Under the patronage of:

Journal Metrics

CiteScore: 1.41

More about CiteScore

Source Normalized Impact per Paper (SNIP): 0.488

SCImago Journal Rank (SJR): 0.305

SCImago Journal & Country Rank

Machine Learning on Minimizing Irrigation Water for Lawns

Original scientific paper

Journal of Sustainable Development of Energy, Water and Environment Systems
ARTICLE IN PRESS (volume, issue and page numbers will be assigned later)
DOI: (registered soon)
Weiqing Gu1 , Zhaocheng Yi2
1 Harvey Mudd College, 301 Platt Boulevard, Claremont, CA 91711, USA
2 Pitzer College, 1050 N Mills Ave, Claremont, CA 91711, USA


Conserving water has always been very important for California especially during drought seasons, due to the fact that California geographically consists most of the hot and dry deserts. The goal of this project is to create an automatic recommendation irrigation system for the purpose of minimizing water use but keeping lawn grass still green. In this project, a mathematical model has been developed and big data analytic techniques have been applied to achieve the goal using the lawns of Harvey Mudd College as an example. The major results of this project include a smart irrigation algorithm by taking weather conditions and human’s induced irrigation patterns and an application to notify the user the irrigation rate calculated automatically by the newly developed algorithm. The method here is scalable to different lawns whether owned by an individual or by an organization as long as there is historical irrigation data available. In conclusion, this method saves money, minimizes water pollution, and preserves water resources especially for drought regions in the world.

Keywords: Energy saving, Smart irrigation system, machine learning, data analytics, emerging needs, saving water, App

Creative Commons License
Views (in 2019): 45 | Downloads (in 2019): 35
Total views: 45 | Total downloads: 35