SPATIOTEMPORAL PREDICTION OF WATER QUALITY ACROSS MONITORING NETWORKS
DOI:
https://doi.org/10.69980/7shh2c51Keywords:
Water quality prediction, Spatiotemporal analysis, Machine learning, Environmental monitoring, Monitoring networksAbstract
Water quality prediction is a fundamental part of environmental monitoring, helpful in early detection of water quality shifts and better management of water resources. This work sought to create a spatiotemporal prediction model for predicting the water quality of the next day for the different monitoring networks in the study area based on previous physicochemical observations. The secondary dataset consisted of daily measurements from the various monitoring stations in Georgia, USA and was analyzed with machine learning methods. Key water quality variables included in the dataset were: pH, dissolved oxygen, water temperature, and specific conductance. Standard statistical and machine learning techniques were used to perform data preprocessing, exploratory spatiotemporal analysis, predictive model development and performance evaluation. The developed framework captured the spatial variability in water quality between monitoring stations and temporal changes in water quality, and yielded predictions that were accurate with good agreement between the actual and the predicted values. Dissolved oxygen, specific conductance and temperature were determined as primary contributors for prediction performance and identified through the feature importance analysis. The results show that the incorporation of spatial dependency and temporal information significantly enhances the ability to predict water quality, thus offering a valuable decision support system for environmental monitoring, pollution monitoring and sustainable water resource management.
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