IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v121y2025i8d10.1007_s11069-025-07140-3.html
   My bibliography  Save this article

Forecasting climate risk and heat stress hazards in arid ecosystems: Machine learning and ensemble models for specific humidity prediction in Dammam, Saudi Arabia

Author

Listed:
  • Adel S. Aldosary

    (King Fahd University of Petroleum and Minerals (KFUPM))

  • Baqer Al-Ramadan

    (King Fahd University of Petroleum and Minerals (KFUPM)
    King Fahd University of Petroleum and Minerals (KFUPM))

  • Abdulla Al Kafy

    (The University of Texas at Austin)

  • Hamad Ahmed Altuwaijri

    (King Saud University)

  • Zullyadini A. Rahaman

    (Sultan Idris Education University)

Abstract

Climate change is intensifying weather-related hazards in arid regions, making precise predictive models crucial for effective adaptation strategies. This study employs machine learning (ML) and ensemble learning (EL) models to predict specific humidity (SH) levels in Dammam, Saudi Arabia, as a foundation for forecasting climate risk and heat stress hazards in arid ecosystems. Using daily climate data from 1982 to 2023, we applied three ML models (Support Vector Machine, M5-Pruned Tree, Reduced Error Pruning Tree) and six EL models (Random Forest, Additive Regression, Random Subspace, Light Gradient Boosting Machine, Adaptive Boosting Regression, eXtreme Gradient Boosting) to predict SH levels. Descriptive statistics showed significant seasonal variations, with the highest SH levels in August (15.87 g/kg) and the lowest in January (7.16 g/kg). The region's minimal annual precipitation (average 60.57 mm) and extreme summer temperatures (average July maximum of 44.06 °C) further underline Dammam’s vulnerability to climate-induced stress. A significant increasing trend in annual SH levels was confirmed through the Mann–Kendall Test and Innovative Trend Analysis, highlighting a rising trend with a Sen's Slope of 0.025 g/kg/year. The most substantial increases occurred in July and August, reaching 0.059 and 0.054 g/kg/year, respectively, indicating escalating heat stress risks during summer season. Among the tested models, LightGBM and XGBoost stood out for accuracy (R2 = 0.99897 and 0.99883 respectively), while Additive Regression achieved the best balance across all performance metrics (R2 = 0.9987). The performance of these models demonstrates strong potential for early warning systems, enabling proactive responses to heat-related hazards. By integrating ML and EL models, this study provides a robust framework for forecasting humidity trends, contributing to improved risk assessment, disaster preparedness, and climate adaptation strategies for arid regions like Dammam.

