The preservation, monitoring, and control of water resources has been a major challenge in recent decades. Water resources must be constantly monitored to know the contamination levels of water. To meet this objective, this paper proposes a water monitoring system using autonomous surface vehicles, equipped with water quality sensors, based on a multimodal particle swarm optimization, and the federated learning technique, with Gaussian process as a surrogate model, the AquaFeL-PSO algorithm. The proposed monitoring system has two phases, the exploration phase and the exploitation phase. In the exploration phase, the vehicles examine the surface of the water resource, and with the data acquired by the water quality sensors, a first water quality model is estimated in the central server. In the exploitation phase, the area is divided into action zones using the model estimated in the exploration phase for a better exploitation of the contamination zones. To obtain the final water quality model of the water resource, the models obtained in both phases are combined. The results demonstrate the efficiency of the proposed path planner in obtaining water quality models of the pollution zones, with a 14$\%$ improvement over the other path planners compared, and the entire water resource, obtaining a 400$\%$ better model, as well as in detecting pollution peaks, the improvement in this case study is 4,000$\%$. It was also proven that the results obtained by applying the federated learning technique are very similar to the results of a centralized system.
翻译:近几十年来,水资源的保存、监测和控制一直是一项重大挑战; 水资源必须不断监测,以了解水的污染程度; 为实现这一目标,本文件提议使用自动地面车辆,配备水质传感器,以多式粒子堆积优化为基础,采用联邦学习技术,采用高森进程作为替代模型,AquaFel-PSO算法; 拟议的监测系统分为两个阶段,即勘探阶段和开发阶段; 在勘探阶段,车辆检查水资源表面,利用水质传感器获得的数据,在中央服务器上估计第一个水质模型; 在开发阶段,将该地区分为行动区,使用勘探阶段估计的模式,以更好地利用污染区; 为了获得水资源的最后水质模型,将这两个阶段获得的模型结合起来; 在勘探阶段,拟议路径规划员在获取污染区水质模型方面的效率为两个阶段; 在勘探阶段,车辆检查水资源表面,根据水质传感器获得的数据,在中央服务器上估算出第一个水质模型; 在开发阶段,将该地区分为行动区,使用模型,利用模型进行4000美元; 在研究过程中,通过测试,将40 000美元作为最佳做法,通过测试,以4 000美元为最佳做法,在中央污染系统取得更好的成果。