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An Example of Augmenting Regional Sensitivity Analysis Using Machine Learning Software
Author(s) -
Spear Robert C.,
Cheng Qu,
Wu Sean L.
Publication year - 2020
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2019wr026379
Subject(s) - sensitivity (control systems) , equifinality , ranking (information retrieval) , calibration , computer science , parametric statistics , machine learning , univariate , random forest , decision tree , data mining , parameter space , parametric model , software , econometrics , artificial intelligence , mathematics , statistics , multivariate statistics , engineering , electronic engineering , programming language
Regional sensitivity analysis, RSA, has been widely applied in assessing the parametric sensitivity of environmental and hydrological models, in part because of its inherent simplicity. In that spirit, this paper reports an example of an augmented approach to improve its utility in ranking parameter importance, beyond reliance solely on the univariate marginal distributions, to include parametric interactions. Both a deterministic and a stochastic model of the transmission of dengue, an important mosquito‐borne disease, were used to explore the effect of interactions to parameter importance ranking using random forests, a commonly used method based on decision trees. The importance ranking based on random forests was generally consistent with the ranking computed from earlier methods that only examined marginal distributions, but with increased importance shown by several interacting parameters. In addition, and building on an earlier application of tree‐structured density estimation, recently developed software was used to map the regions of the parameter space supporting good fits to calibration data. These methods were also found useful in revealing the scale dependence of sensitivity analysis as well as providing a means of identifying alternative explanations for the observed behavior of the system that remain consistent with calibration criteria, a phenomenon known as equifinality.