MIKA-SHA builds and tests many model configurations using the model building components of the two flexible modelling frameworks and quantitatively identifies the optimal model for the watershed of concern. MIKA-SHA's model induction capabilities have been tested on the Rappahannock River basin near Fredericksburg, Virginia, USA. Proposed framework can be coupled with any internally coherent collection of building blocks.
Currently, MIKA-SHA learns models utilizing the modelīuilding components of two flexible modelling frameworks. Rainfall–runoff models for the catchment of interest without any explicit MIKA-SHA captures spatial variabilities and automatically induces This was the motivation behind developing our machine learning approach for distributed rainfall–runoff modelling titled Machine Induction Knowledge Augmented – System Hydrologique Asiatique (MIKA-SHA). The meaningfulness and reliability of hydrological inferences gained from lumped models may tend to deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties is significant. In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall–runoff models. Rainfall–runoff models for a watershed of interest using the building blocks of two flexible rainfall–runoff modelling frameworks. ML-RR-MI is capable of developing fully fledged lumped conceptual Model induction framework was founded on genetic programming (GP), namely the Machine Learning Rainfall–Runoff Model Induction (ML-RR-MI) toolkit. The same principles in our prior work (Chadalawada et al., 2020), a new
To improve the physical meaningfulness of machine learning models byīlending existing scientific knowledge with learning algorithms. New modelling paradigms, such as theory-guided data science (TGDS) and
Interpretability and physical consistency. The approach is often criticized for its lack of Limited success in many scientific fields, including hydrology (Karpatne etĪl., 2017). Despite showing great success of applications in manyĬommercial fields, machine learning and data science models generally show
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