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

Integrating heterogeneous water quality data from multiple monitoring agencies into a single reproducible analytical pipeline remains an unsolved challenge in environmental science. We present the Optimised Water Quality Monitoring Framework (OWQMF), an open-source four-block pipeline that harmonises multi-source monitoring data, computes dual water quality indices with objectively derived weights using a game-theory ensemble of three objective weighting methods, produces gradient-boosting ensemble forecasts with walk-forward cross-validation, and selects the best forecasting model through multi-criteria consensus with Adaptive Conformal Prediction coverage guarantees ( $\ge$ 90%). Applied to 108,270 harmonised records from three contrasting South-eastern Brazilian river basins spanning four agencies (1977-2025), 18-31% of station-months receive discordant quality labels depending solely on which agency's classification scale is applied to the same index score, rising to 23-31% once nitrogen-species substitution and objective re-weighting are also taken into account. Among officially Classe 2 designated station-months, 74.1% conceal at least one per-parameter CONAMA 357 violation-95.5% among those classified as Good (IQA 52-78). The data-driven index redistributes weight systematically away from the low-variance parameters that the regulatory schedule fixes high-pH falls from 0.120 to 0.048, total solids from 0.080 to 0.038, and temperature deviation and total phosphorus also decline-and towards the parameters that discriminate between monitoring conditions. This direction of redistribution holds in every basin and under every weight-derivation variant tested; the ordering among the up-weighted parameters is implementation-dependent and is not claimed as a result. A single fixed national weight schedule cannot express this distinction between measuring compliance and ranking condition. Ensemble forecasting achieves mean absolute errors of 2.3-4.5 IQA units at clean-catchment and main-channel stations. Portability of the weighting scheme is confirmed on the Ohio River Basin, USA ( $n=13,230$ ); Italy ( $n=49,380$ ); and Ireland ( $n=13,380$ ) under WFD 2000/60/EC: IQA-equivalent medians cluster in a narrow 81.9-86.1 band while compliance rates span 12.7%-97.1%, demonstrating that composite scores systematically conceal regulatory non-conformity. Because the sub-index response curves were not regionally recalibrated, these external scores are interpreted comparatively rather than as regulatory assessments.