Accurate prediction of water flow is of utmost importance, particularly for ensuring water supply and informing early actions for floods and droughts. Existing flow prediction methods rely on the input of weather drivers, which hinders their applicability to monitoring small headwater streams due to the limited spatial resolution of existing weather datasets. This paper introduces a new dataset with frequent imagery on streams for water monitoring tasks. Our objective is to automatically predict streamflow for each stream site using frequent images taken at a sub-hourly scale. To overcome the challenge of limited labels for certain stream sites, we employ knowledge transfer from well-observed sites to poorly-observed sites via domain adaptation. As each stream site involves highly variable time series data over long periods, we introduce a novel method STCGAN (Spatial-Temporal Cycle Generative Adversarial Network), which incorporates temporal context by conditioning on the sequence's time and learns overall trends of stream flow variation. It integrates the predictive modeling of streamflow with the cyclic generative process and enhances the prediction with data augmentation using generated synthetic samples. Our experiments demonstrate superior performance of the proposed method using data collected from the West Brook area located in western Massachusetts, US. The proposed method can be further extended to selectively combine information from multiple well-observed stream sites, leading to improved overall performance.

This paper proposes a meta-transfer-learning method for predicting daily maximum water temperature in stream networks with explicit modeling of extreme events. Accurate prediction of these extreme events is challenging because of their sparsity in the training data and their distinct responses to external drivers when compared to non-extreme observations. To overcome these challenges, we propose a sample reweighting strategy to escalate the importance of extreme events in the training process while preserving the predictive performance in normal time periods. The sample weight for each training data point is estimated as the similarity with the target test data point using contextual information and physical simulation. The obtained sample weight values are then used to fine-tune the initial model to transfer it to the test data. This method is further enhanced by an extreme value theory-based loss function to enforce the distribution of extreme data points and accelerated by a clustering algorithm based on the estimated similarities. Additionally, we introduce an online learning strategy to further refine the predictive model using newly collected observed data. The experimental results using real stream data from the Delaware River Basin over the past 36 years demonstrate that our meta-transfer-learning method produces more accurate predictions in both normal and extreme time periods when compared to baselines without the sample re-weighting scheme. The similarity learning method can reveal meaningful relationships amongst data points. We also show that the clustering algorithm can be used to accelerate the prediction while not compromising the predictive performance. The online learning strategy is shown to further improve predictive performance using recently observed data.

This paper introduces a new method for combining simulated data over different types of nodes in heterogeneous graphs to facilitate predictive learning. Simulation has been widely used in scientific domains to mitigate the need for a large number of observation samples. However, simulated data are often created separately for each type of physical systems while interactions amongst different types of systems remain unexplored. Our method is developed in the context of predicting water temperature in stream networks, which is critical for decision making in water management. In particular, we first develop a graph diffusion network (GDN) to model the interactions amongst stream segments and reservoirs in a heterogeneous graph. We use the GDN model to combine simulated data for both streams and reservoirs in the graph, and use the obtained composite simulations to train the GDN model in a semi-supervised manner. Then the GDN model is further fine-tuned using true observations. Since observation data are often sparse and localized, we further leverage the information from simulations to build a reweighting strategy so as to migitage the discrepancy between training and testing data. Our evaluations in the Delaware River Basin have shown the superiority of the proposed method over multiple baselines using either sparse or localized training data. The proposed GDN model also creates a better composite simulation dataset for heterogeneous graphs.