Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

Accurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction model built on one region's samples might be ineffective when tested in another. Moreover, the establishment of a new prediction model requires a large number of sample data, which will cost too much resources and time. To resolve this issue, this study introduced the TrAdaBoost transfer learning method to recognize the pesticide in groundwater using the e-nose. The main work was divided into two steps: (1) qualitatively checking the pesticide type and (2) semi-quantitatively predicting the pesticide concentration. The support vector machine integrated with the TrAdaBoost was adopted to complete these two steps, and the recognition rate can be 19.3% and 22.2% higher than that of methods without transfer learning. These results demonstrated the potential of the TrAdaBoost based on support vector machine approaches in recognizing the pesticide in groundwater when there were few samples in the target domain.

Automated summary

Accurate and rapid prediction of pesticides in groundwater is crucial for protecting human health. This study introduced the TrAdaBoost transfer learning method to recognize pesticides in groundwater using an electronic nose. Two steps were conducted: qualitative identification of pesticide type and semi-quantitative prediction of pesticide concentration. The integration of support vector machine with TrAdaBoost showed significantly improved recognition rates compared to methods without transfer learning. These results demonstrate the potential of TrAdaBoost-based support vector machine approaches in recognizing pesticides in groundwater with limited samples.