IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3043-d1061052.html
   My bibliography  Save this article

A Spatial Model of Landslides with A Micro-Topography and Vegetation Approach for Sustainable Land Management in the Volcanic Area

Author

Listed:
  • Heni Masruroh

    (Postgraduate Program, Universitas Brawijaya, Malang 65145, Indonesia
    Geography Department, University of Malang, Lowokwaru 65141, Indonesia)

  • Soemarno Soemarno

    (Soil Science Department, Faculty of Agriculture, Universitas Brawijaya, Malang 65145, Indonesia)

  • Syahrul Kurniawan

    (Soil Science Department, Faculty of Agriculture, Universitas Brawijaya, Malang 65145, Indonesia)

  • Amin Setyo Leksono

    (Biology Department, Faculty of Mathematics and Natural Science, Universitas Brawijaya, Malang 65145, Indonesia)

Abstract

This study aims to produce a spatial model for sustainable land management in landslide-prone areas, based on exploring non-stationary relationships between landslide events, geomorphological and anthropogenic variables on tropical hillsides, especially in Taji Village, Jabung District, East Java Province, Indonesia. A series of approaches combine in this research, and methods are used to construct independent and dependent variables so that GWR can analyze them to obtain the best model. Transformation of categorical data on microtopography, landform, and land cover variables was carried out. When modelled, landscape metrics can explain landslide events in the study area better than distance metrics with adj. R 2 = 0.75 and AICc = 2526.38. Generally, local coefficient maps for each variable are mapped individually to reveal their relationship with landslide events, but in this study they are integrated to make it more intuitive and less confusing. From this map, it was found that most of the variables that showed the most positive relationship to the occurrence of landslides in the study area were the divergent footslopes. At the same time, the negative one was plantation land. It was concluded that the methodological approach offered and implemented in this study provides significant output results for the spatial analysis of the interaction of landslide events with geomorphological and anthropogenic variables locally, which cannot be explained in a global regression. This study produces a detailed scale landslide-prone conservation model in tropical hill areas and can be reproduced under the same geo-environmental conditions.

Suggested Citation

  • Heni Masruroh & Soemarno Soemarno & Syahrul Kurniawan & Amin Setyo Leksono, 2023. "A Spatial Model of Landslides with A Micro-Topography and Vegetation Approach for Sustainable Land Management in the Volcanic Area," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3043-:d:1061052
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3043/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3043/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamid Reza Pourghasemi & Amiya Gayen & Sungjae Park & Chang-Wook Lee & Saro Lee, 2018. "Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    2. Mason, Robert L. & Gunst, Richard F., 1985. "Selecting principal components in regression," Statistics & Probability Letters, Elsevier, vol. 3(6), pages 299-301, October.
    3. Shuang Liu & Xuefei Li & Long Chen & Qing Zhao & Chaohui Zhao & Xisheng Hu & Jian Li, 2022. "A New Approach to Investigate the Spatially Heterogeneous in the Cooling Effects of Landscape Pattern," Land, MDPI, vol. 11(2), pages 1-21, February.
    4. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elkin Castaño & Santiago Gallón, 2017. "A solution for multicollinearity in stochastic frontier production function models," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 86, pages 9-23, Enero - J.
    2. Castano, Elkin & Gallon, Santiago, 2016. "A solution for multicollinearity in stochastic frontier production function models," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 86, pages 9-23, December.
    3. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    4. Carlos Moreno-Miranda & Hipatia Palacios & Daniele Rama, 2019. "Small-holders perception of sustainability and chain coordination: evidence from Arriba PDO Cocoa in Western Ecuador," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 8(3), December.
    5. Fernandez-Haddad, Zaira & Quiroga, Sonia, 2011. "Adaptation Of Mediterranean Crops To Water Pressure In The Ebro Basin: A Water Efficiency Index," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114358, European Association of Agricultural Economists.
    6. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2015. "Sparse principal component regression with adaptive loading," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 192-203.
    7. Manish Singh Rana & Chandan Mahanta, 2023. "Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 947-969, January.
    8. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.
    9. Hugh L. Christensen, 2015. "Algorithmic arbitrage of open-end funds using variational Bayes," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-38, December.
    10. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    11. Mirza Pasic & Halima Hadziahmetovic & Ismira Ahmovic & Mugdim Pasic, 2023. "Principal Component Regression Modeling and Analysis of PM 10 and Meteorological Parameters in Sarajevo with and without Temperature Inversion," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    12. Cai, Yuezhou & Hanley, Aoife, 2012. "Building BRICS: 2-Stage DEA analysis of R&D efficiency," Kiel Working Papers 1788, Kiel Institute for the World Economy (IfW Kiel).
    13. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
    14. Yanrong Liu & Zhongqiu Meng & Lei Zhu & Di Hu & Handong He, 2023. "Optimizing the Sample Selection of Machine Learning Models for Landslide Susceptibility Prediction Using Information Value Models in the Dabie Mountain Area of Anhui, China," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    15. Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    16. Ranjith Vijayakumar & Ji Yeh Choi & Eun Hwa Jung, 2022. "A Unified Neural Network Framework for Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1503-1528, December.
    17. Mishra, Aditya & Dey, Dipak K. & Chen, Yong & Chen, Kun, 2021. "Generalized co-sparse factor regression," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    18. Anish Agarwal & Keegan Harris & Justin Whitehouse & Zhiwei Steven Wu, 2023. "Adaptive Principal Component Regression with Applications to Panel Data," Papers 2307.01357, arXiv.org, revised Aug 2024.
    19. Santiago Velásquez & Juho Kanniainen & Saku Mäkinen & Jaakko Valli, 2018. "Layoff announcements and intra-day market reactions," Review of Managerial Science, Springer, vol. 12(1), pages 203-228, January.
    20. Arjan J. Frederiks & Sílvia Costa & Boudewijn Hulst & Aard J. Groen, 2024. "The early bird catches the worm: The role of regulatory uncertainty in early adoption of blockchain’s cryptocurrency by fintech ventures," Journal of Small Business Management, Taylor & Francis Journals, vol. 62(2), pages 790-823, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3043-:d:1061052. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.