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Interagency Response Frameworks for Real Time Urban Water Quality Crises

Tanveer Ahmad WaniDepartment of Physics, Noida International University, Uttar Pradesh 203201, IndiaSyed Mohd Uzair IqbalSymbiosis Centre for Advanced Legal Studies and Research, Symbiosis Law School, Symbiosis International (Deemed University), Pune, Maharashtra, IndiaVinayak KaleDepartment of Civil Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Dr. D. Y. Patil Dnyan Prasad University, Pune, Maharashtra, IndiaBekchanova Mokhirakhon Khudaybergan qiziDepartment of Biology, Urgench State University, UzbekistanDivyani HarpalDepartment of Civil Engineering, Tulsiramji Gaikwad Patil College of Engineering and Technology, Nagpur, Maharashtra 441108, IndiaG. SasikalaDepartment of Civil Engineering, S.R.K.R.Engineering College, Bhimavaram, Andhra Pradesh-534203, India
Waterlinesjournal2025en
ABI

Abstract

This paper presents an integrated, operations-focused schema that links real-time urban water quality detection to tiered, interagency response. Urban systems face transient shocks and flood-driven contamination while detection platforms have advanced faster than coordination protocols, leaving triggers, roles, and outcomes weakly specified, especially for resource-constrained utilities. We synthesize existing protocols and incident taxonomies into standardized nodes, triggers, and message artifacts; fuse Supervisory Control and Data Acquisition (SCADA), online water-quality sensors, laboratory confirmations, hydraulic models, and Synthetic Aperture Radar (SAR)-optical flood mapping on Google Earth Engine (GEE); ingest probabilistic rainfall forecasts; and calibrate detectors using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Percent bias. Evaluation reports detection-to-decision latency, trigger rates by type, and coordination indicators, with comparative tests against linear and threshold-only escalation quantifying speed-verification trade-offs under telemetry loss and staffing stress; quantitative gains are context dependent and subject to data coverage limits. The schema standardizes interoperable triggers, role assignments, minimal data fields, and 15-minute bulletin targets, and embeds after-action reviews and quarterly robustness audits to support adaptive learning.

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