Water-COLOR: Water-COnservation using a Learning-based Optimized Recommender

Efficient water use, particularly in the realm of irrigation, has emerged as a critical concern in regions suffering from persistent drought, such as California and Florida. With the advent of smart irrigation controllers encouraged by environmental policies, a new paradigm of water management is gaining traction. Among these, the Rachio smart controller has garnered significant attention. However, without direct feedback or actual water usage data, optimizing these irrigation systems for enhanced efficiency remains challenging. This paper introduces Water-COLOR, a novel recommendation system integrated within the Rachio smart controller's framework to address this challenge. The system leverages similar landscape profiles to suggest irrigation schedules that are both water-efficient and user-preferable. By analyzing manual user interactions with the controller, Water-COLOR infers user satisfaction, which, along with estimated water usage, informs the adaptation of irrigation plans. The system eschews the need for additional sensors, thereby reducing infrastructure requirements. Our evaluation demonstrates consistent performance across diverse climatic regions and indicates that the system's recommendations could significantly contribute to water conservation efforts. The results not only showcase the potential of Water-COLOR to enhance the efficiency of existing smart irrigation systems but also open avenues for deploying real-time, data-driven environmental solutions.