Analysis and forecasting of rivers pH level using deep learning

The pH level of oceans has been largely monitored and studied to make sure that aquatic ecosystems are thriving. However, the pH level of other large bodies of waters, such as rivers, has largely been glanced over. Many rivers contain very sensitive underwater ecosystems, and as a result even small pH changes can largely impact the relative biodiversity. With the addition of increased carbon emissions and pollution, large bodies of water are absorbing more carbon and consequently the pH levels of rivers can rapidly change. This paper studies different deep learning approaches to analyze and forecast pH levels, which include a long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and a Temporal Fusion Transformer (TFT) model to determine which algorithm provides the best pH predictive forecast. We demonstrate that the TFT outperforms other deep learning methods through various metrics. In addition, we clarify the importance of temperature as a feature in pH prediction. Lastly, we use the TFT to predict pH anomalies and discover the significance of the predicted data. We found that nine out of the ten predicted data sets have a significant difference compared to the original data.