IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v120y2024i2d10.1007_s11069-023-06233-1.html
   My bibliography  Save this article

ML-DPIE: comparative evaluation of machine learning methods for drought parameter index estimation: a case study of Türkiye

Author

Listed:
  • Önder Çoban

    (Ataturk University)

  • Musa Eşit

    (Adiyaman University)

  • Sercan Yalçın

    (Adiyaman University)

Abstract

Finding solutions for long-term drought parameter index estimation (DPIE) is very crucial since there is a rising trend of drought which has a huge impact on water supplies, various ecosystems, public health, agriculture, and the tourism industry. Therefore, researchers developed a variety of indices to describe the frequency, intensity, duration, and geographic distribution of droughts. In addition, a variety of physical/conceptual models are proposed and used for DPIE. However, the scientific community has recently focused on machine learning (ML) for a variety of problems including DPIE. This is because data-driven ML models learn through experience and are reliable for hydrological and meteorological forecasting. In this study, we therefore performed a comparative evaluation of regression versus deep learning methods for the task of DPIE. We performed experiments on three stations located in Türkiye and considered three different indices. Our results show that traditional regressors often provide better results than deep learners. In addition, the effect of indices on the results is limited especially for the regression algorithms. Deep learning models on the other hand outperform regression algorithms in some cases and they have a disadvantage in finding the optimum structure. To the best of our knowledge, this study is the first of its kind concerning the number of employed algorithms and indices, the extent of experiments, and considered stations. Besides, the findings of this study can be used to deploy an ML model to monitor drought in a highly accurate way for related stations in Türkiye.

Suggested Citation

  • Önder Çoban & Musa Eşit & Sercan Yalçın, 2024. "ML-DPIE: comparative evaluation of machine learning methods for drought parameter index estimation: a case study of Türkiye," 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(2), pages 989-1021, January.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:2:d:10.1007_s11069-023-06233-1
    DOI: 10.1007/s11069-023-06233-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-06233-1
    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/s11069-023-06233-1?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. Çağlar Kıvanç Kaymaz & Salih Birinci & Yusuf Kızılkan, 2022. "Sustainable development goals assessment of Erzurum province with SWOT-AHP analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 2986-3012, March.
    2. Dianne Lowe & Kristie L. Ebi & Bertil Forsberg, 2011. "Heatwave Early Warning Systems and Adaptation Advice to Reduce Human Health Consequences of Heatwaves," IJERPH, MDPI, vol. 8(12), pages 1-26, December.
    3. Francesca Perla & Ronald Richman & Salvatore Scognamiglio & Mario V. Wüthrich, 2021. "Time-series forecasting of mortality rates using deep learning," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2021(7), pages 572-598, August.
    4. Shastri, Sourabh & Singh, Kuljeet & Kumar, Sachin & Kour, Paramjit & Mansotra, Vibhakar, 2020. "Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. G. Tsakiris & D. Pangalou & H. Vangelis, 2007. "Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 821-833, May.
    6. Çağlar Kıvanç Kaymaz & Salih Birinci & Yusuf Kızılkan, 2022. "Correction to: Sustainable development goals assessment of Erzurum province with SWOT‑AHP analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3013-3013, March.
    7. Muhammad Ahmad Raza & Mohammed M. A. Almazah & Zulfiqar Ali & Ijaz Hussain & Fuad S. Al-Duais & M. Z. Naser, 2022. "Application of Extreme Learning Machine Algorithm for Drought Forecasting," Complexity, Hindawi, vol. 2022, pages 1-28, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Veysi Kartal & Erkan Karakoyun & Muhammed Ernur Akiner & Okan Mert Katipoğlu & Alban Kuriqi, 2025. "Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights," 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. 121(5), pages 5729-5756, March.

