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SRL Level
Resiliants: WATER aims to create a predictive dashboard for avocado...
Our predictive service uses satellite imagery and AI to forecast conditions favorable for cyanobacteria blooms. By analyzing factors like water temperature and nutrient levels, it provides timely risk assessments to help mitigate harmful outbreaks.
• Early Detection: Identify potential cyanobacteria blooms before they become visually apparent or pose a threat to water ecosystems.
• Environmental Risk Assessment: Evaluate and predict the risk of cyanobacteria proliferation based on dynamic environmental conditions.
• Public Health Protection: Safeguard public health by providing timely warnings about harmful algal blooms, allowing for preventive measures.
• Ecosystem Management: Assist in the proactive management of aquatic ecosystems by anticipating and addressing cyanobacteria outbreaks.
• Resource Optimization: Optimize the allocation of resources and response efforts through data-driven insights into bloom likelihood and severity.
• Water Quality Monitoring: Enhance water quality monitoring by integrating advanced satellite imagery and artificial intelligence for comprehensive assessments.
• Decision Support: Provide decision-makers with actionable information to implement preventive measures and mitigate the impact of cyanobacteria blooms.
• Environmental Conservation: Contribute to the conservation of water environments by preventing and minimizing the ecological damage caused by cyanobacteria proliferation.
• Sustainable Development: Support sustainable development practices by offering a proactive approach to managing and preserving aquatic ecosystems.
• Remote Sensing: Utilize satellite technology for remote sensing, enabling wide-scale monitoring and prediction of cyanobacteria bloom conditions across diverse water bodies.
See more information about this level and the TRL and SRL levels.
Our predictive service significantly contributes to an improved water measurement and monitoring system by utilizing advanced satellite imagery and artificial intelligence. The system enhances early detection and continuous surveillance of cyanobacteria blooms, offering a real-time assessment of water quality. This proactive monitoring is crucial for effective water resource management, ensuring early intervention in case of potential contamination. The integration of our predictive service provides a robust foundation for a more comprehensive and dynamic water measurement system.
The predictive service plays a pivotal role in supporting water recovery, recycle, and reuse technologies in industry and tourism. By identifying favorable conditions for cyanobacteria blooms, the service aids in preventing water contamination, which is crucial for industries and tourism sectors relying on water resources. The predictive insights allow for strategic planning, minimizing the risk of water pollution and supporting sustainable water reuse practices.
Our predictive service aligns seamlessly with the development of a methodology and integrated technology for measuring water quality and quantity in lakes, specifically Ohrid and Prespa. The AI-driven analysis of satellite imagery offers a comprehensive understanding of environmental factors influencing cyanobacteria blooms in these lakes. This integrated approach supports the accurate measurement of water quality parameters such as temperature, nutrient levels, and turbidity, contributing to a holistic assessment of lake health.
The predictive service promotes open data source accessibility by providing real-time information on cyanobacteria bloom conditions. This data accessibility is crucial for an operative unit on site, enabling timely decision-making and response strategies. The open nature of the data ensures transparency and collaboration among stakeholders involved in water management, fostering a more cooperative and informed approach to addressing water quality challenges.
Our predictive service acts as a physical multidimensional incubator for the region by addressing both technical and social environmental aspects. Technically, it enhances the region’s capability to monitor and manage water resources effectively. Socially, it facilitates community engagement through early warnings, promoting awareness of potential cyanobacteria-related risks. This multidimensional approach fosters a symbiotic relationship between technological advancements and community involvement, creating a holistic framework for sustainable water management in the region.
SCALIAN (Prime of the consortium) is a French group operating in seven countries. It offers three main areas of expertise (digital transformation, digital systems and operations performance) to its customers in various sectors (e.g., aeronautics, space, defense, energy). It also stands out for its investment in the development of artificial intelligence. WATERSHED MONITORING EUROPE (WME) is a French VSE founded in 2019 providing innovative technology solutions for water quality data storage and analysis and aims to support the collection, exploitation, and valorization of water data to produce sustainable, reliable, and useful knowledge that contributes to informed decision-making on water management issues. The feasibility study (2021) conducted by the same partners and financed by the European Space Agency, included a technical study, a demonstrator, and a market study. It was a successful first step validating the scientific approach, technical feasibility, economic viability, and scalability. It received the support of the CNES, several entities, and a label of France Water Team. The “Industrialization” component that we present here has also received two labels from France Water Team competitiveness cluster and Aerospace Valley labelling cluster . Finally, WME has been selected as one of the 15 finalists for the Space Innovation Challenge in Washington.
Cloud Cover and Satellite Visibility: Cloud cover can obstruct satellite visibility, affecting the accuracy of the imagery used for analysis. But the model is not mainly based on RGB data so the cloud cover would have a moderate impact.
