Summary of Data Optimisation Of Machine Learning Models For Smart Irrigation in Urban Parks, by Nasser Ghadiri et al.
Data Optimisation of Machine Learning Models for Smart Irrigation in Urban Parks
by Nasser Ghadiri, Bahman Javadi, Oliver Obst, Sebastian Pfautsch
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the Smart Irrigation Management for Parks and Cool Towns (SIMPaCT) project, which aims to optimize irrigation and induce physical cooling in urban environments affected by climate change. The SIMPaCT system uses machine learning models and advanced technologies to manage water scarcity and heat stress. The authors propose two novel methods to enhance the efficiency of the SIMPaCT system’s sensor network and applied machine learning models. These methods include clustering sensor time series data using K-shape and K-means algorithms to estimate readings from missing sensors, and sequential data collection from different sensor locations using robotic systems. The paper demonstrates significant improvements in the efficiency and cost-effectiveness of soil moisture monitoring networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SIMPaCT project aims to help cities like Sydney Olympic Park manage their water resources better. It uses special machines and math models to make irrigation more efficient. Two new ways are proposed to make this system even better: one is called clustering, which helps replace missing sensor readings, and the other is using robots to collect data from different places. This can reduce costs and make the system more accurate. |
Keywords
» Artificial intelligence » Clustering » K means » Machine learning » Time series
