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Hydroinformatics Instruction Module Example Code: Introduction to Machine Learning with Residential Water Use Data
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Created: Jan 28, 2022 at 9:27 p.m.
Last updated: Apr 16, 2024 at 2:19 a.m.
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Abstract

This resource contains Jupyter Notebooks with examples that are an introduction to machine learning classification based on residential water use data. The resource is part of set of materials for hydroinformatics and water data science instruction. Complete learning module materials are found in HydroLearn: Jones, A.S., Horsburgh, J.S., Bastidas Pacheco, C.J. (2022). Hydroinformatics and Water Data Science. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:USU+CEE6110+2022/about.

This resources consists of 4 example notebooks and a data files.

Notebooks:
1. Example 1: Data import and exploration
2. Example 2: Implementing a first machine learning model
3. Example 3: Comparing multiple machine learning models
4. Example 4: Model optimization by hyperparameter tuning

Data files:
The data is contained in a flat file and is a record of water use data from a single residential property with manually applied labels to classify the water uses. Columns are:
- StartTime: Start date and time of each individual event. Format: 'YYYY-MM-DD HH:MM:SS'
- EndTime: End date and time of each individual event. Format: 'YYYY-MM-DD HH:MM:SS'
- Duration: Duration of each individual event (end time - start time). Units: Minutes
- Volume: Volume of water used in each individual event. Unit: Gallons
- FlowRate: Average flow rate of each individual event. Unit: Gallons per minute
- Peak: Maximum value observed in each 4-seconds period within each event. Unit: Gallons
- Mode: Most frequent value observed in an event. Unit: Gallons
- Label: Event classification. Values: faucet, toilet, shower, clotheswasher, bathtub

Subject Keywords

Content

Readme.md

This resource is part of a HydroLearn module for Hydroinformatics and Water Data Science.

Instructions for running code in the CUAHSI JupyterHub:

  1. Click on "Open with" at the top right
  2. Select CUAHSI JupyterHub and agree to terms of use
  3. Select Python as the Server Option
  4. Once JupyterHub opens, click to open the *.ipynb file for the example of interest
  5. Use the Jupyter tools and run code in each cell to retrieve data, generate plots, etc.
  6. Once the CUAHSI JupyterHub is launched, additional files associated with the resources may be opened directly (File -> Open)

Related Resources

This resource belongs to the following collections:
Title Owners Sharing Status My Permission
Hydroinformatics Instruction Modules Example Code Amber Jones  Public &  Shareable Open Access

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation Collaborative Research: Elements: Advancing Data Science and Analytics for Water (DSAW) 1931297

How to Cite

Bastidas Pacheco, C. J., A. S. Jones (2024). Hydroinformatics Instruction Module Example Code: Introduction to Machine Learning with Residential Water Use Data, HydroShare, http://www.hydroshare.org/resource/af6989bbbe344cb38118104bc39ea05b

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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