## 4 Experimental Results and Discussion
### Comparisons of Chla Estimation Models  
A real model test, in which the machine-learning model is trained and tested using two geographically different and dependently corrected sample datasets, is rarely conducted due to the limited in situ Chla samples and overfitting problems. In this study, a ANN model was trained using the proposed transfer learning with the processes of data augmentation and rebalancing. The training Chla sample dataset was collected from Laguna Lake, Philippines. The trained ANN model was then applied to the Chla samples acquired from Lake Victoria, Uganda, for testing and evaluation. In addition, the trained ANN model was compared with the related models, including the three-band model [9], two-band model [13], NDCI [14], and WaterNet [34]. WaterNet is described in Section 3.1 and the other models are presented in Table 5. For fair comparisons, the three-band and two-band models were calibrated using in situ Chla-labelled data from Laguna Lake with a linear regression model. Linear regression was selected to ease overfitting problems. In addition, the hyperparameters containing the batch size, the optimizer, and the learning rate in the WaterNet training with original two-stage training are the same as that in the proposed training. Different to the other compared models, it is not necessary to calibrate the NDCI model, as the model directly outputs the estimated Chla concentrations. All of the compared models were trained using the dataset from Laguna Lake and then tested using the dataset from Lake Victoria for fair comparisons.  
Figure 8: Comparisons of ANN model training using original, augmented, and balanced dataset.  
Figure 9: Performance of ANN model training with and without data rebalancing. Yellow and silver dots represent the estimated Chla concentrations using the augmented dataset with and without the process of data rebalancing.  
Table 6 shows the comparison results of WaterNet, trained using original two-stage training with original data, and the proposed method, including the improved transfer learning with data augmentation and rebalancing. The table also contains the related models using the Chla dataset from Lake Victoria. The results indicate that the three-band model with the performance RMSE = 0.588 ug/L and the two-band models with the performance RMSE = 0.509 ug/L have similar Chla concentration prediction accuracy. This may be due to the fact that these two models utilize similar \(R_{rs}\) features, that are \(R_{rs}\) at \(\lambda_{443}\) and at \(\lambda_{490}\), which share similar sensitivity to the absorption [13]. WaterNet trained with original two-stage training and data also performed similarly, with RMSE = 0.496 ug/L. Better performances were obtained when the estimation of Chla concentrations was conducted using WaterNet with the proposed training method and NDCI. The RMSEs of the two models were 0.228 ug/L and 0.244 ug/L, and WaterNet with the proposed training was slightly better than NDCI. This means that the proposed transfer learning with the processes of data augmentation and rebalancing is able to resist the overfitting problem, and the performance of the trained model outperforms the related models.