19 Leveraging AI Tools to Traditional Land-Based Knowledge for Water and Food Security and Ecological Justice: A Case Study Approach on Cumberland House

Abstract

The integration of traditional land-based knowledge and contemporary technologies offers an innovative approach to addressing environmental issues in Indigenous communities. This study explores the use of artificial intelligence (AI) techniques in leveraging the traditional ecological knowledge (TEK) of Cumberland House, the oldest Métis community in Western Canada, to promote water and food security, as well as environmental justice. This chapter utilizes a case study methodology to illustrate the effectiveness of two AI tools—Kepler.gl and H2O.ai—in tackling these difficulties. Kepler.gl is employed for participatory mapping and geospatial visualization, amalgamating TEK and environmental data to discern vital ecological patterns and communal resources. H2O.ai is utilized for predictive hydrological modelling and resource optimization, facilitating sustainable water governance and agricultural systems.

Introduction

The insight and worldview of Indigenous people are closely related to nature around them. The non-human-centric axiological philosophy of Indigenous people makes them believe that all objects of the environment are sacred and have energy (Kovach, 2021). Located in the Saskatchewan River Delta, the Cumberland House Village is the oldest Métis community in western Canada. The current water crises within the Cumberland House highlight the environmental issues that have been affecting the Saskatchewan River Delta for many generations. Studies show hydropower dams have the potential to alter the hydrology of the delta. Abu (2017) observes that from the 1960s, the operation of three large upstream dams, such as E. B. Campbell Dam, The Gardiner Dam and The Nipawin Hydro Dam, have affected the delta and its natural functions. These changes have had devastating impacts on several communities downstream, including Cumberland House (Abu, 2017). Some of these impacts are highlighted by unpredictable water levels, reduced access to harvesting sites, the destruction of livelihoods, and recently, reduced access to safe drinking water (Dorion & Paquin, 2013). “The political ontology of water” or water governance is involved here.

Despite considerable advancements in tackling environmental issues via modern technology, the incorporation of TEK into AI-based solutions is still insufficiently examined, especially concerning Indigenous people. TEK, grounded on thousands of years of experiential knowledge and responsibility towards the environment, provides essential insights for sustainable resource management (add a reference for TEK). Nevertheless, existing studies frequently neglect the integration of emerging technologies, such as generative AI, in tackling complex environmental challenges, including water and food security, as well as ecological justice.  Furthermore, there is an insufficient study on how particular AI technologies might be customized to reflect and enhance Indigenous perspectives while addressing localized environmental issues. The effects of hydroelectric dams and climate change on water levels, livelihoods, and traditional practices in communities such as Cumberland House are inadequately recorded concerning AI integration. Despite the increasing accessibility of geospatial tools such as Kepler.gl and predictive modelling platforms like H2O.ai, there exists a lack of practical frameworks illustrating their effectiveness in integrating TEK with environmental data to foster equitable and sustainable solutions. By using AI tools, we can spread information about environmental injustice to the general people. This communication will help these marginalized communities to raise their voice. It will also help policymakers to integrate Indigenous voices in water governance and water management.

This research aims to fill this gap by showcasing how AI tools can bridge traditional knowledge systems and modern technologies to enhance environmental justice, ecological resilience, and community-led governance.

The learning objectives of this research are to:

• Demonstrate the integration of traditional ecological knowledge (TEK) with AI tools for addressing environmental challenges.
• Evaluate the utility of Kepler.gl and H2O.ai in enhancing water security, food security and ecological justice.
• Develop collaborative frameworks that amplify Indigenous voices, foster ecological resilience, and bridge the gap between traditional practices and innovative technological solutions.

Case Study

This case study explores the practical application of two AI tools—Kepler.gl (kepler.gl) and H2O.ai—in addressing the environmental challenges faced by the Cumberland House community. By integrating TEK with modern AI technologies, this section demonstrates how these tools can support sustainable water management, food security, and ecological justice. Kepler.gl is employed for geospatial visualization and participatory mapping, enabling the integration of TEK with environmental data to identify critical ecological patterns and resource vulnerabilities. Conversely, H2O.ai is utilized for predictive hydrological modelling and resource optimization, providing data-driven insights to inform sustainable governance and resilience-building efforts. Through this approach, the case study highlights the potential of AI tools in advancing environmental justice and bridging traditional knowledge systems with innovative solutions.

