Published April 9, 2024
| Version v3
Publication
Open
Forecasting community water system outages
Creators
- 1. California Office of Data and Innovation
- 2. California State Water Resources Control Board, Division of Drinking Water
Description
The Division of Drinking Water at the California State Water Resources Control Board regulates 2866 Community Water Systems (CWS) throughout the State of California. Some of these CWS risk running out of water during the dry summer season. To address this problem, the Data Science Accelerator at the Office of Data and Innovation collaborated with the Division of Drinking Water to create a machine learning model that forecasts which CWS face the highest risk of running out of water.
Files
bobra-et-al-forecasting-community-water-outages-040924.pdf
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Additional details
Dates
- Created
-
2024-02-08Date of deposit
- Updated
-
2024-04-09Updated imagery/design
Software
- Repository URL
- https://github.com/cagov/aae-dsa-water
- Programming language
- Python
- Development Status
- Concept
References
- Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1), 47. https://doi.org/10.1103/RevModPhys.74.47
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20, 273-297. https://doi.org/10.1007/BF00994018
- Mothilal, R. K., Sharma, A., & Tan, C. (2020, January). Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 607-617). https://doi.org/10.1145/3351095.3372850
- Mullin, M. (2020). The effects of drinking water service fragmentation on drought-related water security. Science, 368(6488), 274-277. https://doi.org/10.1126/science.aba7353
- Fabian, P. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research 12, 2825. https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf