A village remembers, even as the waters rise.
What if ancient wisdom could teach algorithms to save lives?
Silsila: Echoes of the Glacier's Weep: Silsila explores the convergence of glacial melt, machine learning, and Tajik folklore to illuminate future flood risks and empower community resilience.
Vohidov_9347, Tajikistan
Vohidov_9347, Tajikistan
Description
Understanding future flood risks requires looking to the past and the future. *Silsila* presents an interactive simulation of potential flash flood impacts in urban Tajikistan. Drawing on a blend of machine learning, oceanographic models, and the rich narratives of Tajik folklore, this visualization offers a compelling experience. Detailed scenarios highlight the intricate relationship between glacial melt, human infrastructure, and community resilience. Viewers can explore the projected consequences of inaction and the potential for informed preparedness to mitigate community disaster, fostering cautious optimism, and a call for proactive engagement.
Context
In this imagined future, communities live with the constant threat of glacial lake outburst floods. Traditional Tajik architecture blends with resilient, modern infrastructure. Inhabitants maintain a deep respect for the mountains and water, adapting ancient survival practices with new technologies. The culture values community bonds and oral storytelling as vital for sharing knowledge and building collective resilience. Despite the challenges, a spirit of determined optimism and resourcefulness prevails, preparing them for an uncertain future.
Provocation
This project reveals the escalating risk of glacial lake outburst floods. It highlights a critical need for proactive disaster management. Viewers should be concerned about the acceleration of climate change and its disproportionate impact on vulnerable regions. Consider: how can we blend technological advancements with traditional knowledge for better preparedness?
Vohidov_9347's process
Vohidov, a two-spirit designer, draws inspiration from artificial intelligence and oceanography. Their work is deeply influenced by Tajik folklore's legends of natural forces. For *Silsila*, the designer input historical flood records and oceanographic models into machine learning algorithms and layered multiple algorithms. The unique cultural lens of Tajik oral storytelling, particularly surrounding floods, shaped the project's narrative structure. By converting raw algorithmic outputs to human-readable narratives, *Silsila* offers a deeply personal and creative experience.
More about Vohidov_9347
Events leading to ‘Silsila: Echoes of the Glacier's Weep’
2024: Global temperatures continue to rise, accelerating glacial melt in mountain regions.
2026: Increased frequency and intensity of flash floods reported in Tajikistan.
2028: Tajik government invests in early warning systems and disaster preparedness programs.
2029: Initial trials of machine learning models for flood prediction in Tajikistan.
2030: Community workshops integrate local knowledge with technological solutions.
2031: Refinement of predictive models based on real-world data and community feedback.
Imagined Future Scenarios Considered
Vohidov_9347 considered the following imagined future scenarios while working on this project
Energy companies will launch microgrids composed of community-owned renewable resources to provide energy backup to each other.
Extrapolated from How Energy Companies Can Build Resilience Into Their Operations - Forbes
Organizations use reinforcement learning to iteratively refine their scenario plans in response to ongoing feedback from the real world.
Extrapolated from Reinforcement Learning Explained - Substack.com
Hypothetical Product Ideas Considered
Vohidov_9347 considered the following hypothetical product ideas while working on this project
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