Suggested Citation

  • Adel S. Aldosary & Baqer Al-Ramadan & Abdulla Al Kafy & Hamad Ahmed Altuwaijri & Zullyadini A. Rahaman, 2025. "Forecasting climate risk and heat stress hazards in arid ecosystems: Machine learning and ensemble models for specific humidity prediction in Dammam, Saudi Arabia," 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(8), pages 9281-9309, May.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:8:d:10.1007_s11069-025-07140-3
    DOI: 10.1007/s11069-025-07140-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-025-07140-3
    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/s11069-025-07140-3?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Barreca, Alan I., 2012. "Climate change, humidity, and mortality in the United States," Journal of Environmental Economics and Management, Elsevier, vol. 63(1), pages 19-34.
    2. Mst Noorunnahar & Arman Hossain Chowdhury & Farhana Arefeen Mila, 2023. "A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-15, March.
    3. Somya Goyal, 2022. "Effective software defect prediction using support vector machines (SVMs)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 681-696, April.
    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. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
    6. Sheikh Amir Fayaz & Majid Zaman & Muheet Ahmed Butt, 2022. "Numerical and Experimental Investigation of Meteorological Data Using Adaptive Linear M5 Model Tree for the Prediction of Rainfall," Review of Computer Engineering Research, Conscientia Beam, vol. 9(1), pages 1-12.
    7. Vaia I. Kontopoulou & Athanasios D. Panagopoulos & Ioannis Kakkos & George K. Matsopoulos, 2023. "A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks," Future Internet, MDPI, vol. 15(8), pages 1-31, July.
    8. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    9. Fan, Junliang & Ma, Xin & Wu, Lifeng & Zhang, Fucang & Yu, Xiang & Zeng, Wenzhi, 2019. "Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data," Agricultural Water Management, Elsevier, vol. 225(C).
    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. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2022. "Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes," Agricultural Water Management, Elsevier, vol. 261(C).
    2. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    3. Marko Korhonen & Suvi Kangasrääsiö & Rauli Svento, 2017. "Climate change and mortality: Evidence from 23 developed countries between 1960 and 2010," Proceedings of International Academic Conferences 5107635, International Institute of Social and Economic Sciences.
    4. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2014. "What Do We Learn from the Weather? The New Climate-Economy Literature," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 740-798, September.
    5. Xi Chen & Chih Ming Tan & Xiaobo Zhang & Xin Zhang, 2020. "The effects of prenatal exposure to temperature extremes on birth outcomes: the case of China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1263-1302, October.
    6. Ziebarth, N. R. & Schmitt, M. & Karlsson, M., 2013. "The short-term population health effects of weather and pollution: implications of climate change," Health, Econometrics and Data Group (HEDG) Working Papers 13/34, HEDG, c/o Department of Economics, University of York.
    7. Fritz, Manuela, 2021. "Temperature and non-communicable diseases: Evidence from Indonesia's primary health care system," Passauer Diskussionspapiere, Volkswirtschaftliche Reihe V-84-21, University of Passau, Faculty of Business and Economics.
    8. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    9. 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).
    10. Otrachshenko, Vladimir & Popova, Olga & Solomin, Pavel, 2017. "Health Consequences of the Russian Weather," Ecological Economics, Elsevier, vol. 132(C), pages 290-306.
    11. Tarsia, Romano, 2024. "Heterogeneous effects of weather shocks on firm economic performance," LSE Research Online Documents on Economics 124251, London School of Economics and Political Science, LSE Library.
    12. Ook Lee & Hanseon Joo & Hayoung Choi & Minjong Cheon, 2022. "Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
    13. Xuecheng He & Jujie Wang, 2024. "A Hybrid Forecasting System Based on Comprehensive Feature Selection and Intelligent Optimization for Stock Price Index Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-27, November.
    14. Hanlon, W. Walker & Hansen, Casper Worm & Kantor, Jake, 2021. "Temperature, Disease, and Death in London: Analyzing Weekly Data for the Century from 1866 to 1965," The Journal of Economic History, Cambridge University Press, vol. 81(1), pages 40-80, March.
    15. Pierre Masselot & Fateh Chebana & Taha B. M. J. Ouarda & Diane Bélanger & Pierre Gosselin, 2022. "Data-Enhancement Strategies in Weather-Related Health Studies," IJERPH, MDPI, vol. 19(2), pages 1-13, January.
    16. Joshua Graff Zivin & Matthew Neidell, 2012. "The Impact of Pollution on Worker Productivity," American Economic Review, American Economic Association, vol. 102(7), pages 3652-3673, December.
    17. De Cian, Enrica & Wing, Ian Sue, "undated". "Global Energy Demand in a Warming Climate," EIA: Climate Change: Economic Impacts and Adaptation 232222, Fondazione Eni Enrico Mattei (FEEM).
    18. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
    19. Esangbedo, Moses Olabhele & Taiwo, Blessing Olamide & Abbas, Hawraa H. & Hosseini, Shahab & Sazid, Mohammed & Fissha, Yewuhalashet, 2024. "Enhancing the exploitation of natural resources for green energy: An application of LSTM-based meta-model for aluminum prices forecasting," Resources Policy, Elsevier, vol. 92(C).
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:121:y:2025:i:8:d:10.1007_s11069-025-07140-3. 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.