Predictive modeling coupled with wireless sensor networks for sustainable marine ecosystem management using real-time remote monitoring of water quality

Abstract Marine ecosystems, particularly in the Gulf of Aqaba, face growing threats from anthropogenic activities and industrial pollution, necessitating advanced monitoring for sustainable management. This study presents a real-time remote monitoring (RTRM) system that integrates wireless sensor networks and machine learning (ML) to enhance water quality assessment. By continuously capturing key parameters, dissolved oxygen (DO), pH, conductivity, turbidity, and sediment concentration, the system enables dynamic tracking of ecological health. Biosensors and physicochemical sensors were combined with geographic information system-based spatial analysis. At the same time, random forest and artificial neural network models were trained on 6 months of data and validated through 10-fold cross-validation, DO: RMSE = 0.45 mg/L, R 2 = 0.92. The RTRM system provided automated analytics and early warnings for environmental risks, including coral bleaching and pollutant spills. Results showed that DO levels generally supported aquatic life, though northern coastal areas were more vulnerable due to localized pollution. Turbidity and sediment patterns highlighted recreational disturbances, particularly from boating. Compared to traditional methods, the RTRM system improved predictive accuracy by 20% and reduced monitoring costs by 30%. By unifying in situ sensing, remote sensing, and ML-based forecasting, this framework offers a scalable, cost-effective tool for real-time marine ecosystem management in the Red Sea and comparable regions.