Reformulating land-use regression method as sign-constrained regularized regressions: Advantages and improvements. (April 2023) - Kwon, Soon-Sun; Choi, Hosik; Lee, Whanhee; Kim, Yeonjin; Kim, Hwan-Cheol; Lee, Woojoo
Abstract: Land-use regression is a popular method for predicting ambient pollutant concentrations at points of interest where no measurements are taken. However, the model-building process is complicated, and systematically understanding when and how the process works is difficult. To overcome these limitations, we reformulate the existing land use regression method as a sign-constrained regression problem with an explicit objective function to be minimized. This novel formulation always leads to estimated regression coefficients that satisfy the predefined direction based on subject matter knowledge while simultaneously substantially improving the prediction performance of the existing land-use regression method. The advantages of the proposed sign-constrained regression method are confirmed through a numerical study and real data analysis. Highlights: The sign-constrained regularized regressions were added to land use regression (LUR) model. The sign-constrained regression methods showed superior prediction performances than LUR method. The sign-constrained regression models satisfied domain knowledge-based sign constraints. The developed models could enhance the interpretability.