Incorporating Seasonal Trends for River Water Quality Prediction Models Using Deep Learning Algorithms
Keywords:
Deep Learning Architectures, Prediction Models, River Water Quality, Physicochemical Parameters, Seasonal VariationsAbstract
In recent years, numerous contaminants have posed significant threats to rivers, streams, and lakes. The ability to analyse and predict water quality has become crucial in combating water pollution. Various seasonal factors, along with physicochemical properties, influence water quality over time. As water quality data forms a time series, the values of parameters fluctuate with changing meteorological conditions across seasons at each location. Consequently, robust time series analysis is essential for accurate water quality forecasting. Given the effectiveness of Recurrent Neural Networks for time sequence data, this study aims to develop a water quality prediction model by learning seasonal patterns in the time series dataset. The dataset comprises 10,560 unique instances that describe both physicochemical and seasonal factors. Predictive models are developed using RNN and its variants, Gated Recurrent Unit and Long Short-Term Memory and evaluated for their performance. The results demonstrate that incorporating seasonal data alongside regular physicochemical properties during model training significantly enhances predictive accuracy. By leveraging the temporal patterns inherent in the dataset, the models achieve promising results, indicating that the inclusion of seasonal variability is beneficial for improving water quality predictions. This approach not only highlights the importance of considering seasonal influences in water quality analysis but also showcases the potential of advanced neural network architectures in environmental monitoring and management. The study underscores the need for comprehensive data collection and sophisticated modelling techniques to effectively anticipate and mitigate the impacts of water contamination.
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