ANALYSIS OF RAINY DAYS AND RAINFALL TO LANDSLIDE OCCURRENCE USING LOGISTIC REGRESSION IN PONOROGO EAST JAVA

Geosfera Indonesia Pub Date : 2018-08-28 DOI:10.19184/GEOSI.V3I2.8230

D. Muriyatmoko, Sisca Mayang Phuspa

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The data statistically analyzed in simultaneous proves that contribution of rainy days and rainfall to landslide were included adequate correlation (Nagelkerke R Square = 25.4 % and Cox & Snell R Square = 36.9 %) and in partial test proves that rainy days have significant impact (sig. = 0.024) and rainfall does not significant impact (sig. = 0.291) (α = 0.05) to landslide occurrence in Ponorogo regency. The rainy days per month were abled applied to predict for possible landslide elsewhere. \nKeywords: rainy days, rainfall, landslide, Ponorogo, logistic regression \n \nReferences \nAditian, A., Kubota, T., & Shinohara, Y. (2018). Geomorphology Comparison of GIS-based landslide susceptibility models using frequency ratio , logistic regression , and arti fi cial neural network in a tertiary region of Ambon , Indonesia. Geomorphology Journal, 318, 101–111. https://doi.org/10.1016/j.geomorph.2018.06.006 \nAgresti, A. (1996). An Introduction to Categorical Data Analysis. Wiley. https://doi.org/10.1002/0470114754 \nAmri, M. R., Yulianti, G., Yunus, R., Wiguna, S., Adi, A. W., Ichwana, A. N., … Septian, R. T. (2016). Risiko Bencana Indonesia. Jakarta: Badan Nasional Penanggulangan Bencana. \nBadan Nasional Penanggulangan Bencana. (2018). Data Pantauan Bencana. Retrieved June 21, 2018, from http://geospasial.bnpb.go.id/pantauanbencana/data/index.php \nBadan Perencanaan Pembangunan Daerah Ponorogo. (2013). Pembangunan Ponorogo Dalam Angka 2013. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication/ \nBadan Perencanaan Pembangunan Daerah Ponorogo. (2014). Pembangunan Ponorogo Dalam Angka 2014. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication \nBadan Pusat Statistik Kabupaten Ponorogo. (2015a). Ponorogo Dalam angka 2015. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication \nBadan Pusat Statistik Kabupaten Ponorogo. (2015b). Ponorogo Dalam angka 2017. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication \nBadan Pusat Statistik Kabupaten Ponorogo. (2016). Ponorogo Dalam angka 2016. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication \nChuang, Y. C., & Shiu, Y. S. (2018). Relationship between landslides and mountain development—Integrating geospatial statistics and a new long-term database. Science of the Total Environment Journal, 622–623, 1265–1276. https://doi.org/10.1016/j.scitotenv.2017.12.039 \nChuang, Y., & Shiu, Y. (2018). Science of the Total Environment Relationship between landslides and mountain development — Integrating geospatial statistics and a new long-term database. Science of the Total Environment Journal, 622–623, 1265–1276. https://doi.org/10.1016/j.scitotenv.2017.12.039 \nDepartemen Pekerjaan Umum. Pedoman Penataan Ruang Kawasan Rawan Bencana Longsor, Pub. L. No. 22 /PRT/M/2007, 148 (2007). Indonesia: Menteri Pekerjaan Umum Republik Indonesia. Retrieved from landspatial.bappenas.go.id/komponen/peraturan/the_file/permen22_2007.pdf%0A \nHosmer, D. W., & Lemeshow, S. (2005). Multiple Logistic Regression. In Applied Logistic Regression (pp. 31–46). Hoboken, NJ, USA: John Wiley & Sons, Inc. https://doi.org/10.1002/0471722146.ch2 \nKementerian Kesehatan Republik Indonesia. (2018). Pusat Krisis Kesehatan Kementerian Kesehatan Republik Indonesia. Retrieved June 11, 2018, from http://pusatkrisis.kemkes.go.id/ \nLin, 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 Journal, 224(May), 62–74. https://doi.org/10.1016/j.enggeo.2017.05.009 \nLogar, J., Turk, G., Marsden, P., & Ambrožič, T. (2017). Prediction of rainfall induced landslide movements by artificial neural networks. Journal of Natural Hazards and Earth System Sciences Discussions, (July), 1–18. https://doi.org/10.5194/nhess-2017-253 \nPaimin, Sukresno, & Pramono, I. B. (2009). Teknik Mitigasi Banjir dan Tanah Longsor. (A. N. Ginting, Ed.). Balikpapan: Tropenbos International Indonesia Programme. Retrieved from www.tropenbos.org \nPourghasemi, H. R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: Which algorithm, which precision? Catena Journal, 162(November), 177–192. https://doi.org/10.1016/j.catena.2017.11.022 \nReed, P., & Wu, Y. (2013). Journal of Fluency Disorders Logistic regression for risk factor modelling in stuttering research ଝ. Journal of Fluency Disorders, 38(2), 88–101. https://doi.org/10.1016/j.jfludis.2012.09.003 \nUbechu, B. O., & Okeke, O. . (2017). Landslide: Causes, Effects and Control. International Journal of Current Multidisciplinary Studies, 3(03), 647–663. \nYuniarta, H., Saido, A. P., & Purwana, Y. M. (2015). Kerawanan Bencana Tanah Longsor Kabupaten Ponorogo. Jurnal Matriks Teknik Sipil, 3(1), 194–201. \n \n \n \n \n \n \n ","PeriodicalId":33276,"journal":{"name":"Geosfera Indonesia","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geosfera Indonesia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.19184/GEOSI.V3I2.8230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