Vignette

The youth of Cumberland House are concerned about the ongoing water issue.   They possess a keen awareness of environmental issues, the ramifications of global warming, and the contribution of the E.B. Campbell Dam to these impacts. They express dissatisfaction with the administration and the policies. An immense tension surrounded them as they perceived that affluent society and tourists in Saskatchewan garnered more attention than young Indigenous individuals. They discussed the absence of potable drinking water. Furthermore, adolescents are troubled by environmental contamination and its effects on their lives. They also discussed the pollutants present in the air and water resulting from agricultural practices, maritime activities, and other sources. They inquired if any of these toxins could originate from the dam. They briefly addressed oil spills, mostly concentrating on ones in Alberta that affect them as they flow down the river. They extensively discussed the effects of fire on animals, its contribution to respiratory ailments, and its reduction of oxygen levels in water, among other topics.

Additionally, they elucidated the E.B. Campbell dam and mining operations, as well as their impact on human organs, such as the heart, liver, and kidneys. They succinctly outlined some chronic ailments afflicting the community and examined the potential correlations between these conditions and their surrounding environment. They referenced the extinction of certain animals. Likewise, young individuals are apprehensive over their treaty rights. They are exceedingly exasperated by the unfulfilled treaty commitments. They recognized their desire for change but were uncertain about the method. They assert that despite their families’ potential anguish, it is their duty to remind the government of its treaty obligations and to reclaim those rights.

Young individuals are more susceptible to technology. They can incorporate their intergenerational knowledge with the help of AI technologies like Kepler.ai and H2O.ai. These tools enable them to comprehend the actual circumstances of the water problem in their community. They can also engage in lobbying for it. AI can serve as a conduit between contemporary knowledge and traditional land-based wisdom.

Figure 1
Environmentalists Working on Cumberland House 

Note. Kaneta Eya Lam-Lam (2024) generated this image using the H2O.ai platform. I dedicate any rights I hold to this image to the public domain via  CC0. 

Using Kepler.gl to Enhance Environmental Justice

Step 1: Prepare Data

• Identify Data Sources:

To conduct a comprehensive study, it is essential to gather TEK from the Cumberland House community. This includes identifying the locations of traditional harvesting sites, documenting patterns of seasonal water level variations, and recording observed ecological changes through time. Additionally, geospatial datasets may be collected, such as satellite imagery and hydrological information from sources like Google Earth Engine and NASA Earthdata, along with maps of the Saskatchewan River Delta. Historical data on flooding and water quality trends are also critical. Socio-economic data must be incorporated, focusing on community resource utilization patterns and demographic groups most affected by water and food insecurity.

• Format Data:

To facilitate effective analysis, datasets need to be prepared in compatible formats such as CSV, GeoJSON, Shapefile, or KML. The data needs to be organized with clearly defined fields, including latitude and longitude coordinates for geospatial referencing, time series data to enable temporal analysis, and categorical variables (e.g., traditional harvesting sites and water quality zones) to distinguish specific attributes and areas of interest.

Step 2: Import Data into Kepler.gl

• Open Kepler.gl (online or desktop version).
• Click “Add Data to Map” and upload the prepared datasets.
  • – Drag and drop the files directly or import them from a URL.
• Ensure that geospatial data points (latitude, longitude) are correctly plotted on the map.

