HydroSphere: A Framework for Governed, Self-Healing Wastewater Infrastructure

arXiv cs.LG Papers
wastewater-treatment time-series-forecasting reinforcement-learning anomaly-detection governance smart-infrastructure

Summary

The paper presents HydroSphere, a governed data-driven framework for real-time wastewater management combining a TCN-LSTM forecasting model, PPO-based adaptive chemical dosing, and SHADE autoencoder anomaly detection, evaluated on 2.82 million water-quality measurements.

arXiv:2610.08819v1 Announce Type: new Abstract: 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.
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# HydroSphere: A Framework for Governed, Self-Healing Wastewater Infrastructure
Source: [https://arxiv.org/abs/2610.08819](https://arxiv.org/abs/2610.08819)
[View PDF](https://arxiv.org/pdf/2610.08819)

> Abstract: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\.

## Submission history

From: Srini Ramaswamy \[[view email](https://arxiv.org/show-email/e1ec3652/2610.08819)\] **\[v1\]**Thu, 24 Sep 2026 17:18:48 UTC \(688 KB\)

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