Эффективность различных ансамблевых алгоритмов машинного обучения в классификации индексов качества воды
Farid Hassanbaki Garabaghi a, Semra Benzer b, Recep Benzer c
a Gazi University, Graduate School of Natural and Applied Sciences, Teknikokullar, 06500 Ankara, Turkey; fgarabaghi@gmail.com
b Gazi University, Gazi Education Faculty, Teknikokullar, 06500 Ankara, Turkey; sbenzer@gazi.edu.tr,
c Ankara Medipol University, Department of Management Information System, Ankara, 06050 Ankara, Turkey; recep.benzer@ankaramedipol.edu.tr
https://doi.org/10.29258/CAJWR/2026-R1.v12-1/68-92.eng
*e-mail: sbenzer@gazi.edu.tr
Thematic cluster: Climate & Environment
Type of paper: Research paper
Abstract
The Aral Sea crisis, a landmark case of human-induced environmental disaster, has prompted extensive international intervention. The World Bank (WB), as a major donor, plays a pivotal role in shaping responses to this crisis. This paper argues that the WB’s interventions are legitimized through a powerful discursive strategy: the partial securitization of the crisis as a threat to human security. Using Critical Discourse Analysis (CDA) and securitization theory, this study examines how the WB frames the Aral Sea’s degradation in two key projects: the Syr Darya Control and Northern Aral Sea Phase I Project (NAS) and the Climate Adaptation and Mitigation Program for the Aral Sea Basin (CAMP4ASB). The analysis reveals that by constructing a narrative of imminent threat, the WB prioritizes technical, top-down solutions and measurable economic outcomes. This WB’s strategy in the Aral Sea basin, however, effectively depoliticizes the crisis, obscuring its historical and socio-political roots and sidelining community participation and structural inequities. This paper identifies a process of partial securitization, whereby the WB frames an issue as a critical threat to human security to justify intervention, but deliberately contains it within the realm of technical, market-based solutions, thereby avoiding the full political ramifications of a traditional securitization “speech act”. The study concludes by discussing the implications of this security framing for development policy and advocates for more inclusive, locally grounded approaches to environmental governance.
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Hassanbaki, F., Benzer, S., Benzer, R. (2026). Performance of Different Ensemble Machine Learning Algorithms in Classification of the Water Quality Indices. Central Asian Journal of Water Research, 12(1), 68-92. https://doi.org/10.29258/CAJWR/2026-R1.v12-1/68-92.eng
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