Preprints
https://doi.org/10.5194/nhess-2022-249
https://doi.org/10.5194/nhess-2022-249
28 Oct 2022
 | 28 Oct 2022
Status: this preprint has been withdrawn by the authors.

Review article: Current State of Deep Learning Application to Water-related Disaster Management in Developing Countries

Kola Yusuff Kareem, Yeonjeong Seong, Shiksha Bastola, and Younghun Jung

Abstract. Availability of abundant water resources data is a great concern hindering adoption of deep learning techniques (DL) for disaster mitigation in developing countries. However, over the last three decades, a sizeable amount of DL publication in disaster management emanated mostly from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster problems in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 1993–2022 using Web of Science, Scopus, VOSviewer software and PRISMA model schema. Results revealed a ‘slightly’ increasing trend of DL-based water disaster publication in developing countries (tau = 0.35, p = 0.00045, Sen-slope, s = 0.00 at confidence level of 95 %), as opposed to the ‘significantly’ increasing trend globally (tau = 0.910, p = 1.72 e-12, Sen-slope, s = 2.52 at confidence level of 95 %). Also, pluvio-fluvial flooding is found to constitute 78 % most disaster prevalence and China is the only ‘high human development’ developing country with an impressive 51 % DL adoption rate, due to China’s increasing need for AI-based solutions to persistent multiyear severe water stress, climate change, environmental degradation, recurrent flood, and saltwater intrusion into estuaries. COVID-19 among other factors is identified as a driver of DL adoption. Further analysis indicates that developing countries will experience implementation delay based on their low Human Development Indices (HDI) because model deployment in solving disaster problems in real life scenarios is currently lacking due to high cost. Therefore, data augmentation, transfer learning, intensive research, deployment using cheap web-based servers and APIs are recommended to enhance disaster preparedness. Developing countries can explore these solutions to foster inclusion in global DL-based disaster mitigation approaches.

This preprint has been withdrawn.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Kola Yusuff Kareem, Yeonjeong Seong, Shiksha Bastola, and Younghun Jung

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-249', Anonymous Referee #1, 10 Jan 2023
    • AC1: 'Reply on RC1', Kola Yusuff Kareem, 17 Jan 2023
  • RC2: 'Comment on nhess-2022-249', Anonymous Referee #2, 06 Jul 2023
    • AC2: 'Reply on RC2', Kola Yusuff Kareem, 24 Jul 2023
      • AC3: 'Reply on AC2', Kola Yusuff Kareem, 25 Jul 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-249', Anonymous Referee #1, 10 Jan 2023
    • AC1: 'Reply on RC1', Kola Yusuff Kareem, 17 Jan 2023
  • RC2: 'Comment on nhess-2022-249', Anonymous Referee #2, 06 Jul 2023
    • AC2: 'Reply on RC2', Kola Yusuff Kareem, 24 Jul 2023
      • AC3: 'Reply on AC2', Kola Yusuff Kareem, 25 Jul 2023
Kola Yusuff Kareem, Yeonjeong Seong, Shiksha Bastola, and Younghun Jung
Kola Yusuff Kareem, Yeonjeong Seong, Shiksha Bastola, and Younghun Jung

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Short summary
Globally, data-driven approaches for mitigating water-induced disaster risk have gained momentum in developed countries over the last three decades but sadly, few publications emerged from developing countries, thereby indicating slow adoption. Our Trend analysis authenticated this hypothesis. Through bibliometric and theory-based analysis, we identified China as the country with the most impressive deep learning adoption while revealing that model deployment is lacking in developing countries.
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