Optimization of Living Trees Moisture Content Diagnosis Method Based on CTGAN and GDMPA-RF Algorithm
Accurate and real-time diagnosis of living trees moisture content(MC)is an important research issue in the field of smart for-est,which can provide key indications for plant physiological state analysis,eco-hydrological control of forest areas and forest fire pre-vention.Wireless acoustic emission sensors(WASN)based MC diagnosis method is particularly suitable for forest as they are highly effi-cient and non-destructive,and can be deployed in the field for long periods of time.In order to further improve the recognition accuracy of WASN.Firstly,conditional tabular generative adversarial network(CTGAN)is used for data augmentation of the collected AE fea-tures,secondly,feature optimization is performed on the augmented hybrid dataset based on distributed gradient boosting framework(LightGBM),and then a novel golden-sine dynamic marine predators algorithm-random forests(GDMPA-RF)strategy is proposed to es-tablish an accurate inversion model of MC.The experimental comparison results show that the GDMPA-RF model based on the preferred feature subset is the most effective in enhancing the diagnostic performance of standing wood MC,with the Accuracy,Precision,F1-Score,Weighted Average and AUC of 99.17%,99.52%,98.14%,0.994 3 and 0.985 0 respectively,all of which are higher than the e-valuation indices of other optimized algorithm combined with RF,such as whale optimization algorithm,indicating that the method has excellent monitoring efficacy and greatly optimizes the accuracy of online real-time detection of moisture content of live tree trunks.
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