Abstract

Referred to data of Badan Nasional Penanggulangan Bencana (BNPB) and Kementerian Kesehatan Republik Indonesia (Kemenkes RI), almost landslide occurrence in Ponorogo always starts with high-intensity rain. This research aimed to determine simultaneously correlation and partial assessment impact of rainy days every month and monthly rainfall toward landslide occurrence in Ponorogo using logistic regression. The data collection was conducted through Badan Pusat Statistik (BPS) in the book of Ponorogo Regency in Figure on 2012 to 2016. The existing data shows that in sixty months have been twenty-six times landslides occurrence in Ponorogo districts. The data statistically analyzed in simultaneous proves that contribution of rainy days and rainfall to landslide were included adequate correlation (Nagelkerke R Square = 25.4 % and Cox & Snell R Square = 36.9 %) and in partial test proves that rainy days have significant impact (sig. = 0.024) and rainfall does not significant impact (sig. = 0.291) (α = 0.05) to landslide occurrence in Ponorogo regency. The rainy days per month were abled applied to predict for possible landslide elsewhere. Keywords: rainy days, rainfall, landslide, Ponorogo, logistic regression References Aditian, A., Kubota, T., & Shinohara, Y. (2018). Geomorphology Comparison of GIS-based landslide susceptibility models using frequency ratio , logistic regression , and arti fi cial neural network in a tertiary region of Ambon , Indonesia. Geomorphology Journal, 318, 101–111. https://doi.org/10.1016/j.geomorph.2018.06.006 Agresti, A. (1996). An Introduction to Categorical Data Analysis. Wiley. https://doi.org/10.1002/0470114754 Amri, M. R., Yulianti, G., Yunus, R., Wiguna, S., Adi, A. W., Ichwana, A. N., … Septian, R. T. (2016). Risiko Bencana Indonesia. Jakarta: Badan Nasional Penanggulangan Bencana. Badan Nasional Penanggulangan Bencana. (2018). Data Pantauan Bencana. Retrieved June 21, 2018, from http://geospasial.bnpb.go.id/pantauanbencana/data/index.php Badan Perencanaan Pembangunan Daerah Ponorogo. (2013). Pembangunan Ponorogo Dalam Angka 2013. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication/ Badan Perencanaan Pembangunan Daerah Ponorogo. (2014). Pembangunan Ponorogo Dalam Angka 2014. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication Badan Pusat Statistik Kabupaten Ponorogo. (2015a). Ponorogo Dalam angka 2015. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication Badan Pusat Statistik Kabupaten Ponorogo. (2015b). Ponorogo Dalam angka 2017. