Solar radiation modeling with KNIME and Solar Analyst: Increasing environmental model reproducibility using scientific workflows. (October 2020) - Radosevic, Nenad; Duckham, Matt; Liu, Gang-Jun; Sun, Qian
Abstract: The inherent complexity of environmental models is frequently a limiting factor in their usefulness and practical applicability. This paper aims to demonstrate how scientific workflows can increase the reproducibility of environmental models by better managing this complexity. Specifically, through the example of Solar Analyst solar radiation model, the paper identifies three primary mechanisms for managing environmental modeling complexity using scientific workflows: (i) increasing transparency and improving reproducibility, in both the modeling process and the model itself; (ii) integrating validation and improving warrantability of solar radiation model outputs; and (iii) widening opportunities for supporting parameter-setting decisions for a diversity of modelers, using machine learning. The results demonstrate how each of these mechanisms can be realized using a freely-available and open-source scientific workflow management system (SWFMS) called KNIME. Firstly, our example KNIME workflows demonstrate increased transparency and improved reproducibility of solar radiation models and the entire modeling process. In turn, improving transparency and reproducibility can aid novice users in understanding and reusing solar radiation models. Secondly, an extended KNIME workflow is used to integrate both modeling and validation into a single, transparent workflow. Lastly, using KNIME workflows facilitates integration with other decision-support tools and techniques, such as machine learning. Using decision trees, an extended solar radiation KNIME workflow offers the capability to support more transparent and warrantable decisions around setting Solar Analyst parameter values. Ultimately, better managing the complexity of environmental modeling contributes to wider uptake and scrutiny of environmental models and the outputs they generate, both in scientific research and in applied evidence-based decision making. Highlights: Using KNIME scientific workflows for improving the reproducibility and warrantability of solar radiation modeling. Implementing KNIME scientific workflows to expose the inner workings of "black box" solar radiation models. Integrating machine learning with solar radiation model workflows to improve decision-support for setting model parameters.