Long-term Water Quality Prediction Based on Multimodal Fusion and Temporal 2D-variation

The global water environment confronts numerous challenges, e.g., water pollution, overexploitation, and ecological degradation. Comprehensive protection and management are imperative for sustainable water resource utilization. Water quality predictions provide timely warning of future water quality problems and enable early action to avoid deterioration. As science and technology are increasingly applied in comprehensive water environment management, a diverse array of multimodal data is gathered from various sources, including remote sensing images and hydrological time series. However, current water quality prediction methods, e.g., statistical, machine learning, and deep learning methods fail to utilize multimodal data to enhance their accuracy of water quality prediction. To solve the above problem, this work proposes a multi-factor and long-term water quality prediction model based on multimodal data fusion named Low-rank Multimodal Fusion TimesNet (LMF-TimesN et), It first extracts features from hydrological time series and remote sensing images, respectively. Then, they are fused with the low-rank multimodal fusion network to extract diverse information. Finally, TimesNet is adopted to integrate fused multimodal water environment information for water quality prediction. Experimental results on a real-world dataset show that LMF - TimesNet achieves higher prediction accuracy and generalization ability than its state-of-the-art peers.