Flooding poses a significant threat to both rural and urban areas in South Africa, with devastating consequences for communities, infrastructure, and the economy. From 1981 to 2023, numerous floods were reported across the country, yet many events, especially in urban areas, were either underreported or not recorded with sufficient detail for further analysis or prediction work.
Traditional flood monitoring systems often rely on hydrological models that can miss localised events, especially in densely populated areas where infrastructure and topography can create microclimates. The challenge lies in accurately identifying the exact timing (to the day) and location of past floods using historical data, key to creating a comprehensive flood archive that can inform disaster preparedness and mitigation efforts.The goal of this challenge is to develop a machine learning model capable of identifying the specific day in which an urban flash flood occurred, based on aggregated historical CHIRPS daily precipitation data and a composite earth observation image of the area.
You will work with a dataset that includes reported flood locations from 1981 to 2023 in South Africa, with CHIRPS data aggregated within a 5km radius of each reported flood point.
In this challenge, you must determine the precise date a flood occurred and distinguish between time series with and without flood events. By successfully predicting the day a flood occurred, the model will contribute to building a complete historical catalogue of urban floods across South Africa. This catalogue will be implemented by the South African Environmental Observation Network (SAEON), providing critical data to identify previously missed flood events and to improve future flood forecasting and response strategies.
By creating a detailed archive of past urban floods, SAEON will have access to a powerful tool for analysing flood patterns, understanding the impact of urbanisation on flooding, and identifying vulnerable areas that may be at higher risk in the future. This archive can be used to improve early warning systems, optimise resource allocation during emergencies, and inform urban planning to reduce flood risk.
About Google DeepMind (deepmind.google)
Google DeepMind is a world-leading AI research lab with British heritage and an international team, committed to building AI responsibly, delivering scientific breakthroughs, and creating products that improve billions of lives. The unit’s breakthroughs over the last decade include AlphaGo - the first computer program to defeat a Go world champion, Transformers - neural networks that underpin all modern language models, AlphaFold - an AI system that predicts 3D models of protein structures enabling further scientific advancement, and Gemini, a family of versatile AI models built from the ground up for multimodality, seamlessly combining and understanding text, code, images, audio and video.
About South African Environmental Observation Network (SAEON) and South African Risk and Vulnerability Atlas (SARVA) (saeon.ac.za)
The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). Its three focus areas are environmental observation, data management, and education outreach. SAEON has a distributed network of seven nodes and two research infrastructures. The research network covers the major terrestrial and marine ecosystems in South Africa. The South African Risk and Vulnerability Atlas (SARVA) is hosted within SAEON's data management node, uLwazi. SARVA is an initiative of the Department of Science and Innovation Global Change Grand Challenge. The objective of SARVA is to profile the vulnerability of local municipalities and proactively strengthen the ability of the people of South Africa to cope with a range of natural and anthropogenic hazards (including climate change, biodiversity loss, and epidemics). SARVA achieves these objectives by providing open access to decision-ready data and translating the data and risk maps into digestible narratives for decision-makers using innovative decision-support tools, including indicator dashboards, infographics, and a searchable atlas.
The evaluation metric for this challenge is Log Loss.
Your submission should look like:
ID Target id_j7b6sokflo4k_X_0 0.73 id_j7b6sokflo4k_X_1 0.03
The values can be between 0 and 1. Do not set thresholds (or round your probabilities) to improve your place on the leaderboard. In order to ensure that the client receives the best solution Zindi will need the raw probabilities. This will allow the clients to set thresholds to their own needs.
🥇 1st Place: @Tomek_
🥈 2nd Place: Team Central Park
🥉 3rd Place: @3B
1st prize: $5 000 USD
2nd prize: $3 000 USD
3rd prize: $2 000 USD
If your solution places 1st, 2nd, or 3rd on the final leaderboard, you will be required to submit your winning solution code to us for verification, and you thereby agree to share your solution under a CC-BY SA 4.0 license.
There are 10 000 Zindi points available. You can read more about Zindi points here.
Competition closes on 16 February 2025.
Final submissions must be received by 11:59 PM GMT.
We reserve the right to update the contest timeline if necessary.
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This challenge is open to all.
