Rainwater harvesting (RWH) is a decentralized water-supply strategy that supports water security, stormwater management, and climate resilience. Recent literature indicates that artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT)-enabled sensing can strengthen RWH by improving rainfall forecasting, optimizing storage and release, identifying suitable harvesting zones, and monitoring system performance in near real time. Smart cistern studies have shown that forecast-informed control can improve retained and detained stormwater volumes, while geospatial ML models have demonstrated strong performance in delineating rainwater harvesting suitability zones. At the same time, smart RWH architectures increasingly integrate sensors, wireless communication, and data-processing layers to detect leaks, monitor levels, and support automated decisions. This review synthesizes the current state of AI in RWH across planning, operation, monitoring, and optimization and concludes that the field is promising but still limited by data scarcity, modest field validation, and the need for more generalizable, low-cost, and explainable models.
📖 افتح في inklap 🔗 DOI 📮 اطلب بحثاً