Hybrid modelling in hydrology by Neural Network-based prediction of conceptual model parameters

In recent years data-driven techniques, specifically LSTMs, have outperformed conceptual hydrological models for rainfall-runoff prediction. However, even though great progress has been made to explain the internal functioning of the model ((Kratzert, et al., 2019); (Lees, et al., 2022)), their interpretation is still not as straightforward as conceptual models. Additionally, latent variables, different from the target quantity, need postprocessing methods to be extracted. One way to combine the flexibility of data-driven techniques with the interpretability of conceptual models is the use of hybrid models. In our contribution,  we will present results from applying a similar technique as (Kraft, Jung, Korner, & Reichstein, 2020) and (Feng, Liu, Lawson, & Shen, 2022), in which an artificial neural network dynamically calculates the parameters of the conceptual model. This approach increases the model flexibility, allows the inclusion of multiple information sources, and compensates for model uncertainty, while maintaining the straightforward interpretability of the conceptual part. In this contribution, we will look at the performance of the hybrid model, analyze the parameter variation over time, and present a technique to avoid parameter cross-compensation.

 

References

Feng, D., Liu, J., Lawson, K., & Shen, C. (2022). Differentiable, learnable, regionalized process-based models with multiphysical outputs can approach state-of-the-art hydrologic prediction accuracy. Water Resources Research. doi:https://doi.org/10.1029/2022WR032404

Kraft, B., Jung, M., Korner, M., & Reichstein, M. (2020). HYBRID MODELING: FUSION OF A DEEP LEARNING APPROACH AND A PHYSICS-BASED MODEL FOR GLOBAL HYDROLOGICAL MODELING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1537--1544. doi:https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1537-2020

Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., & Klambauer, G. (2019). NeuralHydrology--Interpreting LSTMs in Hydrology. In W. Samek, G. Montavon, A. Vedaldi, L. Hansen, & K.-R. Müller, Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (pp. 347--362). Springer.

Lees, T., Reece, S., Kratzert, F., Klotz, D., Gauch, M., De Bruijn, J., . . . Dadson, S. (2022). Hydrological concept formation inside long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 3079-3101. doi:https://doi.org/10.5194/hess-26-3079-2022