Operational machine learning for park-scale irrigation to support urban cooling

TL;DR

This paper presents SIMPaCT, an operational system that uses sensor data and machine learning to optimize park irrigation for urban cooling, demonstrating robust, accurate control at city scale.

Contribution

The paper introduces an operational, sensor-driven irrigation management system that integrates machine learning and anomaly detection for cooling-focused urban park irrigation.

Findings

Mean absolute error of 0.78%, comparable to complex baselines

Lower P75 error for kNN compared to SARIMA (0.71% vs 0.93%)

Operates daily with proposed set-points for urban cooling

Abstract

Urban parks can mitigate local heat, yet irrigation control is usually tuned for water savings rather than cooling. We report on SIMPaCT (Smart Irrigation Management for Parks and Cool Towns), a park-scale deployment that links per-zone soil-moisture forecasts to overnight irrigation set-points in support of urban cooling. SIMPaCT ingests data from 202 soil-moisture sensors, 50 temperature-relative humidity (TRH) nodes, and 13 weather stations, and trains a per-sensor k-nearest neighbours (kNN) predictor on short rolling windows (200-900h). A rule-first anomaly pipeline screens missing and stuck-at signals, with model-based checks (Isolation Forest and ARIMA). When a device fails, a mutual-information neighbourhood selects the most informative neighbour and a small multilayer perceptron supplies a "virtual sensor" until restoration. Across sensors the mean absolute error was 0.78%, comparable to more complex baselines; the upper-quartile error (P75) was lower for kNN than SARIMA (0.71% vs 0.93%). SIMPaCT runs daily and writes proposed set-points to the existing controller for operator review. This short communication reports an operational recipe for robust, cooling-oriented irrigation at city-park scale.