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication Badan Pusat Statistik Kabupaten Ponorogo. (2016). Ponorogo Dalam angka 2016. Ponorogo. Retrieved from https://ponorogokab.bps.go.id/publication Chuang, Y. C., & Shiu, Y. S. (2018). Relationship between landslides and mountain development—Integrating geospatial statistics and a new long-term database. Science of the Total Environment Journal, 622–623, 1265–1276. https://doi.org/10.1016/j.scitotenv.2017.12.039 Chuang, Y., & Shiu, Y. (2018). Science of the Total Environment Relationship between landslides and mountain development — Integrating geospatial statistics and a new long-term database. Science of the Total Environment Journal, 622–623, 1265–1276. https://doi.org/10.1016/j.scitotenv.2017.12.039 Departemen Pekerjaan Umum. Pedoman Penataan Ruang Kawasan Rawan Bencana Longsor, Pub. L. No. 22 /PRT/M/2007, 148 (2007). Indonesia: Menteri Pekerjaan Umum Republik Indonesia. Retrieved from landspatial.bappenas.go.id/komponen/peraturan/the_file/permen22_2007.pdf%0A Hosmer, D. W., & Lemeshow, S. (2005). Multiple Logistic Regression. In Applied Logistic Regression (pp. 31–46). Hoboken, NJ, USA: John Wiley & Sons, Inc. https://doi.org/10.1002/0471722146.ch2 Kementerian Kesehatan Republik Indonesia. (2018). Pusat Krisis Kesehatan Kementerian Kesehatan Republik Indonesia. Retrieved June 11, 2018, from http://pusatkrisis.kemkes.go.id/ 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 Journal, 224(May), 62–74. https://doi.org/10.1016/j.enggeo.2017.05.009 Logar, J., Turk, G., Marsden, P., & Ambrožič, T. (2017). Prediction of rainfall induced landslide movements by artificial neural networks. Journal of Natural Hazards and Earth System Sciences Discussions, (July), 1–18. https://doi.org/10.5194/nhess-2017-253 Paimin, Sukresno, & Pramono, I. B. (2009). Teknik Mitigasi Banjir dan Tanah Longsor. (A. N. Ginting, Ed.). Balikpapan: Tropenbos International Indonesia Programme. Retrieved from www.tropenbos.org Pourghasemi, H. R., & Rahmati, O. (2018). Prediction of the landslide susceptibility: Which algorithm, which precision? Catena Journal, 162(November), 177–192. https://doi.org/10.1016/j.catena.2017.11.022 Reed, P., & Wu, Y. (2013). Journal of Fluency Disorders Logistic regression for risk factor modelling in stuttering research ଝ. Journal of Fluency Disorders, 38(2), 88–101. https://doi.org/10.1016/j.jfludis.2012.09.003 Ubechu, B. O., & Okeke, O. . (2017). Landslide: Causes, Effects and Control. International Journal of Current Multidisciplinary Studies, 3(03), 647–663. Yuniarta, H., Saido, A. P., & Purwana, Y. M. (2015). Kerawanan Bencana Tanah Longsor Kabupaten Ponorogo. Jurnal Matriks Teknik Sipil, 3(1), 194–201.

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利用logistic回归分析东爪哇波诺罗戈地区降雨和降雨对滑坡发生的影响

