Year: 2026
Type: article
Abstract: Biofouling is the primary cause of maintenance and data gaps for online underwater water quality sensors, which are crucial for environmental compliance. This study proposes a novel method for predicting the likelihood and timing of biofouling events, moving beyond reactive sensor servicing and creating a proactive maintenance strategy. Utilising a comprehensive survival analysis dataset derived from time series data and service logs from 14 monitoring stations on the Fehmarnbelt Fixed Link project, we constructed a comprehensive survival dataset supported by Large Language Model (LLM) extraction of biofouling related events from unstructured service reports and quality-control notes. We evaluated multiple survival analysis models and found that a gradient boosted Cox proportional hazards model delivered the highest predictive performance (concordance index: 0.993). The model reliably identifies short-term fouling risk and provides decision support for optimising maintenance schedules. This model is highly promising as a decision support tool to optimise maintenance schedules, potentially reducing costs and environmental footprint while significantly extending sensor uptime.
Source: Digital Water
Authors: Jannik Elsäßer, Rocco Palmitessa
Institution: DHI
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204497883