Snowcast Showdown: Development Stage Hosted By Bureau of Reclamation
Seasonal snowpack is a critical water resource throughout the Western U.S. Help the Bureau of Reclamation estimate snow water equivalent (SWE) at a high spatiotemporal resolution using near real-time data sources.
Model report template
Each report will consist of up to 8 pages, excluding visualizations and figures, submitted in PDF format through the Model Report Submission. Incorporating graphics is encouraged and does not count against the page limit. Winners will also be responsible for submitting any accompanying code (e.g., notebooks used to visualize data).
The template below provides guiding questions as you put together your report. You do not necessarily need to answer every question, though your report should sufficiently address each of the topics described below. These are the topics that judges will consider when evaluating reports.
Provide a concise summary of your solution, highlighting the key features and algorithm(s) underlying your solution, previous work that your solution builds on, and any novel or unique aspects of your solution.
- Describe the model(s) you used in your solution and why you selected them.
- What other models did you consider and/or test? Why did you choose not to use them?
- Does your solution incorporate any modifications or additions to a “standard” or “out of the box” model?
- Is there anything novel about how the selected algorithm was applied? If yes, please describe.
- What (raw) data sources does your solution use?
- Why did you select these particular data sources?
- Were other data sets considered and rejected? If so, please describe.
- What preprocessing steps did you take to develop features from raw data? This includes steps taken to clean and prepare raw data (e.g. reformatting raw data, filling missing values, removing outlier values, encoding categorial values, etc.), as well as steps taken to combine or transform raw or cleaned/processed data into features.
- Are there preprocessing steps you tested but did not end up using? If so, what were they and why were they ultimately not used?
- What features did you select for use in your model? What criteria or methods did you use to select these features?
- Are there any learnings, key insights, and/or visualizations worth sharing from your exploratory data analysis?
- What are the most important features? Do the features it considers important make scientific sense? What insights does this provide?
- Explain how your model makes its SWE estimates in a way that would be useful to a scientist. Please provide associated visualizations and metrics to support your explanation.
Training, validation, and testing
- How were available observations divided for training, validation, and testing (e.g. random sampling, stratified sampling by metadata like location, elevation, snow conditions)?
- What approach was used to validate your model (e.g. holdout set performance, k-fold cross-validation)?
- What metric(s) were used to evaluate model performance against validation and testing datasets?
- Summarize validation and testing results. Tables and/or visualizations summarizing model performance are strongly encouraged.
Performance across conditions
- How does your model performance vary by the following variables? Please provide associated visualizations and metrics to justify your explanations.
- SWE levels? (e.g. Is it more accurate for higher values? Are errors randomly distributed?)
- Time? (e.g. how does performance differ between peak snow and melt season)
- Conditions? (e.g. elevations, snow climate zones (coastal, intermountain, continental), seasonal climate conditions (dry, normal, wet years))
- What robustness checks were part of your training and validation process (e.g. checking for disparate error rates)?
- Describe under which conditions your model is the most accurate and under which conditions your model is the least accurate.
- How robust is your model to missing data and how is missing data handled (e.g. imputation)?
- Are there particular failure modes for your model (e.g. does high cloud cover get interpreted as snow)?
Considerations for use
- Please describe the machine specs that were used to train your model (e.g. number of GPUs, GPU memory, CPU memory, RAM).
- What simplifications could be made to run your solution faster without sacrificing significant accuracy?
- If you were to continue working on this problem for the next year, what methods or techniques might you try in order to build on your work so far? Are there other fields or features you felt would have been very helpful to have?
- Feel free to provide any additional notes or recommendations regarding your approach.
Snowcast Showdown: Development Stage: Rules and Terms of Data Use
DrivenData Competition Rules
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Private sharing of code
Privately sharing code or data outside of teams is not permitted.
Winner License Type: Open Source License
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT) in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
Winning solutions must be documented using the Winning Model Documentation Template provided to top-ranking participants in order to be eligible for recognition and prize money if offered.
The number of submissions per day is restricted to a fixed value on a per-competition basis. Any attempt to circumvent this limit will result in disqualification.
External data is not allowed unless otherwise approved explicitly on these competition pages. Participants agree to make no attempt to use additional data or data sources not provided on these competition pages.
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Selection of winners
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Data use and code sharing
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Limitations of liability
By participating in the Competition, each Participant agrees that: (1) any and all disputes, claims, and causes of action arising out of or in connection with the Competition, or any prize awarded, shall be resolved individually without resort to any form of class action; (2) any claims, judgments and awards shall be limited to actual out-of-pocket costs incurred, including costs associated with entering the Competition, but in no event attorney's fees; and (3) under no circumstances will a Participant be permitted to obtain any award for, and each Participant hereby waives all rights to claim, punitive, incidental or consequential damages and any and all rights to have damages multiplied or otherwise increased and any other damages, other than damages for actual out-of-pocket expenses.