    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. Mohammad Ghabaei Sough & Hamid Zare Abyaneh & Abolfazl Mosaedi, 2018. "Assessing a Multivariate Approach Based on Scalogram Analysis for Agricultural Drought Monitoring," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3423-3440, August.
    2. Dimitrios Myronidis & Konstantinos Ioannou & Dimitrios Fotakis & Gerald Dörflinger, 2018. "Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1759-1776, March.
    3. Brunella Bonaccorso & David Peres & Antonio Castano & Antonino Cancelliere, 2015. "SPI-Based Probabilistic Analysis of Drought Areal Extent in Sicily," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 459-470, January.
    4. Konstantinos Spiliotis & Konstantinos Voudouris & Harris Vangelis & Mike Spiliotis, 2025. "Analysis of Annual Drought Episodes Using Complex Networks," Sustainability, MDPI, vol. 17(4), pages 1-17, February.
    5. Pere Quintana-Seguí & Anaïs Barella-Ortiz & Sabela Regueiro-Sanfiz & Gonzalo Miguez-Macho, 2020. "The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(7), pages 2135-2156, May.
    6. Efrosyni Kanellou & Nicos Spyropoulos & Nicolas Dalezios, 2012. "Geoinformatic Intelligence Methodologies for Drought Spatiotemporal Variability in Greece," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(5), pages 1089-1106, March.
    7. Peng Qi & Y. Jun Xu & Guodong Wang, 2020. "Quantifying the Individual Contributions of Climate Change, Dam Construction, and Land Use/Land Cover Change to Hydrological Drought in a Marshy River," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    8. Halkos, George E & Aslanidis, Panagiotis-Stavros & Landis, Conrad & Papadaki, Lydia & Koundouri, Phoebe, 2024. "A review on primary and cascading hazards by exploring individuals’ willingness-to-pay for urban sustainability policies," MPRA Paper 122262, University Library of Munich, Germany.
    9. Nicolas R. Dalezios & Nicholas Dercas & Nicos V. Spyropoulos & Emmanouil Psomiadis, 2019. "Remotely Sensed Methodologies for Crop Water Availability and Requirements in Precision Farming of Vulnerable Agriculture," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1499-1519, March.
    10. Enes Gul & Efthymia Staiou & Mir Jafar Sadegh Safari & Babak Vaheddoost, 2023. "Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
    11. D. Chiru Naik & Sagar Rohidas Chavan & P. Sonali, 2023. "Incorporating the climate oscillations in the computation of meteorological drought over India," 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. 117(3), pages 2617-2646, July.
    12. Gift Nxumalo & Bashar Bashir & Karam Alsafadi & Hussein Bachir & Endre Harsányi & Sana Arshad & Safwan Mohammed, 2022. "Meteorological Drought Variability and Its Impact on Wheat Yields across South Africa," IJERPH, MDPI, vol. 19(24), pages 1-22, December.
    13. E. Preziosi & A. Bon & E. Romano & A. Petrangeli & S. Casadei, 2013. "Vulnerability to Drought of a Complex Water Supply System. The Upper Tiber Basin Case Study (Central Italy)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4655-4678, October.
    14. Rina Wu & Jiquan Zhang & Yuhai Bao & Enliang Guo, 2019. "Run Theory and Copula-Based Drought Risk Analysis for Songnen Grassland in Northeastern China," Sustainability, MDPI, vol. 11(21), pages 1-17, October.
    15. Laura Şmuleac & Ciprian Rujescu & Adrian Șmuleac & Florin Imbrea & Isidora Radulov & Dan Manea & Anișoara Ienciu & Tabita Adamov & Raul Pașcalău, 2020. "Impact of Climate Change in the Banat Plain, Western Romania, on the Accessibility of Water for Crop Production in Agriculture," Agriculture, MDPI, vol. 10(10), pages 1-24, September.
    16. Saeed Azimi & Mehdi Azhdary Moghaddam, 2020. "Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1369-1405, March.
    17. George Tsakiris & Mike Spiliotis & Harris Vangelis & Panagiotis Tsakiris, 2015. "Εvaluation of Measures for Combating Water Shortage Based on Beneficial and Constraining Criteria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 505-520, January.
    18. Shahab Araghinejad, 2011. "An Approach for Probabilistic Hydrological Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(1), pages 191-200, January.
    19. Mohamed Meddi & Ali Assani & Hind Meddi, 2010. "Temporal Variability of Annual Rainfall in the Macta and Tafna Catchments, Northwestern Algeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 3817-3833, November.
    20. Dilayda Soylu Pekpostalci & Rifat Tur & Ali Danandeh Mehr & Mohammad Amin Vazifekhah Ghaffari & Dominika Dąbrowska & Vahid Nourani, 2023. "Drought Monitoring and Forecasting across Turkey: A Contemporary Review," Sustainability, MDPI, vol. 15(7), pages 1-23, 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:spr:nathaz:v:120:y:2024:i:2:d:10.1007_s11069-023-06233-1. 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.