Data-driven optimisation of multi-agency river water quality monitoring networks under high anthropogenic pressure.

Human activities put a lot of stress on rivers, so we need reliable and affordable water‑quality monitoring networks. This framework helps agencies improve their monitoring setups by using data‑driven methods to choose where to place sensors and how often to sample. We combine historic water‑quality records, spatial analysis, and statistical models to spot unnecessary stations and gaps in coverage. The guidance shows how to apply clustering tools like k‑means and spatial‑interpolation techniques inside a GIS to judge each station’s usefulness. The analysis focuses on key physical‑chemical variables—dissolved oxygen, pH, turbidity, and nitrate—to flag areas most at risk from industrial runoff and urban discharge. The resulting optimization model gives agencies clear

decision support, allowing them to allocate resources efficiently while keeping data quality high. Overall, the approach enables the design of scalable, cost‑effective monitoring plans for river basins under heavy human pressure, helping meet environmental standards and improve regional water‑resource management.

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