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Interpreting the Effect of Generative Adversarial Network Application on Deep Learning Model Performance for

Jungsu Park1, Woo Hyoung Lee2, Ilsuk Kang3

  • 1Department of Civil and Environmental Engineering, Hanbat National University, Dongseo-daero, Republic of Korea.

Water Environment Research : a Research Publication of the Water Environment Federation
|December 23, 2025
PubMed
Summary
This summary is machine-generated.

Generative artificial intelligence (AI) models like Generative Adversarial Networks (GANs) can create synthetic data to improve algal bloom prediction models. This study shows GAN-generated data meaningfully influences model performance, offering potential for better water quality management.

Keywords:
algal bloomexplainable artificial intelligence (XAI)generative adversarial networks (GAN)knowledge distillationlong short‐term memory (LSTM)water quality

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Area of Science:

  • Environmental Science
  • Artificial Intelligence
  • Water Quality Management

Background:

  • Algal bloom prediction is vital for water quality management.
  • Data-driven models, especially deep learning, show promise but require extensive, high-quality data.
  • Collecting real-world environmental data is often expensive and time-consuming.

Purpose of the Study:

  • To investigate the impact of synthetic data generated by a time-series Generative Adversarial Network (GAN) on the performance of a Long Short-Term Memory (LSTM) network for algal bloom prediction.
  • To compare a model trained solely on real data (LSTM_REAL) with one incorporating GAN-generated data (LSTM_GAN) via knowledge distillation.
  • To assess the influence of different input sequence lengths on model performance.

Main Methods:

  • Employed a time-series GAN to generate synthetic data.
  • Utilized a Long Short-Term Memory (LSTM) network for time-series prediction.
  • Compared two scenarios: LSTM_REAL (real data only) and LSTM_GAN (real + GAN data).
  • Analyzed input sequence lengths from 3 to 18.
  • Applied Shapley value analysis to quantify the importance of GAN-generated data.

Main Results:

  • The LSTM_GAN model with a sequence length of 6 achieved the best performance (NSE of 0.802).
  • Performance varied with sequence length; LSTM_GAN showed improvement over LSTM_REAL for lengths 3-12 but degradation for lengths 15-18.
  • Shapley value analysis indicated GAN-generated data contributed 14.5%-24.3% to variable importance, demonstrating its influence on model inference.
  • The overall performance impact of GAN data was modest, but its contribution to model understanding was significant.

Conclusions:

  • Generative Adversarial Network (GAN) algorithms hold potential for enhancing algal bloom prediction models by providing valuable synthetic data.
  • GAN-generated data can meaningfully influence the internal inference processes of deep learning models, even if overall performance gains are modest.
  • Further research into optimizing GAN data integration and sequence length selection is warranted for improved water quality management through advanced AI.