Step 3: Customize Visualization Layers

• Create Layers for Different Data Types:
  • o Add a Point Layer to represent specific locations (e.g., harvesting sites, traditional knowledge markers).
  • o Use a Polygon Layer for outlining regions such as water bodies or delta zones.
  • o Incorporate a Heatmap Layer to visualize changes in water quality, harvesting activity, or food security zones.
• Use Time Controls:
  • o Enable temporal analysis by configuring time-series data (e.g., seasonal water changes over years).
  • o Create animations to show how hydrological and ecological factors have changed over time.
• Colour and Opacity Settings:
  • o Assign different colours to data categories (e.g., water quality zones, affected harvesting sites).
  • o Adjust opacity to layer multiple datasets without losing clarity.

Step 4: Integrate Community Input

• Participatory Mapping:
  • o Collaborate with community members to validate and annotate the map.
  • o Add geotagged notes or images representing local observations (e.g., markers for disrupted ecosystems).
• Highlight TEK:
  • o Use markers or labels to showcase traditional knowledge insights (e.g., seasonal patterns of flooding or species migration).

Step 5: Analyse Patterns

• Overlay Data:
  • o Compare TEK data with satellite imagery and hydrological data.
  • o Identify correlations, such as the relationship between dam operations and reduced access to harvesting sites.
• Run Visual Analyses:
  • o Use Kepler.gl’s filters to isolate specific variables (e.g., specific years, areas, or water quality indicators).
  • o Highlight hotspots where water insecurity overlaps with food insecurity.

Step 6: Generate Outputs

• Interactive Maps:
  • o Create interactive visualizations to communicate findings to stakeholders, policymakers, and the community.
  • o Use features like tooltips to provide detailed information for specific points.
• Animations:
  • o Generate time-lapse animations of water level changes or ecological impacts over time.
• Export Results:
  • o Export maps as HTML files for online sharing or as PNG images for reports and presentations.

Step 7: Share and Collaborate

• Present Findings:
  • o Use the interactive maps to present insights at community workshops or meetings.
  • o Include the Cumberland House community in discussions on interpreting the visualized data.
• Enable Feedback:
  • o Collect feedback from community members on the accuracy of visualizations and refine the maps as needed.

Step 8: Advocate for Policy Changes

• Support Advocacy:
  • o gl maps can be used to communicate the urgency of ecological justice and sustainable water management to policymakers.
  • o The integration of traditional knowledge with scientific data can be highlighted for credibility.
• Documentation:
  • o The maps and visualizations can be incorporated into policy briefs, research reports, and funding proposals.

By following these steps, Kepler.gl may help to bridge traditional knowledge and AI-driven insights, fostering collaborative solutions for water security, food security, and ecological justice in Cumberland House.

Figure 2
Residents of  Cumberland House Incorporating Traditional Ecological Knowledge with Technology  

Note. Kaneta Eya Lam-Lam (2024) generated this image using the H2O.ai platform. I dedicate any rights I hold to this image to the public domain via  CC0. 

Using H2O.ai to Enhance Environmental Justice

Integrating H2O.ai into research focused on leveraging traditional land-based knowledge for water security, food security, and ecological justice in Cumberland House involves several structured steps:

By following these steps, H2O.ai can be effectively utilized to bridge traditional ecological knowledge with advanced AI analytics, promoting sustainable solutions for water security, food security, and ecological justice in Cumberland House.

Collaborative Frameworks for Amplifying Indigenous Voices and Bridging Traditional and Technological Solutions

The following section illustrates the procedure of developing collaboration frameworks for ensuring the integration of TEK with AI techniques through promoting ecological resilience as well as acknowledging indigenous perspectives.

• Community-Centered Design

The participatory design methodology, guided by Indigenous knowledge holders, is crucial to the collaborative framework for the implementation of AI tools. In Cumberland House, this can be ensured through arranging seminars and knowledge-sharing events where community members can establish objectives, authenticate datasets, and collaboratively devise solutions with AI developers. This would ensure that the technologies conform to local specifications and cultural situations.

• Knowledge Exchange

This approach emphasizes capacity building by training community people in the use of AI technologies, including Kepler.gl and EJ GPT. This empowers individuals to independently engage with technological solutions and advocate for their rights in environmental governance. Simultaneously, AI practitioners gain insights into the principles and methodologies of TEK, fostering mutual respect and understanding.

