aceData-driven machine learning approach for water quality anomaly detection and NPS-FM non-compliance risk prediction: A study with five New Zealand rivers
The National Policy Statement for Freshwater Management (NPS-FM) 2020 sets national water quality objectives for New Zealand's freshwater, but compliance is mostly assessed after the fact, and tools for anticipating which sites may be at greatest risk of future non-compliance remain limited. This study presents a machine learning pipeline that detects abnormal water quality events and quantifies NPS-FM non-compliance risk, applied to five South Island rivers of New Zealand spanning four land cover types. Anomaly…
A machine learning pipeline gave interpretable risk scores that separated one-off pollution events from persistent contamination and ranked five rivers by likelihood of failing national freshwater limits.
Temporal hold-out validation passed at only four of five sites, and the study covers just five South Island rivers, which limits how far the results generalise.
Evidence
- Peer-reviewedWater Research2026-10-17
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Truvace Impact Record TRV-2026-2084, v1: “Data-driven machine learning approach for water quality anomaly detection and NPS-FM non-compliance risk prediction: A study with five New Zealand rivers.” Truvace, 2026-10-21. /record/TRV-2026-2084 (accessed at citation time). sha256 5f32096a5ca269a1…
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