Prabu P, Alluhaidan Ala Saleh, Aziz Romana, Basheer Shakila
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Sci Rep. 2025 May 31;15(1):19182. doi: 10.1038/s41598-025-99200-8.
This paper presents AquaFlowNet, a machine learning-based algorithm for real-time wastewater flow management. It addresses critical challenges related to operational efficiency, resource optimization, and environmental sustainability. Wastewater management systems require innovative methods for dynamic and efficient flow control to meet growing demands driven by urbanization, climate change, and increasingly stringent regulations. However, most existing methods rely on static or rule-based models, which lack the flexibility to handle fluctuating flow rates, variable environmental loads, and unforeseen disruptions. These limitations often lead to inefficiencies such as energy wastage, treatment delays, and overflow incidents, negatively impacting system performance and sustainability.AquaFlowNet leverages state-of-the-art machine learning algorithms to analyze real-time data from sensors, forecast flow variations, and optimize wastewater treatment processes. By integrating predictive analytics with intelligent control strategies, it enhances resource efficiency, prevents overflow events, and ensures regulatory compliance. Experimental evaluations demonstrate that AquaFlowNet outperforms conventional approaches in prediction accuracy and operational efficiency, reducing energy consumption, improving treatment effectiveness, and mitigating environmental impacts.The results highlight AquaFlowNet's potential to revolutionize wastewater management systems, making them more resilient, adaptive, and beneficial for urban and industrial applications.
本文介绍了AquaFlowNet，一种基于机器学习的实时废水流量管理算法。它解决了与运营效率、资源优化和环境可持续性相关的关键挑战。废水管理系统需要创新方法来进行动态高效的流量控制，以满足城市化、气候变化和日益严格的法规所带来的不断增长的需求。然而，大多数现有方法依赖于静态或基于规则的模型，这些模型缺乏处理流量波动、可变环境负荷和不可预见干扰的灵活性。这些限制常常导致能源浪费、处理延迟和溢流事件等效率低下的情况，对系统性能和可持续性产生负面影响。AquaFlowNet利用先进的机器学习算法来分析来自传感器的实时数据，预测流量变化，并优化废水处理过程。通过将预测分析与智能控制策略相结合，它提高了资源效率，防止溢流事件，并确保符合法规要求。实验评估表明，AquaFlowNet在预测准确性和运营效率方面优于传统方法，降低了能源消耗，提高了处理效果，并减轻了环境影响。结果突出了AquaFlowNet在彻底改变废水管理系统方面的潜力，使其更具弹性、适应性，对城市和工业应用更有益处。
