Hackathon projects

List of hackathon projects

Hackathon projects solve real problems. Working as a team, use Data Science to solve challenges posed by partner companies.

Hackathon projects are an essential component of the Green Data Science master programme. Data Science problems proposed by companies or entities are worked on by teams of at least five students, providing real work experience. The word Hackathon comes from hacker+marathon, which means challenge to reach the goal!

Benefits of Hackathon Projects

  • work with real problems
  • train and apply acquired skills to complex problems
  • work in collaborative teams

I want to propose a data science problem!

If, at your company or entity, you have a data science problem, and you would like it to be solved by one of our hackathon teams, you can become a partner company or entity of the Green Data Science. See how at PARTNERSHIPS.

List of Hackathon projects

The following projects were developed by the Hackathon teams

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Raspberry harvest forecasting challenge

Partner: The Summer Berry Company Portugal
Domain: Agriculture
Year: 2023
Predicting the quantity of fruits that can be harvested weekly is essential to support the daily management of red fruit production in greenhouses. This value is important for planning the allocation of human resources necessary to carry out the harvest, predicting the quantity of fruit delivered to customers. In the case of red fruits, which cannot be stored and have a short shelf life, the accuracy of this prediction is essential. The Summer Berry Company (TSBCo) group produces small fruits throughout the year. Production is carried out in greenhouse tunnels, which allow stable conditions for plant growth. However, not all environmental factors can be controlled, especially meteorological ones. This may have implications for the speed of fruit development and quantity produced at each time. In this Hackathon, the challenge was launched to create a short and medium-term prediction model for the quantity of raspberries harvested. The project bases its analysis on the history of data collected by the company on agricultural holdings, which includes, among others, production data, characterization of crop phenological parameters, meteorological and environmental data. Team Members Afonso Marques, Aziza Ben Tanfous, Beatriz Cardoso, Diogo Pinto, Luís Soares, Miguel Paulo Faculty advisor Rui Figueira Partner advisor João Alves, Ana Morais github repository https://github.com/isa-ulisboa/greends-hack2023-smb

 

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Photo by Zach Inglis on Unsplash

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Prioritization of occurrences and allocation of means to combat forest fires

Partner: ANEPC – FEPC
Domain: Wildfire management
Year: 2023
Portugal is a country that has been severely affected by fires in recent decades, which generate high social, economic and environmental impacts. There are several factors that contribute to this reality: (1) territory with high rural abandonment, which combined with the low profitability of rural spaces, creates a flammable landscape, with high continuity and quantity of fuel; (2) favorable weather conditions, especially during summer, aggravated by climate change; (3) a high number of ignitions promoted by a culture of negligent use of fire. The fires that generate the largest burned area and negative impacts are usually concentrated in a period of 10-15 days. This fact introduces complexity into fire management, since large fires are often accompanied by simultaneity (i.e. several occurrences occurring at the same time) and high geographic dispersion across the national territory. In this context, fire suppression management is a complex task that requires knowledge, tools and concerted efforts from all involved. This must take into account the environmental conditions that influence the development of fires, but also the spatial, temporal and capacity heterogeneity of resources available for fighting. In order to improve the forest fire fighting system, it is important to increase the effectiveness of its response. This improvement may involve the promotion of anticipatory measures, which promote more effective management of resources, maintaining the balance of the combat system (i.e. avoiding its collapse). The possibility of predicting the type of event, based on the analysis of the history of occurrences, is an important element in supporting resource management decisions. In this Hackathon, the challenge is to predict the probability of a new forest fire event escaping the initial attack that allows it to be extinguished in 90 minutes, given the terrain and meteorological conditions associated with the occurrence. The aim is also to identify which factors determine a forest fire to escape this initial attack. For this project, historical data on occurrences in Portugal since 2018 is used, including operational data, meteorological data and geographic data. Team Members António Lacerda, Benjamin Hilliger, Christoph Fischer, Inês Silveira, Joana Esteves, Johanna Rauberger, Vasco Florentino Faculty advisor Akli Benali, Rui Figueira Partner advisor Fábio Silva, Alexandre Penha, Carlos Mota github repository https://github.com/isa-ulisboa/greends-hack2023-wildfire

 

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Photo by Egor Vikhrev on Unsplash

List of Hackathon workshops