Multitask Deep Learning Enabling a Synergy for Cadmium and Methane Mitigation with Biochar Amendments in Paddy Soils

Abstract

Biochar has demonstrated significant promise in addressing heavy metal contamination and methane (CH 4 ) emissions in paddy soils; however, achieving a synergy between these two goals is challenging due to various variables, including the characteristics of biochar and soil properties that influence biochar's performance. Here, we successfully developed an interpretable multitask deep learning (MTDL) model by employing a tensor tracking paradigm to facilitate parameter sharing between two separate data sets, enabling a synergy between Cd and CH 4 mitigation with biochar amendments. The characteristics of biochar contribute similar weightings of 67.9% and 62.5% to Cd and CH 4 mitigation, respectively, but their relative importance in determining biochar's performance varies significantly. Notably, this MTDL model excels in custom-tailoring biochar to synergistically mitigate Cd and CH 4 in paddy soils across a wide geographic range, surpassing traditional machine learning models. Our findings deepen our understanding of the interactive effects of Cd and CH 4 mitigation with biochar amendments in paddy soils, and they also potentially extend the application of artificial intelligence in sustainable environmental remediation, especially when dealing with multiple objectives.