Addressing Irregularities and Enhancing Forecasting for Water Quality Metrics by Integrating Ordinary Differential Equations with BiLSTM

Environmental monitoring data often have irregularities, like missing values or unevenly spaced observations. These irregularities are not just data anomalies; rather, they mirror the complexity and unpredictability of natural ecosystems and the challenges of collecting data in such settings. Preserving these irregularities is essential because artificial smoothing or interpolation can obscure critical insights and misrepresent natural processes. In this work, we propose a novel method that incorporates Neural Ordinary Differential Equations (Neural ODEs) with Long Short-Term Memory (LSTM)-based models, as well as feature engineering, to address these irregularities in water quality metrics forecasting. To ensure the authenticity of real-world dynamics, our methodology retains the dataset's inherent irregularities. While Long Short-Term Memory (LSTM)-based models excel in capturing sequential patterns and dependencies, Neural ODEs excel at handling uneven sampling by offering a continuous-time perspective. To make the models more adaptable, we incorporate features such as time differences between observations, timestamps, and other relevant water quality metrics. For this study, we use 30 years of data from five monitoring stations along Florida's Saint Lucie River, carefully maintaining the irregularities inherent in the dataset. This study shows that our proposed model significantly outperforms state-of-the-art Neural ODEs and recurrent architectures. This framework provides a reliable way to forecast water quality metrics while handling irregularities in time series forecasting, offering new insights and better predictions for environmental monitoring and management.