KBR Hackathon 2023
Harnessing Data to Safeguard Our Seas
\
Project Steps
- Data Collection and Cleaning:
Obtain historical data on fish populations, fishing activities, environmental factors, and other relevant variables.
Possible data sources:
- FAO (Food and Agriculture Organization of the United Nations) - They maintain a comprehensive database of information on many aspects of fisheries and aquaculture, including fish stocks.
- ICES (International Council for the Exploration of the Sea) - This organization provides fish stock assessment data, some of which is publicly accessible on their ICES Data Portal.
- RAM Legacy Stock Assessment Database - This is a compilation of stock assessment results for commercially exploited marine populations from around the world.
- FishBase - This is a global database with information on practically all fish species known to science.
- Data Analysis and Feature Engineering: Explore the data to understand the patterns and relationships between variables. Engineer features for the machine learning models based on findings from the data analysis.
- Development of Predictive Model: Choose appropriate model (or models) for predicting future fish populations. Train and validate the model using preprocessed data. Fine-tune the model to achieve the best performance.
- Development of Risk Assessment Model: Develop a model to predict high-risk areas based on historical data. Train, validate, and fine-tune this model as well.
- Integration of Models into a Dashboard: Design and build a user-friendly dashboard. Integrate models so users can interactively adjust parameters and see the results. Make sure to visualize the results in an intuitive way (e.g., maps, charts).
- Testing: Conduct thorough testing of the dashboard and the models. Ensure that the dashboard works correctly and provides accurate information.
- Documentation and Presentation: Documenting our work process, the choices we made, the performance of our models, etc. Prepare a presentation to showcase the project, explain how to use the dashboard, and discuss findings.
Project tasks
- #TODO: Find model for fish prediction
- #TODO: Find model for risk management
- #TODO: Find data sets
- historical fish populations
- water temperatures/acidity
- fish landing/consumption
- #TODO: Find Dashboards to model after
- #TODO: Find Python libraries that may exist
- #TODO: Find literature
Taskings
- Greg - Data, Dashboards
- Kaushik - Tableau interface, logos, production graphics
- Carson - Dashboard, data collection, financial lobbying piece
- Kim - Modeling of fisheries Chris - data collection