参考印度尼西亚国家公园（BNPB）和印度尼西亚共和国公园（Kemenkes RI）的数据，Ponorogo的滑坡几乎总是从高强度降雨开始的。本研究旨在使用逻辑回归法同时确定波诺罗戈每月降雨天数和月降雨量对滑坡发生的相关性和部分评估影响。数据收集是通过Badan Pusat Statistik（BPS）在2012年至2016年的Ponorogo Regency一书中进行的。现有数据显示，在60个月内，波诺罗戈地区发生了26次山体滑坡。同时统计分析的数据证明，雨天和降雨对滑坡的贡献具有充分的相关性（Nagelkerke R Square=25.4%，Cox&Snell R Square=36.9%），部分测试证明，雨天对滑坡的发生有显著影响（sig.=0.024），降雨对滑坡发生无显著影响（sig.=0.291）（α=0.05）波诺罗戈摄政。每月的降雨天数能够用于预测其他地方可能发生的滑坡。关键词：雨天，降雨，滑坡，Ponorogo，逻辑回归参考文献Aditian，A.，Kubota，T.，&Shinohara，Y.（2018）。使用频率比、逻辑回归和人工神经网络的基于GIS的滑坡易感性模型在印度尼西亚安汶第三纪地区的地貌比较。《地貌学杂志》，318，101–111。https://doi.org/10.1016/j.geomorph.2018.06.006阿格雷斯蒂A.（1996）。分类数据分析导论。威利。https://doi.org/10.1002/0470114754Amri，M.R.，Yulianti，G.，Yunus，R.，Wiguna，S.，Adi，A.W.，Ichwana，A.N.，…Septian，R.T.（2016）。印度尼西亚本卡纳里希科。雅加达：马来西亚国家银行。马来西亚国家银行。（2018）。数据Pantauan Bencana。2018年6月21日检索自http://geospasial.bnpb.go.id/pantauanbencana/data/index.phpPonorogo地区的建设。（2013）。Ponorogo Dalam Angka项目2013年。波诺罗戈。检索自https://ponorogokab.bps.go.id/publication/Ponorogo地区的建设。（2014）。Ponorogo Dalam Angka项目2014年。波诺罗戈。检索自https://ponorogokab.bps.go.id/publicationPonorogo县统计局。（2015a）。Ponorogo Dalam angka 2015。波诺罗戈。检索自https://ponorogokab.bps.go.id/publicationPonorogo县统计局。（2015b）。Ponorogo Dalam angka 2017。波诺罗戈。检索自https://ponorogokab.bps.go.id/publicationPonorogo县统计局。（2016）。Ponorogo Dalam angka 2016。波诺罗戈。检索自https://ponorogokab.bps.go.id/publication庄、萧（2018）。山体滑坡与山区发展之间的关系——整合地理空间统计数据和一个新的长期数据库。《全面环境科学杂志》，622–623，1265–1276。https://doi.org/10.1016/j.scitotenv.2017.12.039庄、萧（2018）。滑坡和山区发展之间的总体环境关系科学——整合地理空间统计和一个新的长期数据库。《全面环境科学杂志》，622–623，1265–1276。https://doi.org/10.1016/j.scitotenv.2017.12.039Pekerjaan Umum部门。Pedoman Penataan Ruang Kawasan Rawan Bencana Longsor，Pub。L.第22/PRT/M/2007/148号（2007年）。印度尼西亚：印度尼西亚共和国外交部长。检索自landspatial.bappenas.go.id/componen/peraturan/the_file/permen22/2007.pdf%0A Hosmer，D.W.和Lemeshow，S.（2005）。多元逻辑回归。在应用逻辑回归中（第31-46页）。美国新泽西州霍博肯：John Wiley&Sons，股份有限公司。https://doi.org/10.1002/0471722146.ch2印度尼西亚共和国卫生部长。（2018）。印度尼西亚共和国卫生部长。2018年6月11日检索自http://pusatkrisis.kemkes.go.id/林、张、黄、何（2017）。使用改进的自组织线性输出图、支持向量机和逻辑回归评估降雨诱发滑坡的易感性。工程地质学杂志，224（5月），62–74。https://doi.org/10.1016/j.enggeo.2017.05.009Logar，J.、Turk，G.、Marsden，P.和Ambrožič，T.（2017）。人工神经网络预测降雨引起的滑坡运动。《自然灾害与地球系统科学讨论杂志》，（7月），1-18。https://doi.org/10.5194/nhess-2017-253Paimin，Sukresno和Pramono，I.B.（2009）。Banjir和Tanah Longsor的技术。（A.N.Ginting，Ed.），《巴厘岛：Tropenbos国际印度尼西亚方案》。检索自www.tropenbos.org Pourghasemi，H.R.和Rahmati，O.（2018）。滑坡易发性预测：哪种算法，哪种精度？Catena期刊，162（11月），177–192。https://doi.org/10.1016/j.catena.2017.11.022Reed，P.和Wu，Y.（2013）。

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Geosfera Indonesia

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16 weeks