Expanding the theory for reducing the CO2 disaster—Hypotheses from partial least-squares regression and machine learning

Abstract

The rapid increase in atmospheric CO2 concentration has caused a climate disaster (CO2 disaster). This study expands the theory for reducing this disaster by analyzing the possibility of reinforcing soil CO2 uptake (Fx) in arid regions using partial least-squares regression (PLSR) and machine learning models such as artificial neural networks. The results of this study demonstrated that groundwater level is a leading contributor to the regulation of the dynamics of the main drivers of Fx–air temperature at 10 cm above the soil surface, the soil volumetric water content at 0–5 cm (R2=0.76, RMSE=0.435), and soil pH (R2=0.978, RMSE=0.028) in arid regions. Fx can be reinforced through groundwater source management which influences the groundwater level (R2=0.692, RMSE=0.03). This study also presents and discusses some basic hypotheses and evidence for quantitively reinforcing Fx.