Schadstoffe im Grundwasser: aktuelle Herausforderungen

Banning, Andre; Banning, Helena

doi:10.1007/s00767-018-00414-7

Cited by 1 publication

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“…At present, the application of e-nose in the groundwater pesticide detection field is mainly limited in two aspects: (1) The training model cannot be transferred, and (2) limited target samples. For the same pesticide, the e-nose response signals collected in two different regions are distributed differently, which is caused by the e-nose being subjected to other interfering volatiles (fertilizers, chemicals) in the groundwater [18]. Thus, the recognition accuracy of the model trained in region A is reduced when used to predict samples from region B.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Wang

Yang

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%