• Cultural Preservation and Ethical AI Integration

This collaborative approach emphasizes ethical considerations, protecting TEK from misappropriation. This feature can enhance cultural preservation by utilizing AI tools to document and safeguard conventional practices and ecological narratives for future generations.

• Iterative Feedback and Co-Design

Ancient traditions can be connected with contemporary technical solutions through iterative feedback and co-design. For example, TEK insights on wildlife movement patterns are improved with AI-driven geospatial analysis, resulting in a holistic knowledge that neither method would achieve in isolation. This synthesis addresses urgent concerns and establishes the foundation for adaptable, long-term sustainability initiatives.

Figure 3:

Using Kepler.GL to Enhance Environmental Justice 

Note. Kaneta Eya Lam-Lam (2024) generated this image using the Canva platform. I dedicate any rights I hold to this image to the public domain via  CC0. 

Policy Contribution

This study contributes to policy by delineating a framework for integrating TEK with AI techniques to address environmental challenges. It underscores AI’s potential to enhance water and food security, ecological equity, and sustainable resource management. The study illustrates the efficacy of tools like Kepler.gl and EJ GPT, offering policymakers practical insights for integrating Indigenous knowledge into decision-making, promoting equitable governance, and meeting treaty obligations, thereby ensuring culturally sensitive and environmentally sustainable solutions for marginalized communities.

Summary

Conclusion 

This research highlights the essential importance of combining TEK with AI tools to address environmental issues in Indigenous communities. This study illustrates how Kepler.gl for geospatial visualization and H2O.ai for predictive modelling may facilitate sustainable water management, food security, and ecological justice in Cumberland House. The results underscore AI’s capacity to enhance indigenous voices, safeguard cultural heritage, and promote ecological resilience by integrating old approaches with novel solutions. The research emphasizes the necessity for culturally sensitive and community-focused methodologies in AI development, ensuring that Indigenous viewpoints inform the design and execution of technology solutions. This research adds to the increasing evidence that the integration of TEK and AI serves as an effective framework for attaining environmental sustainability and justice.

Future Research Directions 

Future research is needed to expand the breadth of AI integration within Indigenous contexts, emphasizing the development of adaptive tools that integrate various TEK systems. Comparative analyses across many ecosystems and cultural contexts can yield comprehensive insights into the scalability of AI applications such as Kepler.gl and H2O.ai. Furthermore, the integration of AI with emerging technologies, including Internet of Things (IoT) devices and blockchain, may improve real-time monitoring and transparency in resource management. An in-depth investigation into participatory AI design involving Indigenous populations as co-creators of technology is crucial for cultivating trust and sustaining long-term collaboration. Eventually, research focused on policy is essential to convert these technological innovations into practical tactics, ensuring that AI-driven solutions conform to ecological justice as well as equitable resource governance frameworks.

Open Researcher and Contributor ID (ORCID)

Sabbir Ahmed Chowdhury: https://orcid.org/0000-0003-1652-3204
Institute of Education & Research, University of Dhaka, Bangladesh

Kaneta Eya Lam-Lam: https://orcid.org/0009-0008-8749-3763
University of Saskatchewan

References

Abu, R. (2018). Knowledge, use, and change in the Saskatchewan River Delta: Assessing the changing livelihoods of Cumberland House Métis and Cree Nation. Environmental Science, Sociology, Geography.

Dorion, L., & Paquin, T. (2013). Cumberland House. In Metis Museum. https://www.metismuseum.ca/media/document.php/00717.Cumberland%20House.pdf

Hart, M. A. (2010). Indigenous worldviews, knowledge, and research: The development of an Indigenous research paradigm. Journal of Indigenous Social Development, 1(1A).

Kimmerer, R. W. (2002). Weaving traditional ecological knowledge into biological education: a call to action. BioScience, 52(5), 432-438.

Stahl, B. C. (2021). Artificial intelligence for a better future: an ecosystem perspective on the ethics of AI and emerging digital technologies. Springer Nature.