DrivenData, Inc., Competition Sponsor, and the employees, officers, directors, shareholders, agents, representatives, affiliates, subsidiaries, and advertising, promotional and legal advisors thereof (collectively, the “Released Parties”) are not responsible and shall not be liable for: (i) late, lost, delayed, damaged, misdirected, incomplete, illegible, or unintelligible entries; (ii) telephone, electronic, hardware, or software program, network, Internet, or computer malfunctions, failures, or difficulties; (iii) errors in transmission; (iv) any condition caused by events beyond the control of the Released Parties that may cause the Competition to be disrupted or corrupted; (v) any injuries, losses, or damages of any kind caused by a prize or resulting from acceptance, possession, or use of a prize, or from participation in the Competition (however (but only if required by law in your jurisdiction), this release and hold harmless commitment does not apply to cases of bodily injury or loss of life to the extent that any death or personal injury is caused by the negligence of DrivenData, Competition Sponsor, or other third parties, where liability to the injured party cannot be excluded by law); or (vi) any printing or typographical errors in any materials associated with the Competition.
Disclaimer of Warranties
You understand that we cannot and do not guarantee or warrant that the Data or other files available for downloading from the internet or the Competition Website will be free of viruses or other destructive code. You are responsible for implementing sufficient procedures and checkpoints to satisfy your particular requirements for anti-virus protection and accuracy of data input and output, and for maintaining a means external to our site for any reconstruction of any lost data. RELEASED PARTIES WILL NOT BE LIABLE FOR ANY LOSS OR DAMAGE CAUSED BY A DISTRIBUTED DENIAL-OF-SERVICE ATTACK, VIRUSES OR OTHER TECHNOLOGICALLY HARMFUL MATERIAL THAT MAY INFECT YOUR COMPUTER EQUIPMENT, COMPUTER PROGRAMS, DATA OR OTHER PROPRIETARY MATERIAL DUE TO YOUR USE OF THE WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE OR TO YOUR DOWNLOADING OF ANY MATERIAL POSTED ON IT, OR ON ANY WEBSITE LINKED TO IT.
YOUR USE OF THE COMPETITION WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE AND RECEIPT OF ANY PRIZES IS AT YOUR OWN RISK. THE WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE ARE PROVIDED ON AN “AS IS” AND “AS AVAILABLE” BASIS, WITHOUT ANY WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, BEYOND ANY NON-EXCLUDABLE GUARANTEES (DEFINED BELOW). NEITHER THE RELEASED PARTIES NOR ANY PERSON ASSOCIATED WITH RELEASED PARTIES MAKES ANY WARRANTY OR REPRESENTATION WITH RESPECT TO THE COMPLETENESS, SECURITY, RELIABILITY, QUALITY, ACCURACY OR AVAILABILITY OF THE COMPETITION WEBSITE. WITHOUT LIMITING THE FOREGOING, NEITHER RELEASED PARTIES NOR ANYONE ASSOCIATED WITH THE RELEASED PARTIES REPRESENTS OR WARRANTS THAT THE COMPETITION WEBSITE, ITS CONTENT OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL BE ACCURATE, RELIABLE, ERROR-FREE OR UNINTERRUPTED, THAT DEFECTS WILL BE CORRECTED, THAT OUR SITE OR THE SERVER THAT MAKES IT AVAILABLE ARE FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS OR THAT THE COMPETITION WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL OTHERWISE MEET YOUR NEEDS OR EXPECTATIONS.
NOTHING IN THESE COMPETITION RULES LIMITS, EXCLUDES, OR MODIFIES OR PURPORTS TO LIMIT, EXCLUDE, OR MODIFY ANY STATUTORY CONSUMER GUARANTEE OR ANY IMPLIED CONDITION OR WARRANTY, THE EXCLUSION OF WHICH FROM THESE COMPETITION RULES WOULD CONTRAVENE ANY STATUTE OR CAUSE ANY PART OF THESE TERMS TO BE VOID (“NON-EXCLUDABLE GUARANTEES”). SUBJECT TO THE LIMITATIONS IN THE PRECEDING SENTENCE AND TO THE MAXIMUM EXTENT PERMITTED BY ANY MANDATORY PROVISIONS OF APPLICABLE LAW, THE RELEASED PARTIES HEREBY DISCLAIM ALL WARRANTIES OF ANY KIND, WHETHER EXPRESS OR IMPLIED, STATUTORY OR OTHERWISE, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR PARTICULAR PURPOSE.
THE FOREGOING DOES NOT AFFECT ANY WARRANTIES WHICH CANNOT BE EXCLUDED OR LIMITED UNDER APPLICABLE LAW.
Governing Law and Jurisdiction
DrivenData shall be the sole interpreter of these Competition Rules.
All matters relating to the Competition Rules or the Competition and any dispute or claim arising therefrom or related thereto (in each case, including non-contractual disputes or claims), shall be governed by and construed in accordance with the internal laws of the Commonwealth of Massachusetts without giving effect to any choice or conflict of law provision or rule (whether of the Commonwealth of Massachusetts or any other jurisdiction).
The invalidity or unenforceability of any provision of these Competition Rules shall not affect the validity or enforceability of any other provision. In the event that any provision is determined to be invalid or otherwise unenforceable or illegal, these Competition Rules shall otherwise remain in effect and be construed in accordance with their terms as if the invalid or illegal provision was not contained herein.