← Back to all news
arXiv Machine Learning By Prabu, Fancy C, Suresh A, Srini Ramaswamy

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

Read the original on arXiv Machine Learning →

HydroSphere is a governed, data‑driven framework that enables real‑time monitoring, forecasting, treatment optimization, and fault recovery for wastewater infrastructure. It combines a hybrid TCN‑LSTM model for multi‑step water‑quality forecasting, a PPO‑based adaptive dosage optimization module, and a SHADE autoencoder for anomaly detection, all evaluated on 2.82 million measurements from 1940 to 2023. The system incorporates safety bounds, human oversight, and tiered governance to promote safer, more adaptive water management aligned with UN Sustainable Development Goals 6 and 13.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Sep 28

Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AI

SustainAI is a water‑aware, closed‑loop framework that embeds environmental accountability into AI deployment. It combines real‑time water metering, a hallucination‑aware penalty model, and a water‑aware routing algorithm that considers regional water stress. In tests with small language models, water footprints varied 11‑fold across data centers, and 1,335 inference runs consumed about 399 mL of water but yielded only 240 correct outputs, highlighting the resource cost of inaccurate responses.

By Farnaz Farid, Tashfia Towkee, Sania Nasreen, Sami bin Azad
llmssafety
More like this →
arXiv Machine Learning
Sep 2

Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints

The paper introduces the Mass‑Conserving Perceptron (MCP), a physics‑aware AI framework that enforces conservation laws while learning hydrological process relationships from data. By progressively adding physically meaningful components—such as bounded soil storage, state‑dependent conductivity, variable porosity, infiltration capacity, surface ponding, vertical drainage, and nonlinear water‑table dynamics—to a single MCP storage unit, the authors demonstrate that predictive skill for daily streamflow improves across 15 U.S. catchments. The study finds that the impact of each process representation varies with hydroclimate, with vertical drainage boosting performance in arid and snow‑dominated basins but hindering it in rainfall‑dominated ones, while surface ponding has minimal effect; the best MCP configurations rival LSTM benchmarks while retaining explicit physical interpretability.

By Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi, Muhammad Jawad, Sadaf Moghisi, Guo-Yue Niu
ragagentsbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 22

Predictors and Orchestrators: Parsimonious Machine Learning within an Agentic AI Harness for Multi-Horizon Karst Aquifer Forecasting

The study presents a deployment‑aware framework for forecasting spring discharge and groundwater levels in the Edwards Aquifer over 1‑12 week horizons using 79 years of hydroclimatic data. Five machine‑learning families—extreme gradient boosting, extremely randomized trees, LSTM, CNN, and Transformers—were compared, with extreme gradient boosting consistently delivering the highest reliability (R² ≥ 0.94) and strong agreement with operational drought thresholds. The validated models were integrated into a five‑agent operational architecture that automates data acquisition, model selection, prediction, threshold monitoring, verification, literature retrieval, and reporting.

By Pramod Lekhak, Chetan Sharma, Hakan Ba\c{s}a\u{g}ao\u{g}lu, F. Paul Bertetti, Debaditya Chakraborty
llmsagentssafety
More like this →