Preprints
https://doi.org/10.5194/hess-2020-487
https://doi.org/10.5194/hess-2020-487

  19 Oct 2020

19 Oct 2020

Review status: this preprint is currently under review for the journal HESS.

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Herath Mudiyanselage Viraj Vidura Herath, Jayashree Chadalawada, and Vladan Babovic Herath Mudiyanselage Viraj Vidura Herath et al.
  • Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore

Abstract. Despite showing a great success of applications in many commercial fields, machine learning and data science models in general, show a limited use in scientific fields including hydrology. The approach is often criticized for lack of interpretability and physical consistency. This has led to the emergence of new paradigms, such as Theory Guided Data Science (TGDS) and physics informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles, in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on Genetic Programming (GP) namely Machine Learning Rainfall-Runoff Model Induction Toolkit (ML-RR-MI). ML-RR-MI is cable of developing fully-fledged lumped conceptual rainfall-runoff models for a watershed of interest using the building blocks of two flexible rainfall-runoff modelling frameworks (FUSE and SUPERFLEX). In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall-runoff models. This effort is motivated by the desire to address the decreasing meaningfulness of lumped models which tend to particularly deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties are significant. Henceforth, our machine learning approach for rainfall-runoff modelling titled Machine Induction Knowledge-Augmented System Hydrologique Asiatique (MIKA-SHA) captures spatial variabilities and automatically induces rainfall-runoff models for the catchment of interest without any subjectivity in model selection. Currently, MIKA-SHA learns models utilizing the model building components of FUSE and SUPERFLEX. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA’s model induction capabilities have been tested on the Red Creek catchment near Vestry, Mississippi, United States. The resulted model architectures through MIKA-SHA are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists.

Herath Mudiyanselage Viraj Vidura Herath et al.

 
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Herath Mudiyanselage Viraj Vidura Herath et al.

Herath Mudiyanselage Viraj Vidura Herath et al.

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Short summary
The main objective of this study is to blend the existing hydrological knowledge with the machine learning algorithm to induce readily interpretable distributed rainfall-runoff models. At present, the model building components of two flexible modelling frameworks (FUSE and SUPERFLEX) represent the elements of hydrological knowledge. The proposed toolkit captures spatial variabilities and automatically induces semi-distributed rainfall-runoff models without any subjectivity in model selection.