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Deep learning system monitors sewer overflows across cloud and edge

A new research paper details a web-based demonstrator for monitoring sewer overflows, integrating deep learning models for forecasting. This system is designed to operate resiliently across both cloud and edge computing environments, ensuring functionality even during network outages. The demonstrator aims to help anticipate capacity exceedance in combined sewer systems, enabling timely preventive actions against overflows. AI

影响 Provides a novel application of deep learning for environmental monitoring and resilience.

排序理由 The cluster contains a research paper detailing a new system. [lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

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  1. arXiv cs.AI TIER_1 English(EN) · Vipin Singh, Tianheng Ling, Peter Ghaly, Felix Grimmeisen, Gregor Schiele, Felix Biessmann ·

    A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

    arXiv:2605.10592v2 Announce Type: replace Abstract: Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows (CSO) with significant environmental and public health impacts. Forecasting …