Teams and collaboration
You may participate in challenges as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the challenge, minus the total number of submissions among team members at team formation. Prizes are transferred only to the individual players or to the team leader.
Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be immediately disqualified from the platform.
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Datasets and packages
The solution must use publicly-available, open-source packages only.
You may use only the datasets provided for this challenge. Automated machine learning tools such as automl are not permitted.
You may use pretrained models as long as they are openly available to everyone.
You are allowed to access, use and share challenge data for any commercial,. non-commercial, research or education purposes, under a CC-BY SA 4.0 license.
You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the challenge data, and work with Zindi to rectify any unauthorised transmission or access.
Your solution must not infringe the rights of any third party and you must be legally entitled to assign ownership of all rights of copyright in and to the winning solution code to Zindi.
Submissions and winning
You may make a maximum of 10 submissions per day.
You may make a maximum of 200 submissions for this challenge.
Before the end of the challenge you need to choose 2 submissions to be judged on for the private leaderboard. If you do not make a selection your 2 best public leaderboard submissions will be used to score on the private leaderboard.
During the challenge, your best public score will be displayed regardless of the submissions you have selected. When the challenge closes your best private score out of the 2 selected submissions will be displayed.
Zindi maintains a public leaderboard and a private leaderboard for each challenge. The public leaderboard includes approximately 30% of the test dataset. While the challenge is open, the public leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the challenge, the private leaderboard, which covers the other 70% of the test dataset, will be made public and will constitute the final ranking for the challenge.
Note that to count, your submission must first pass processing. If your submission fails during the processing step, it will not be counted and not receive a score; nor will it count against your daily submission limit. If you encounter problems with your submission file, your best course of action is to ask for advice on the Competition’s discussion forum.
If resource restrictions are indicated on the challenge page you must adhere to them.
If you are in the top 10 at the time the leaderboard closes, we will email you to request your code. On receipt of email, you will have 48 hours to respond and submit your code following the Reproducibility of submitted code guidelines detailed below. Failure to respond will result in disqualification.
If your solution places 1st, 2nd, or 3rd on the final leaderboard, you will be required to submit your winning solution code to us for verification, and you thereby agree to assign all worldwide rights of copyright in and to such winning solution to Zindi.
If two solutions earn identical scores on the leaderboard, the tiebreaker will be the date and time in which the submission was made (the earlier solution will win).
The winners will be paid via bank transfer, PayPal if payment is less than or equivalent to $100, or other international money transfer platform. International transfer fees will be deducted from the total prize amount, unless the prize money is under $500, in which case the international transfer fees will be covered by Zindi. In all cases, the winners are responsible for any other fees applied by their own bank or other institution for receiving the prize money. All taxes imposed on prizes are the sole responsibility of the winners. The top winners or team leaders will be required to present Zindi with proof of identification, proof of residence and a letter from your bank confirming your banking details. Winners will be paid in USD or the currency of the challenge. If your account cannot receive US Dollars or the currency of the challenge then your bank will need to provide proof of this and Zindi will try to accommodate this.
Please note that due to the ongoing Russia-Ukraine conflict, we are not currently able to make prize payments to winners located in Russia. We apologise for any inconvenience that may cause, and will handle any issues that arise on a case-by-case basis.
Payment will be made after code review and sealing the leaderboard.
You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this challenge constitutes your acceptance of these official challenge rules.
Zindi is committed to providing solutions of value to our clients and partners. To this end, we reserve the right to disqualify your submission on the grounds of usability or value. This includes but is not limited to the use of data leaks or any other practices that we deem to compromise the inherent value of your solution.
Zindi also reserves the right to disqualify you and/or your submissions from any challenge if we believe that you violated the rules or violated the spirit of the challenge or the platform in any other way. The disqualifications are irrespective of your position on the leaderboard and completely at the discretion of Zindi.
Please refer to the FAQs and Terms of Use for additional rules that may apply to this challenge. We reserve the right to update these rules at any time.
A README markdown file is required
It should cover:
Your code needs to run properly, code reviewers do not have time to debug code. If code does not run easily you will be bumped down the leaderboard.
Consequences of breaking any rules of the challenge or submission guidelines:
Monitoring of submissions
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