  17. T. Hamza and T. K. Raghuvanshi, “GIS based landslide hazard evaluation and zonation – A case from Jeldu District, Central Ethiopia,” J. King Saud Univ. - Sci., vol. 29, no. 2, pp. 151–165, 2017.
  18. W. Chen et al., “GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method,” Catena, vol. 164, no. April 2017, pp. 135–149, 2018.
  19. S. Chauhan, M. Sharma, M. K. Arora, and N. K. Gupta, “Landslide susceptibility zonation through ratings derived from artificial neural network,” Int. J. Appl. Earth Obs. Geoinf., vol. 12, no. 5, pp. 340–350, 2010.
  20. M. Torkashvand, A. Irani, and J. Sorur, “The preparation of landslide map by Landslide Numerical Risk Factor (LNRF) model and Geographic Information System (GIS),” Egypt. J. Remote Sens. Sp. Sci., vol. 17, no. 2, pp. 159–170, 2014S. Kaboodvandpour and L. K. P. Leung, “Modelling density thresholds for managing mouse damage to maturing wheat,” Crop Prot., vol. 42, pp. 134–140, 2012.
  21. Y. G. Lou, G. R. Zhang, W. Q. Zhang, Y. Hu, and J. Zhang, “Reprint of: Biological control of rice insect pests in China,” Biol. Control, vol. 68, no. 1, pp. 103–116, 2014.
  22. Walters and Q. Cai, “Investigating the Use of Holt-Winters Time Series Model for Forecasting Population at the State and Sub-State Levels,” J. Demogr. Work. Sect., vol. 2, pp. 7–8, 2008.
  23. Ganatra, Y. P. Kosta, G. Panchal, and C. Gajjar, “Initial Classification Through Back Propagation In a Neural Network Following Optimization Through GA to Evaluate the Fitness of an Algorithm,” Int. J. Comput. Sci. Inf. Technol., vol. 3, no. 1, pp. 98–116, 2011.
  24. J. Tarigan, Nadia, R. Diedan, and Y. Suryana, “Plate Recognition Using Backpropagation Neural Network and Genetic Algorithm,” Procedia Comput. Sci., vol. 116, pp. 365–372, 2017.
  25. P. Kalekar, “Time series forecasting using Holt-Winters exponential smoothing,” Kanwal Rekhi Sch. Inf. Technol., no. 04329008, pp. 1–13, 2004.
  26. S. . Kosbatwar and S. . Pathan, “Pattern Association for Character Recognition by Back Propagation Algorithm Using Neural Network Approach,” Int. Comput. Sci. Eng. Surv., vol. 3, no. 1, pp. 127–34, 2012.
  27. R. Tripathi et al., “Forecasting Rice Productivity and Production of Odisha , India , Using Autoregressive Integrated Moving Average Models,” Adv. Agric., vol. 1, pp. 1–9, 2014.
  28. Chatfield and M. Yar, “Holt-Winters Forecasting: Some Practical Issues,” Source J. R. Stat. Soc. Ser. D (The Stat. J. R. Stat. Soc. Ser. D Stat., vol. 37, no. 2, pp. 129–140, 1988.
  29. L. Ferbar Tratar and E. Strmčnik, “The comparison of Holt-Winters method and Multiple regression method: A case study,” Energy, vol. 109, pp. 266–276, 2016.
  30. N. A. Elmunim, M. Abdullah, A. M. Hasbi, and S. A. Bahari, “Comparison of GPS TEC variations with Holt-Winter method and IRI-2012 over Langkawi, Malaysia,” Adv. Sp. Res., vol. 60, no. 2, pp. 276–285, 2017.
  31. G. Tirkeş, C. Güray, and N. Çelebi, “Demand forecasting: a comparison between the Holt-Winters, trend analysis and decomposition models,” Teh. Vjesn. - Tech. Gaz., vol. 24, no. Supplement 2, pp. 503–509, 2017.
  32. U. Khair, H. Fahmi, S. Al Hakim, and R. Rahim, “Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error,” J. Phys. Conf. Ser., vol. 930, no. 1, pp. 1–6, 2017.
  33. W. N. Networks, W. Now, H. Are, and N. Networks, Fundamental of Neural Network:: Architecture, Algorithm, and Application. New Jarsey: Prentice-Hall, 1994.
  34. T. Baldigara, “Forecasting Tourism Demand in Croatia: A Comparison of Different Extrapolative Methods,” J. Bus. Adm. Res., vol. 2, no. 1, pp. 84–92, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

TOPSIS Method Anbalagan Method Overlay Technique Landslide Web.