用聚类与机器人巡检优化城市公园灌溉传感器网络，降本增效。
Data Optimisation of Machine Learning Models for Smart Irrigation in Urban Parks
- 通过K-shape/K-means聚类补全缺失传感器数据，提升连续性。
- 机器人模拟采集使圆形路径误差降17.2%，线性路径降2.1%。
- 适合智慧农业、城市节水系统研发者参考。
城市受气候变化影响，面临极端高温、干旱和水资源短缺，威胁公共健康与社区福祉。悉尼奥林匹克公园依赖澳大利亚最大的灌溉系统之一，其智能灌溉管理项目（SIMPaCT）自2021年起运用机器学习优化灌溉与降温。本文提出两种新方法：一是基于K-shape和K-means算法对传感器时间序列聚类，用于估算缺失数据，可检测异常、修正源数据并识别冗余传感器，降低维护成本；二是采用机器人系统按序采集不同位置传感器数据，大幅减少固定传感器数量。两项方法协同实现土壤湿度预测精度提升，同时优化部署与运维。评估显示，聚类补全使平均误差降低最高达5.4%；机器人模拟采集在圆形路径和线性路径上分别降低平均误差17.2%和2.1%。
原文摘要 · Abstract (English)
Urban environments face significant challenges due to climate change, including extreme heat, drought, and water scarcity, which impact public health, community well-being, and local economies. Effective management of these issues is crucial, particularly in areas like Sydney Olympic Park, which relies on one of Australia's largest irrigation systems. The Smart Irrigation Management for Parks and Cool Towns (SIMPaCT) project, initiated in 2021, leverages advanced technologies and machine learning models to optimize irrigation and induce physical cooling. This paper introduces two novel methods to enhance the efficiency of the SIMPaCT system's extensive sensor network and applied machine learning models. The first method employs clustering of sensor time series data using K-shape and K-means algorithms to estimate readings from missing sensors, ensuring continuous and reliable data. This approach can detect anomalies, correct data sources, and identify and remove redundant sensors to reduce maintenance costs. The second method involves sequential data collection from different sensor locations using robotic systems, significantly reducing the need for high numbers of stationary sensors. Together, these methods aim to maintain accurate soil moisture predictions while optimizing sensor deployment and reducing maintenance costs, thereby enhancing the efficiency and effectiveness of the smart irrigation system. Our evaluations demonstrate significant improvements in the efficiency and cost-effectiveness of soil moisture monitoring networks. The cluster-based replacement of missing sensors provides up to 5.4% decrease in average error. The sequential sensor data collection as a robotic emulation shows 17.2% and 2.1% decrease in average error for circular and linear paths respectively.
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