Interpreting The Effect of Generative Adversarial Network Application on Deep Learning Model Performance for Chlorophyll‐ A Concentration Prediction in A Stream Using Explainable Artificial Intelligence

Water Environment Research  ·  2025

Jungsu Park; Woo Hyoung Lee; Ilsuk Kang; Tae‐Young Heo;

HIGHLIGHT

However, the effectiveness of these models heavily relies on the availability of high‐quality data, which is often costly and time‐intensive to collect in real‐world environments. This study employed a time‐series generative adversarial network (GAN), a representative generative artificial intelligence (AI) model, to produce synthetic data and evaluate its impact on the performance of a long short‐term memory (LSTM) network, a widely used deep learning model for time‐series prediction.

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ABSTRACT

Predicting algal blooms is crucial for effective water quality management. Recent studies have leveraged advanced data‐driven models, such as deep learning, for this purpose. However, the effectiveness of these models heavily relies on the availability of high‐quality data, which is often costly and time‐intensive to collect in real‐world environments. This study employed a time‐series generative adversarial network (GAN), a representative generative artificial intelligence (AI) model, to produce synthetic data and evaluate its impact on the performance of a long short‐term memory (LSTM) network, a widely used deep learning model for time‐series prediction. The input variables were constructed with sequence lengths of 3, 6, 9, 12, 15, and 18. Two modeling scenarios were analyzed: one using only real data (LSTM_REAL) and another (LSTM_GAN) that applies knowledge distillation to incorporate information learned from both real data and GAN‐generated data. Among the two scenarios, LSTM_GAN with a sequence length of 6 achieved the best performance with an NSE of 0.802, and the results indicated that the impact on performance varied depending on the sequence length. LSTM_GAN showed slight improvement over LSTM_REAL at sequence lengths 3, 6, 9, and 12, while showing degradation at 15 and 18, and the overall effect on performance was modest. However, a quantitative assessment using Shapley value analysis, a well‐known explainable AI technique, revealed that the GAN‐generated data accounted for 14.5%–24.3% of the total variable importance depending on the sequence length. These findings indicate that GAN‐generated data meaningfully influence the model's internal inference process. Overall, this study demonstrates the potential of the GAN algorithm in improving algal bloom prediction models.

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