No abstract

Changes in hydrological characteristics and increases in various pollutant loadings due to rapid climate change and urbanization have a significant impact on the deterioration of aquatic ecosystem health (AEH). Therefore, it is important to effectively evaluate the AEH in advance and establish appropriate strategic plans. Recently, machine learning (ML) models have been widely used to solve hydrological and environmental problems in various fields. However, in general, collecting sufficient data for ML training is time-consuming and labor-intensive. Especially in classification problems, data imbalance can lead to erroneous prediction results of ML models. In this study, we proposed a method to solve the data imbalance problem through data augmentation based on Wasserstein Generative Adversarial Network (WGAN) and to efficiently predict the grades (from A to E grades) of AEH indices (i.e., Benthic Macroinvertebrate Index (BMI), Trophic Diatom Index (TDI), Fish Assessment Index (FAI)) through the ML models. Raw datasets for the AEH indices composed of various physicochemical factors (i.e., WT, DO, BOD5, SS, TN, TP, and Flow) and AEH grades were built and augmented through the WGAN. The performance of each ML model was evaluated through a 10-fold cross-validation (CV), and the performances of the ML models trained on the raw and WGAN-based training sets were compared and analyzed through AEH grade prediction on the test sets. The results showed that the ML models trained on the WGAN-based training set had an average F1-score for grades of each AEH index of 0.9 or greater for the test set, which was superior to the models trained on the raw training set (fewer data compared to other datasets) only. Through the above results, it was confirmed that by using the dataset augmented through WGAN, the ML model can yield better AEH grade predictive performance compared to the model trained on limited datasets; this approach reduces the effort needed for actual data collection from rivers which requires enormous time and cost. In the future, the results of this study can be used as basic data to construct big data of aquatic ecosystems, needed to efficiently evaluate and predict AEH in rivers based on the ML models.

The Agricultural Policy/Environmental eXtender (APEX) model has been used for farm/small watershed management, and the ArcAPEX interface was developed using the ArcGIS extension. However, the interface requires a paid license and limits dynamic applications that reflect various agricultural farming practices. In this study, a novel APEX model interface using Quantum GIS, the QAPEX analysis system, was developed by incorporating open-source-based GIS software for the simulation of water quality impacts of various best management practices reflecting local farming activities. The watershed delineation process running on the QAPEX interface is more flexible than that on the ArcAPEX interface, which renders simulations on hydrology and water quality with considerable precision. The newly developed system can be used to visually interpret simulation results (e.g., flow and load duration curve functions). Therefore, the open-source-based model can be used to derive data for sustainable agricultural policies, with a focus on the field-level application of management practices.