Deep learning rainfall-runoff predictions of extreme events

Frame, Jonathan; Kratzert, Frederik; Klotz, Daniel; Gauch, Martin; Shelev, Guy; Gilon, Oren; Qualls, Logan M.; Gupta, Hoshin; Nearing, Grey

doi:10.5194/hess-2021-423

Cited by 18 publications

(23 citation statements)

References 23 publications

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“…They found that various hydrometeorological and geological characteristics impacted the simulation accuracy and recommended continued work to increase trust in the use of ML technology in hydrology. Frame and colleagues (2021) aimed to understand whether process-based models are more reliable for predicting extreme events (in this case, streamflow) as compared to data-driven models [8]. This work demonstrated that under these testing conditions the data-driven models (including physics-informed ML model and purely ML model) performed better than process-based models at predicting peak flows under a variety of conditions, including extreme events.…”

Section: Introductionmentioning

confidence: 99%

Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning

Gallagher

Williams

Lazzeri

et al. 2021

Water

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Hydrologists and water managers increasingly face challenges associated with extreme climatic events. At the same time, historic datasets for modeling contemporary and future hydrologic conditions are increasingly inadequate. Machine learning is one promising technological tool for navigating the challenges of understanding and managing contemporary hydrological systems. However, in addition to the technical challenges associated with effectively leveraging ML for understanding subsurface hydrological processes, practitioner skepticism and hesitancy surrounding ML presents a significant barrier to adoption of ML technologies among practitioners. In this paper, we discuss an educational application we have developed—Sandtank-ML—to be used as a training and educational tool aimed at building user confidence and supporting adoption of ML technologies among water managers. We argue that supporting the adoption of ML methods and technologies for subsurface hydrological investigations and management requires not only the development of robust technologic tools and approaches, but educational strategies and tools capable of building confidence among diverse users.

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Section: Introductionmentioning

confidence: 99%

Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning

Gallagher

Williams

Lazzeri

et al. 2021

Water

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“…Machine learning methods provide great versatility (Shen, 2018;Shen et al, 2018;Reichstein et al, 2019) and have demonstrated unprecedented accuracy in various modelling tasks like predictions in un-gauged basins (PUB, e.g. Kratzert et al, 2019b;Prieto et al, 2019), in transfer learning to data-scarce regions (Ma et al, 2021) or flood forecasting (Frame et al, 2021;Nevo et al, 2021).…”

Section: Machine Learning In Hydrologymentioning

confidence: 99%

“…Therefore, it becomes more and more inaccurate to label machine learning methods as black boxes since techniques exist that shed light on the internals of machine learning methods (see also Nearing et al, 2021;Frame et al, 2021) -turning them toward so-called grey box models. Yet, internal investigation of machine learning models relies on additional methods that come with their own assumptions and caveats, and the current straight-forward interpretability of conceptual models serves as benchmark in the hydrologic community.…”

Section: Machine Learning In Hydrologymentioning

confidence: 99%

“…Yet, internal investigation of machine learning models relies on additional methods that come with their own assumptions and caveats, and the current straight-forward interpretability of conceptual models serves as benchmark in the hydrologic community. Much environmental research is dedicated toward extrapolation in space, in time and of boundary conditions, in order to investigate extreme events (Frame et al, 2021), climate change projections (Nearing et al, 2019) and so on. In all these fields ease of interpretability is desirable.…”

Section: Machine Learning In Hydrologymentioning

confidence: 99%

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Improving hydrologic models for predictions and process understanding using Neural ODEs

Höge

Scheidegger

Baity‐Jesi

et al. 2022

Preprint

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Abstract. Deep learning methods have frequently outperformed conceptual hydrologic models in rainfall-runoff modelling. Attempts of investigating the internals of such deep learning models are being made but traceability of model states and processes and their interrelations to model input and output is not yet fully given. Direct interpretability of mechanistic processes has always been considered as asset of conceptual models that helps to gain system understanding aside of predictability. We introduce hydrologic Neural Ordinary Differential Equation (ODE) models that perform as well as state-of-the-art deep learning methods in stream flow prediction while maintaining the ease of interpretability of conceptual hydrologic models. In Neural ODEs, internal processes that are represented in differential equations are substituted by neural networks. Therefore, Neural ODE models enable fusing deep learning with mechanistic modelling. We demonstrate the basin-specific predictive performance for several hundred catchments of the continental USA. For exemplary basins, we analyse the dynamics of states and processes learned by the model-internal neural networks. Finally, we discuss the potential of Neural ODE models in hydrology.

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“…The method can be applied to either physically based, process-based and data-based deterministic 10.1029/2021WR031215 3 of 20 prediction/simulation models. It can also be applied in conjunction with prediction models based on deep learning, which are gaining increasing popularity for hydrological predictions (see, for instance, Frame et al, 2021).…”

mentioning

confidence: 99%

Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions

Koutsoyiannis

Montanari

2022

Water Resources Research

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We present a new method for simulating and predicting hydrologic variables with uncertainty assessment and provide example applications to river flows. The method is identified with the acronym “Bluecat” and is based on the use of a deterministic model which is subsequently converted to a stochastic formulation. The latter provides an adjustment on statistical basis of the deterministic prediction along with its confidence limits. The distinguishing features of the proposed approach are the ability to infer the probability distribution of the prediction without requiring strong hypotheses on the statistical characterization of the prediction error (e.g., normality, homoscedasticity), and its transparent and intuitive use of the observations. Bluecat makes use of a rigorous theory to estimate the probability distribution of the predictand conditioned by the deterministic model output, by inferring the conditional statistics of observations. Therefore Bluecat bridges the gaps between deterministic (possibly physically based, or deep learning‐based) and stochastic models, as well as between rigorous theory and transparent use of data with an innovative and user oriented approach. We present two examples of application to the case studies of the Arno river at Subbiano and Sieve river at Fornacina. The results confirm the distinguishing features of the method along with its technical soundness. We provide an open software working in the R environment, along with help facilities and detailed instructions to reproduce the case studies presented here.

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Deep learning rainfall-runoff predictions of extreme events

Cited by 18 publications

References 23 publications

Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning

Sandtank-ML: An Educational Tool at the Interface of Hydrology and Machine Learning

Improving hydrologic models for predictions and process understanding using Neural ODEs

Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions

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