• Limited Satellite Resolution: Satellite imagery may have limitations in spatial and spectral resolution, impacting the ability to detect subtle variations in water quality parameters.
• Local Environmental Factors: The model relies on historical data to predict cyanobacteria blooms, and local environmental factors may introduce unforeseen variables.
• Limited Data for Training: The effectiveness of the AI model is contingent on the availability and quality of training data.
Our predictive service uses satellite imagery and AI to forecast conditions favorable for cyanobacteria blooms. By analyzing factors like water temperature and nutrient levels, it provides timely risk assessments to help mitigate harmful outbreaks.
• Early Detection: Identify potential cyanobacteria blooms before they become visually apparent or pose a threat to water ecosystems.
• Environmental Risk Assessment: Evaluate and predict the risk of cyanobacteria proliferation based on dynamic environmental conditions.
• Public Health Protection: Safeguard public health by providing timely warnings about harmful algal blooms, allowing for preventive measures.
• Ecosystem Management: Assist in the proactive management of aquatic ecosystems by anticipating and addressing cyanobacteria outbreaks.
• Resource Optimization: Optimize the allocation of resources and response efforts through data-driven insights into bloom likelihood and severity.
• Water Quality Monitoring: Enhance water quality monitoring by integrating advanced satellite imagery and artificial intelligence for comprehensive assessments.
• Decision Support: Provide decision-makers with actionable information to implement preventive measures and mitigate the impact of cyanobacteria blooms.
• Environmental Conservation: Contribute to the conservation of water environments by preventing and minimizing the ecological damage caused by cyanobacteria proliferation.
• Sustainable Development: Support sustainable development practices by offering a proactive approach to managing and preserving aquatic ecosystems.
• Remote Sensing: Utilize satellite technology for remote sensing, enabling wide-scale monitoring and prediction of cyanobacteria bloom conditions across diverse water bodies.
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
Our predictive service significantly contributes to an improved water measurement and monitoring system by utilizing advanced satellite imagery and artificial intelligence. The system enhances early detection and continuous surveillance of cyanobacteria blooms, offering a real-time assessment of water quality. This proactive monitoring is crucial for effective water resource management, ensuring early intervention in case of potential contamination. The integration of our predictive service provides a robust foundation for a more comprehensive and dynamic water measurement system.
The predictive service plays a pivotal role in supporting water recovery, recycle, and reuse technologies in industry and tourism. By identifying favorable conditions for cyanobacteria blooms, the service aids in preventing water contamination, which is crucial for industries and tourism sectors relying on water resources. The predictive insights allow for strategic planning, minimizing the risk of water pollution and supporting sustainable water reuse practices.
Our predictive service aligns seamlessly with the development of a methodology and integrated technology for measuring water quality and quantity in lakes, specifically Ohrid and Prespa. The AI-driven analysis of satellite imagery offers a comprehensive understanding of environmental factors influencing cyanobacteria blooms in these lakes. This integrated approach supports the accurate measurement of water quality parameters such as temperature, nutrient levels, and turbidity, contributing to a holistic assessment of lake health.
The predictive service promotes open data source accessibility by providing real-time information on cyanobacteria bloom conditions. This data accessibility is crucial for an operative unit on site, enabling timely decision-making and response strategies. The open nature of the data ensures transparency and collaboration among stakeholders involved in water management, fostering a more cooperative and informed approach to addressing water quality challenges.
Our predictive service acts as a physical multidimensional incubator for the region by addressing both technical and social environmental aspects. Technically, it enhances the region’s capability to monitor and manage water resources effectively. Socially, it facilitates community engagement through early warnings, promoting awareness of potential cyanobacteria-related risks. This multidimensional approach fosters a symbiotic relationship between technological advancements and community involvement, creating a holistic framework for sustainable water management in the region.
SCALIAN (Prime of the consortium) is a French group operating in seven countries. It offers three main areas of expertise (digital transformation, digital systems and operations performance) to its customers in various sectors (e.g., aeronautics, space, defense, energy). It also stands out for its investment in the development of artificial intelligence. WATERSHED MONITORING EUROPE (WME) is a French VSE founded in 2019 providing innovative technology solutions for water quality data storage and analysis and aims to support the collection, exploitation, and valorization of water data to produce sustainable, reliable, and useful knowledge that contributes to informed decision-making on water management issues. The feasibility study (2021) conducted by the same partners and financed by the European Space Agency, included a technical study, a demonstrator, and a market study. It was a successful first step validating the scientific approach, technical feasibility, economic viability, and scalability. It received the support of the CNES, several entities, and a label of France Water Team. The “Industrialization” component that we present here has also received two labels from France Water Team competitiveness cluster and Aerospace Valley labelling cluster . Finally, WME has been selected as one of the 15 finalists for the Space Innovation Challenge in Washington.
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