IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/118724.html
   My bibliography  Save this paper

Deep learning detection of types of water-bodies using optical variables and ensembling

Author

Listed:
  • Nasir, Nida
  • Kansal, Afreen
  • Alshaltone, Omar
  • Barneih, Feras
  • Shanableh, Abdallah
  • Al-Shabi, Mohammad
  • Al Shammaa, Ahmed

Abstract

Water features are one of the most crucial environmental elements for strengthening climate-change adaptation. Remote sensing (RS) technologies driven by artificial intelligence (AI) have emerged as one of the most sought-after approaches for automating water information extraction and indeed. In this paper, a stacked ensemble model approach is proposed on AquaSat dataset (more than 500,000 images collection via satellite and Google Earth Engine). A one-way Analysis of variance (ANOVA) test and the Kruskal Wallis test are conducted for various optical-based variables at 99% significance level to understand how these vary for different water bodies. An oversampling is done on the training data using Synthetic Minority Oversampling Technique (SMOTE) to solve the problem of class imbalance while the model is tested on an imbalanced data, replicating the real-life situation. To enhance state-of-the-art, the pros of standalone machine learning classifiers and neural networks have been utilized. The stacked model obtained 100% accuracy on the testing data when using the decision tree classifier as the meta model. This study has been cross validated five-fold and will help researchers working in in-situ water bodies detection with the use of stacked model classification.

Suggested Citation

  • Nasir, Nida & Kansal, Afreen & Alshaltone, Omar & Barneih, Feras & Shanableh, Abdallah & Al-Shabi, Mohammad & Al Shammaa, Ahmed, 2023. "Deep learning detection of types of water-bodies using optical variables and ensembling," LSE Research Online Documents on Economics 118724, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:118724
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/118724/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kamran Shaukat & Suhuai Luo & Vijay Varadharajan & Ibrahim A. Hameed & Shan Chen & Dongxi Liu & Jiaming Li, 2020. "Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity," Energies, MDPI, vol. 13(10), pages 1-27, May.
    2. Sangmok Lee & Donghyun Lee, 2018. "Improved Prediction of Harmful Algal Blooms in Four Major South Korea’s Rivers Using Deep Learning Models," IJERPH, MDPI, vol. 15(7), pages 1-15, June.
    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. Fatima Rafiq & Mazhar Javed Awan & Awais Yasin & Haitham Nobanee & Azlan Mohd Zain & Saeed Ali Bahaj, 2022. "Privacy Prevention of Big Data Applications: A Systematic Literature Review," SAGE Open, , vol. 12(2), pages 21582440221, May.
    2. Chetna Monga & Deepali Gupta & Devendra Prasad & Sapna Juneja & Ghulam Muhammad & Zulfiqar Ali, 2022. "Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    3. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    4. Frank Cremer & Barry Sheehan & Michael Fortmann & Arash N. Kia & Martin Mullins & Finbarr Murphy & Stefan Materne, 2022. "Cyber risk and cybersecurity: a systematic review of data availability," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 698-736, July.
    5. Wojciech Szczepanik & Marcin Niemiec, 2022. "Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis," Energies, MDPI, vol. 15(11), pages 1-19, May.
    6. Feng Wu & Wanqiang Xu & Chaoran Lin & Yanwei Zhang, 2022. "Knowledge Trajectories on Public Crisis Management Research from Massive Literature Text Using Topic-Clustered Evolution Extraction," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    7. Ahmed Abdu & Zhengjun Zhai & Redhwan Algabri & Hakim A. Abdo & Kotiba Hamad & Mugahed A. Al-antari, 2022. "Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey," Mathematics, MDPI, vol. 10(17), pages 1-26, August.
    8. Fatin Nadiah Yussof & Normah Maan & Mohd Nadzri Md Reba, 2021. "LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
    9. Pengyi Liao & Jun Yan & Jean Michel Sellier & Yongxuan Zhang, 2022. "TADA: A Transferable Domain-Adversarial Training for Smart Grid Intrusion Detection Based on Ensemble Divergence Metrics and Spatiotemporal Features," Energies, MDPI, vol. 15(23), pages 1-18, November.
    10. Lee, Donghyun & Kim, Mingyu & Lee, Beomhui & Chae, Sangwon & Kwon, Sungjun & Kang, Sungwon, 2022. "Integrated explainable deep learning prediction of harmful algal blooms," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    11. Yuan Wang & Liping Yang & Jun Wu & Zisheng Song & Li Shi, 2022. "Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
    12. Zhencheng Fan & Zheng Yan & Shiping Wen, 2023. "Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

    More about this item

    Keywords

    ANOVA; classification; meta learning; smote; stacked modeling;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ehl:lserod:118724. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    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.