IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v120y2024i7d10.1007_s11069-024-06477-5.html
   My bibliography  Save this article

Predicting topographic collapse following lava dome growth at Ibu volcano (North Maluku, Indonesia) using high-resolution PlanetScope images

Author

Listed:
  • Asep Saepuloh

    (Bandung Institute of Technology (ITB))

  • Nur Ayu Anas

    (Cenderawasih University)

  • Estu Kriswati

    (National Research and Innovation Agency (BRIN))

  • Anjar Dimara Sakti

    (Bandung Institute of Technology (ITB))

  • Oktory Prambada

    (Geological Agency)

Abstract

In this study, we described a rare case of lava dome growth at Mt. Ibu in West Halmahera Regency, North Maluku Province, Indonesia, in which the inner crater was filled, even exceeding the outer crater rim on the northern flank. The observed lava dome growth caused concern due to the rapid volumetric change, followed by topographic collapse, thus producing hazardous pyroclastic flows and debris avalanches. Based on the condition of Mt. Ibu, we calculated the lava dome area and volume using PlanetScope images and a national digital elevation model, respectively. Comparing the lava dome volume to the crater space, we predicted the area and time of future topographic collapse. We calculated the time series of the lava dome volume from January 2020 to August 2022 to predict the time of the maximum volume of the outer crater rim using autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models. According to the time series, Mt. Ibu was beyond the critical conditions for collapse when the lava dome exceeded the outer crater rim by approximately 0.114 km3 or an area of 1.477 km2. Then, ARIMA and SARIMA predictions were simultaneously obtained, and the critical condition was predicted to be achieved in 2037. The confidence level of the ARIMA model was captured by the root mean squared error (0.008 km2) and mean absolute percentage error (approximately 0.554%). Moreover, the values were approximately 0.009 km2 and 0.397%, respectively, for the SARIMA model.

Suggested Citation

  • Asep Saepuloh & Nur Ayu Anas & Estu Kriswati & Anjar Dimara Sakti & Oktory Prambada, 2024. "Predicting topographic collapse following lava dome growth at Ibu volcano (North Maluku, Indonesia) using high-resolution PlanetScope images," 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. 120(7), pages 6755-6773, May.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:7:d:10.1007_s11069-024-06477-5
    DOI: 10.1007/s11069-024-06477-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-024-06477-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-024-06477-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Tommaso Carlà & Emanuele Intrieri & Federico Traglia & Nicola Casagli, 2016. "A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series," 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. 84(1), pages 669-683, October.
    4. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    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. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    2. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    3. Han Lin Shang, 2017. "Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(1), pages 55-84, February.
    4. Leonardo Di Gangi & M. Lapucci & F. Schoen & A. Sortino, 2019. "An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series," Computational Optimization and Applications, Springer, vol. 74(3), pages 919-948, December.
    5. Lange, Steffen & Pütz, Peter & Kopp, Thomas, 2018. "Do Mature Economies Grow Exponentially?," Ecological Economics, Elsevier, vol. 147(C), pages 123-133.
    6. Naomi Muggleton & Charles Rahal & Aaron Reeves, 2025. "Capitalizing on a crisis: a computational analysis of all five million British firms during the Covid-19 pandemic," Journal of Computational Social Science, Springer, vol. 8(2), pages 1-29, May.
    7. Schipfer, Fabian & Kranzl, Lukas & Olsson, Olle & Lamers, Patrick, 2020. "The European wood pellets for heating market - Price developments, trade and market efficiency," Energy, Elsevier, vol. 212(C).
    8. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    9. Malliga Subramanian & Jaehyuk Cho & Sathishkumar Veerappampalayam Easwaramoorthy & Akash Murugesan & Ramya Chinnasamy, 2023. "Enhancing Sustainable Transportation: AI-Driven Bike Demand Forecasting in Smart Cities," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    10. Barnes, Stuart J., 2021. "Stuck in the past or living in the present? Temporal focus and the spread of COVID-19," Social Science & Medicine, Elsevier, vol. 280(C).
    11. Naomi Muggleton & Charles Rahal & Aaron Reeves, 2025. "Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic," Papers 2502.09383, arXiv.org, revised Feb 2025.
    12. Robert I. Harris & William A. Pizer, 2020. "Using Taxes to Meet an Emission Target," NBER Working Papers 27781, National Bureau of Economic Research, Inc.
    13. Joana M. Barros & Ruth Melia & Kady Francis & John Bogue & Mary O’Sullivan & Karen Young & Rebecca A. Bernert & Dietrich Rebholz-Schuhmann & Jim Duggan, 2019. "The Validity of Google Trends Search Volumes for Behavioral Forecasting of National Suicide Rates in Ireland," IJERPH, MDPI, vol. 16(17), pages 1-18, September.
    14. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844.
    15. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    16. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    17. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    18. Ghufran Ahmad & Furqan Ahmed & Muhammad Suhail Rizwan & Javed Muhammad & Syeda Hira Fatima & Aamer Ikram & Hajo Zeeb, 2021. "Evaluating data-driven methods for short-term forecasts of cumulative SARS-CoV2 cases," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.
    19. Rice, William L. & Park, So Young & Pan, Bing & Newman, Peter, 2019. "Forecasting campground demand in US national parks," Annals of Tourism Research, Elsevier, vol. 75(C), pages 424-438.
    20. Saša Obradoviæ & Lela Ristiæ & Nemanja Lojanica, 2018. "Are unemployment rates stationary for SEE10 countries? Evidence from linear and nonlinear dynamics," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 36(2), pages 559-583.

    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:spr:nathaz:v:120:y:2024:i:7:d:10.1007_s11069-024-06477-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.