HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning

• URL: http://arxiv.org/abs/2409.15213v1
• Date: Mon, 23 Sep 2024 16:57:43 GMT
• Title: HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning
• Authors: Naghmeh Shafiee Roudbari, Ursula Eicker, Charalambos Poullis, Zachary Patterson,
• Abstract summary: Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. We propose a hybrid graph learning structure that combines static and dynamic graph learning. Our method significantly reduces prediction error by an average of 10% across all days, with greater improvements for longer forecasting horizons.
• Score: 4.499833362998488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences the measurements at other stations. However, the dynamic and implicit nature of water flow paths makes it challenging to extract a priori knowledge of the connectivity structure. We hypothesize that terrain elevation significantly affects flow and connectivity. To incorporate this, we use LiDAR terrain elevation data encoded through a Vision Transformer (ViT). The ViT, which has demonstrated excellent performance in image classification by directly applying transformers to sequences of image patches, efficiently captures spatial features of terrain elevation. To account for both spatial and temporal features, we employ GRU blocks enhanced with graph convolution, a method widely used in the literature. We propose a hybrid graph learning structure that combines static and dynamic graph learning. A static graph, derived from transformer-encoded LiDAR data, captures terrain elevation relationships, while a dynamic graph adapts to temporal changes, improving the overall graph representation. We apply graph convolution in two layers through these static and dynamic graphs. Our method makes daily predictions up to 12 days ahead. Empirical results from multiple water stations in Quebec demonstrate that our method significantly reduces prediction error by an average of 10\% across all days, with greater improvements for longer forecasting horizons.

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