IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v110y2022i3d10.1007_s11069-021-05019-7.html
   My bibliography  Save this article

Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model

Author

Listed:
  • Ali Barzkar

    (Graduate University of Advanced Technology)

  • Mohammad Najafzadeh

    (Graduate University of Advanced Technology)

  • Farshad Homaei

    (Graduate University of Advanced Technology)

Abstract

Due to a wide range of socio-economic losses caused by drought over the past decades, having a reliable insight of drought properties plays a key role in monitoring and forecasting the drought situations, and finally generating robust methodologies for adapting to the various vulnerability of drought situations. The most important factor in causing drought is rainfall, but increasing or decreasing the temperature and consequently, evapotranspiration can intensify or moderate the severity of drought events. Standardized Precipitation Evaporation Index (SPEI), as one of the most well-known indices in the definition of the drought situation, is applied based on potential precipitation, evapotranspiration, and the water balance. In this study, values of SPEI are formulated for various climates by three robust Artificial Intelligence (AI) models: Gene Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS). Meteorological variables including maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tmean), relative humidity (RH), 24-h rainfall (P24) and wind speed (U2) were used to perform the AI models. Dataset reported from four synoptic stations through Iran, dating back to a 58-year period beginning in 1957. Each AI technique was run for all the climatic situations: Temperate-Warm (T-W), Wet-Warm (W-W), Arid-Cold (A-C), and Arid-Warm (A-W). Results of AI models development indicated that M5 version of MT provided the most accurate SPEI prediction for all the climatic situations in comparison with GEP and MARS techniques. SPEI values for four climatic conditions were evaluated in the reliability-based probabilistic framework to take into account the influence of any uncertainty and randomness associated with meteorological variables. In this way, the Monte-Carlo scenario sampling approach has been used to assess the limit state function from the AI models-based-SPEI. Based on the reliability analysis for all the synoptic stations, as the probability of exceedance values declined to below 75%, drought situations varied from “Normal” to “Very Extreme Humidity”.

Suggested Citation

  • Ali Barzkar & Mohammad Najafzadeh & Farshad Homaei, 2022. "Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model," 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. 110(3), pages 1931-1952, February.
  • Handle: RePEc:spr:nathaz:v:110:y:2022:i:3:d:10.1007_s11069-021-05019-7
    DOI: 10.1007/s11069-021-05019-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-05019-7
    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/s11069-021-05019-7?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. Maroufpoor, Saman & Shiri, Jalal & Maroufpoor, Eisa, 2019. "Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables," Agricultural Water Management, Elsevier, vol. 215(C), pages 63-73.
    2. Jalal Shiri & Ali Keshavarzi & Ozgur Kisi & Sahar Mohsenzadeh Karimi & Sepideh Karimi & Amir Hossein Nazemi & Jesús Rodrigo-Comino, 2020. "Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    3. Salim Djerbouai & Doudja Souag-Gamane, 2016. "Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2445-2464, May.
    4. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    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. Željka Brkić & Mladen Kuhta, 2022. "Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning," Sustainability, MDPI, vol. 14(16), pages 1-28, August.

    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. Fuentes, Sigfredo & Ortega-Farías, Samuel & Carrasco-Benavides, Marcos & Tongson, Eden & Gonzalez Viejo, Claudia, 2024. "Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling," Agricultural Water Management, Elsevier, vol. 297(C).
    2. Fabio Di Nunno & Marco De Matteo & Giovanni Izzo & Francesco Granata, 2023. "A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    3. Beáta Novotná & Ľuboš Jurík & Ján Čimo & Jozef Palkovič & Branislav Chvíla & Vladimír Kišš, 2022. "Machine Learning for Pan Evaporation Modeling in Different Agroclimatic Zones of the Slovak Republic (Macro-Regions)," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    4. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
    5. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).
    6. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    7. Mohammad Taghi Sattari & Halit Apaydin & Shahaboddin Shamshirband, 2020. "Performance Evaluation of Deep Learning-Based Gated Recurrent Units (GRUs) and Tree-Based Models for Estimating ETo by Using Limited Meteorological Variables," Mathematics, MDPI, vol. 8(6), pages 1-18, June.
    8. Okan Mert Katipoğlu, 2023. "Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    9. So Young Woo & Chung Gil Jung & Ji Wan Lee & Seong Joon Kim, 2019. "Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique," Sustainability, MDPI, vol. 11(12), pages 1-15, June.
    10. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
    11. Djerbouai Salim & Souag-Gamane Doudja & Ferhati Ahmed & Djoukbala Omar & Dougha Mostafa & Benselama Oussama & Hasbaia Mahmoud, 2023. "Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1401-1420, February.
    12. Ohana-Levi, Noa & Ben-Gal, Alon & Munitz, Sarel & Netzer, Yishai, 2022. "Grapevine crop evapotranspiration and crop coefficient forecasting using linear and non-linear multiple regression models," Agricultural Water Management, Elsevier, vol. 262(C).
    13. Naser Shiri & Jalal Shiri & Zaher Mundher Yaseen & Sungwon Kim & Il-Moon Chung & Vahid Nourani & Mohammad Zounemat-Kermani, 2021. "Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.
    14. Belkhiri, Lazhar, 2021. "Spatial and temporal variability of water stress risk in the Kebir Rhumel Basin, Algeria," Agricultural Water Management, Elsevier, vol. 253(C).
    15. De Caro, Dario & Ippolito, Matteo & Cannarozzo, Marcella & Provenzano, Giuseppe & Ciraolo, Giuseppe, 2023. "Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using gr," Agricultural Water Management, Elsevier, vol. 290(C).
    16. Jiang, Xiaoman & Wang, Yuntao & A., Yinglan & Wang, Guoqiang & Zhang, Xiaojing & Ma, Guangwen & Duan, Limin & Liu, Kai, 2024. "Optimizing actual evapotranspiration simulation to identify evapotranspiration partitioning variations: A fusion of physical processes and machine learning techniques," Agricultural Water Management, Elsevier, vol. 295(C).
    17. Zhang, Lei & Zhao, Xin & Zhu, Ge & He, Jun & Chen, Jian & Chen, Zhicheng & Traore, Seydou & Liu, Junguo & Singh, Vijay P., 2023. "Short-term daily reference evapotranspiration forecasting using temperature-based deep learning models in different climate zones in China," Agricultural Water Management, Elsevier, vol. 289(C).
    18. Anshuka Anshuka & Floris F. van Ogtrop & R. Willem Vervoort, 2019. "Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis," 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. 97(2), pages 955-977, June.
    19. Erdem Küçüktopcu & Emirhan Cemek & Bilal Cemek & Halis Simsek, 2023. "Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling," Sustainability, MDPI, vol. 15(7), pages 1-15, March.
    20. Halil Karahan & Mahmut Cetin & Muge Erkan Can & Omar Alsenjar, 2024. "Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques for Sustainable Water Management," Sustainability, MDPI, vol. 16(6), pages 1-17, 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:110:y:2022:i:3:d:10.1007_s11069-021-05019-7. 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.