A Reinforcement Learning–Integrated AutoML Framework for Accurate Water-Quality Prediction

Automating unsupervised learning tasks remains a key challenge in the field The ecology, particularly the quality of the water, has suffered due to the world's population growth. Consequently, over the past ten years, water-quality prediction has been a popular topic.Due in large part to the lack of ground truth, current methods are not entirely accurate. The difficulties presented by unlabeled water-related data are addressed in this work by examining the incorporation of Reinforcement Learning (RL) into AutoML for unsupervised clustering. Intelligent and self-adaptive systems for evaluating unsupervised data can be created by integrating RL with AutoML, especially for applications involving water quality monitoring. Tasks including method selection, cluster number estimates, parameter optimization, and model assessment are especially difficult when labels are not present. In this work, we offer a method to address the lack of the ground truth problem: RL-AutoML chooses and optimizes clustering models and finds the optimal clustering methods by maximizing a reward function based on cluster separation and cohesiveness. Monitoring, evaluating, and managing water quality is made easier by this connection, which makes it possible to automatically classify water data..