IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v108y2021i1d10.1007_s11069-021-04722-9.html
   My bibliography  Save this article

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Author

Listed:
  • Md. Abul Kalam Azad

    (Begum Rokeya University)

  • Abu Reza Md. Towfiqul Islam

    (Begum Rokeya University)

  • Md. Siddiqur Rahman

    (Begum Rokeya University)

  • Kurratul Ayen

    (Begum Rokeya University)

Abstract

Thunderstorm frequency (TSF) prediction with higher accuracy is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed two novel hybrid machine learning models through hybridization of data pre-processing ensemble empirical mode decomposition (EEMD) with two state-of-arts models, namely artificial neural network (ANN), support vector machine for TSF prediction at three categories over Bangladesh. We have demarcated the yearly TSF datasets into three categories for the period 1981–2016 recorded at 28 sites; high (March–June), moderate (July–October), and low (November–February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared with classical ANN, SVM, and autoregressive integrated moving average. EEMD-ANN and EEMD-SVM hybrid models showed 8.02–22.48% higher performance precision in terms of root mean square error compared to other models at high-, moderate-, and low-frequency categories. Eleven out of 21 input parameters were selected based on the random forest variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category, and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid models developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world.

Suggested Citation

  • Md. Abul Kalam Azad & Abu Reza Md. Towfiqul Islam & Md. Siddiqur Rahman & Kurratul Ayen, 2021. "Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh," 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. 108(1), pages 1109-1135, August.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04722-9
    DOI: 10.1007/s11069-021-04722-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-04722-9
    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/s11069-021-04722-9?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. Xingyu Zhang & Yuanyuan Liu & Min Yang & Tao Zhang & Alistair A Young & Xiaosong Li, 2013. "Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    2. Mohsen Alizadeh & Esmaeil Alizadeh & Sara Asadollahpour Kotenaee & Himan Shahabi & Amin Beiranvand Pour & Mahdi Panahi & Baharin Bin Ahmad & Lee Saro, 2018. "Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran," Sustainability, MDPI, vol. 10(10), pages 1-23, September.
    3. Rajesh Kumar Sahu & Jiteshwar Dadich & Bhishma Tyagi & Naresh Krishna Vissa & Jyotsna Singh, 2020. "Evaluating the impact of climate change in threshold values of thermodynamic indices during pre-monsoon thunderstorm season over Eastern 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. 102(3), pages 1541-1569, July.
    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. Dieu Tien Bui & Ataollah Shirzadi & Ata Amini & Himan Shahabi & Nadhir Al-Ansari & Shahriar Hamidi & Sushant K. Singh & Binh Thai Pham & Baharin Bin Ahmad & Pezhman Taherei Ghazvinei, 2020. "A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers," Sustainability, MDPI, vol. 12(3), pages 1-24, February.
    2. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    3. Jihye Han & Soyoung Park & Seongheon Kim & Sanghun Son & Seonghyeok Lee & Jinsoo Kim, 2019. "Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
    4. Xiaoxin Zhu & Yanyan Wang & David Regan & Baiqing Sun, 2020. "A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis," IJERPH, MDPI, vol. 17(19), pages 1-13, September.
    5. Yongqing Zhao & Rendong Li & Juan Qiu & Xiangdong Sun & Lu Gao & Mingquan Wu, 2019. "Prediction of Human Brucellosis in China Based on Temperature and NDVI," IJERPH, MDPI, vol. 16(21), pages 1-15, November.
    6. Navdeep Agrawal & Laxmi Gupta & Jagabandhu Dixit, 2021. "Assessment of the Socioeconomic Vulnerability to Seismic Hazards in the National Capital Region of India Using Factor Analysis," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
    7. Jihye Han & Jinsoo Kim & Soyoung Park & Sanghun Son & Minji Ryu, 2020. "Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    8. Gordana Pavić & Marijana Hadzima-Nyarko & Borko Bulajić, 2020. "A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek," Sustainability, MDPI, vol. 12(5), pages 1-24, February.
    9. Mohammad Ilbeigi & Sarath Chandra K. Jagupilla, 2020. "An Empirical Analysis of Association between Socioeconomic Factors and Communities’ Exposure to Natural Hazards," Sustainability, MDPI, vol. 12(16), pages 1-13, August.
    10. Qinqin Xu & Runzi Li & Yafei Liu & Cheng Luo & Aiqiang Xu & Fuzhong Xue & Qing Xu & Xiujun Li, 2017. "Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model," IJERPH, MDPI, vol. 14(8), pages 1-11, August.
    11. Wei Wu & Junqiao Guo & Shuyi An & Peng Guan & Yangwu Ren & Linzi Xia & Baosen Zhou, 2015. "Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-13, August.
    12. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
    13. Xin Wei & Niaz Muhammad Shahani & Xigui Zheng, 2023. "Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach," Mathematics, MDPI, vol. 11(7), pages 1-17, March.
    14. Khan, Firdos & Saeed, Alia & Ali, Shaukat, 2020. "Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    15. Yuxin Gao & Xianrui Yu & Menghao Xi & Qiuhong Zhao, 2023. "Assessment of Vulnerability Caused by Earthquake Disasters Based on DEA: A Case Study of County-Level Units in Chinese Mainland," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
    16. Hyung-Sup Jung & Saro Lee & Biswajeet Pradhan, 2020. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations," Sustainability, MDPI, vol. 12(6), pages 1-6, March.
    17. Gordana Pavić & Marijana Hadzima-Nyarko & Borko Bulajić & Željka Jurković, 2020. "Development of Seismic Vulnerability and Exposure Models—A Case Study of Croatia," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    18. Xingyu Zhang & Tao Zhang & Jiao Pei & Yuanyuan Liu & Xiaosong Li & Pau Medrano-Gracia, 2016. "Time Series Modelling of Syphilis Incidence in China from 2005 to 2012," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-18, February.
    19. Ahmad M Awajan & Mohd Tahir Ismail & S AL Wadi, 2018. "Improving forecasting accuracy for stock market data using EMD-HW bagging," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-20, July.
    20. Vaishnav, Vaibhav & Vajpai, Jayashri, 2020. "Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

    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:108:y:2021:i:1:d:10.1007_s11069-021-04722-9. 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.