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2024
Journal Article
Titel
Probabilistic multi-step ahead streamflow forecast based on deep learning
Alternative
Probabilistische mehrstufige Vorhersage des Abflusses auf der Grundlage von Deep Learning
Abstract
The use of deep learning methods for fluvial flood forecasting is rapidly gaining traction, offering a promising solution to the challenges associated with accurate yet time-consuming numerical models. This paper presents two physics-inspired deep learning approaches specifically designed for fluvial flood forecasting, each embracing different learning principles: centralized and federated learning. The centralized model utilizes an Encoder-Decoder technique to handle input data of varying types and scales, while the federated model is based on a node-link graph with a seq2seq internal model. Both models are enhanced with a probabilistic forecasting head to account for the inherent uncertainty in streamflow forecasts. The objective of these approaches is to address the limitations of traditional numerical models while leveraging the potential of deep learning to improve the speed, accuracy, and scalability of flood forecasting. To validate their effectiveness, the models were tested across different use cases. The findings from the federated learning approach emphasize the importance of catchment clustering before model utilization and demonstrate the models’ ability to generalize effectively in catchments with similar properties. On the other hand, the results of the centralized method highlight the model’s reliance on the test set falling within the data range of the training set (Average NSE and KGE for the sixth hour ahead of 0.88 and 0.78, respectively). To address this limitation, the paper suggests the development of a method for the future, such as leveraging a numerical model or using Generative Adversarial Networks, to generate highly extreme events, particularly in the context of a changing climate. The models are implemented in a flexible operational framework based on open standards, ensuring their adaptability and usability in various settings.
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Dieser Beitrag präsentiert zwei Deep-Learning-Ansätze für die Vorhersage von Hochwassern: zentrales und föderiertes Lernen mit probabilistischem Vorhersagekopf. Das zentrale Modell verwendet eine Encoder-Decoder-Technik, um Eingabedaten unterschiedlicher Typen und Skalen zu verarbeiten, während das federated Modell auf einem Knoten-Link-Graphen mit einem seq2seq-Internen Modell basiert. Die Modelle überwinden Einschränkungen traditioneller Modelle und verbessern Geschwindigkeit, und Skalierbarkeit. Validierung in verschiedenen Anwendungsfällen zeigt die Effektivität der Methoden. Methoden wie die Nutzung von Generative Adversarial Networks werden vorgeschlagen, um extreme Ereignisse zu generieren. Die Modelle sind flexibel und benutzerfreundlich in einem operativen Framework implementiert.
Author(s)