Technology-Driven Solutions for Harmful Algal Bloom Monitoring and Prediction: A Bibliometric Analysis of Research Trends, Collaboration Networks, and Emerging Technologies in Southeast Asia

Technology-Driven Solutions for Harmful Algal Bloom Monitoring and Prediction: A Bibliometric Analysis of Research Trends, Collaboration Networks, and Emerging Technologies in Southeast Asia

Authors

  • Lyndon A. Rosas

Keywords:

Artificial intelligence; Bibliometric analysis; Harmful algal blooms; Remote sensing; Southeast Asia

Abstract

Harmful algal blooms (HABs) pose significant ecological, economic, and public health challenges across Southeast Asia, driving increasing interest in technology-enabled monitoring and prediction systems. Despite the rapid adoption of digital technologies, a comprehensive understanding of the region's research development remains limited. This study provides a bibliometric analysis of technology-driven research on HAB monitoring and prediction in Southeast Asia using publications indexed in the Scopus database. A total of 2,181 English-language publications published between 1997 and 2026 were analyzed following a systematic screening process. Bibliometric analyses were performed using Bibliometrix (RStudio 4.5.3) and VOSviewer (v1.6.20) to examine research productivity, citation trends, influential publication sources, authors, institutions, countries, scientific collaboration, and thematic evolution. The results reveal a marked increase in scientific output after 2015, with the research field currently approaching its peak growth stage based on logistic life-cycle analysis. Indonesia, Malaysia, and Singapore emerged as leading contributors, supported by extensive international collaboration involving the United States, China, Japan, and other research-intensive countries. Influential publication venues and authors highlight the multidisciplinary nature of the field, spanning environmental science, remote sensing, engineering, and artificial intelligence. Thematic evolution further demonstrates a transition from conventional environmental monitoring toward intelligent approaches integrating remote sensing, machine learning, artificial intelligence, deep learning, and the Internet of Things (IoT). These findings provide a comprehensive overview of the intellectual landscape of technology-driven HAB research and offer valuable insights for researchers, policymakers, and funding agencies in identifying collaboration opportunities and future research priorities for sustainable aquatic environmental management in Southeast Asia.

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Published

2026-08-01

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Articles

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