AI 中文总结
HydroSphere是一个数据驱动的污水管理框架，结合TCN-LSTM预测、PPO投药优化和SHADE异常检测，在282万条历史数据上验证，实现了预测和优化的性能提升，并支持可持续发展目标。
AI 中文摘要
快速工业化和城市增长正日益加剧水质和污水处理系统的压力，而传统处理厂往往依赖静态监测和控制策略，难以适应不断变化的污染物条件。本文提出HydroSphere，一个受治理的、数据驱动的框架，用于实时水质监测、预测、处理优化和故障恢复。HydroSphere使用1940年至2023年间收集的282万个水质测量数据进行评估。该框架整合了三个主要组件。首先，混合TCN-LSTM模型对七项水质参数进行多步预测，实现了0.1417的RMSE、0.1047的MAE和0.3596的R2。其次，自适应投药优化模块使用PPO强化学习调整化学药剂投加量，平均步奖励达到1.059，而固定剂量基线为1.017。结果还表明，无约束的奖励优化可能导致过量投药，这证明了明确操作保障措施的必要性。第三，SHADE异常检测模块使用深度自编码器识别传感器和过程异常，在受控故障注入下实现了0.651的F1分数。HydroSphere将这些能力与分层治理、确定性安全边界和人工监督相结合，以支持更安全、更具适应性的水基础设施。该框架为智能污水管理提供了可扩展的基础，并支持联合国可持续发展目标6和13。
英文摘要
Rapid industrialization and urban growth are increasing pressure on water quality and wastewater treatment systems, while conventional treatment plants often rely on static monitoring and control strategies that cannot easily adapt to changing pollutant conditions. This paper presents HydroSphere, a governed, data-driven framework for real-time water quality monitoring, forecasting, treatment optimization, and fault recovery. HydroSphere is evaluated using 2.82 million water-quality measurements collected between 1940 and 2023. The framework integrates three main components. First, a hybrid TCN-LSTM model performs multi-step forecasting across seven water-quality parameters, achieving an RMSE of 0.1417, MAE of 0.1047, and R2 of 0.3596. Second, the Adaptive Dosage Optimization Module uses PPO reinforcement learning to adjust chemical dosing, achieving a mean step reward of 1.059 compared with 1.017 for a fixed-dose baseline. The results also show that unconstrained reward optimization can lead to excessive dosing, demonstrating the need for explicit operational safeguards. Third, the SHADE anomaly detection module uses a deep autoencoder to identify sensor and process anomalies, achieving an F1 score of 0.651 under controlled fault injection. HydroSphere combines these capabilities with tiered governance, deterministic safety bounds, and human oversight to support safer and more adaptive water infrastructure. The framework provides a scalable foundation for intelligent wastewater management and supports the objectives of UN Sustainable Development Goals 6 and 13.
Comments16 pages, 5 tables and 9 figures - article to be submitted to a Journal / Conference
