Kim Young Woo, Cha YoonKyung, Shin Jihoon
School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, Republic of Korea.
School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, Republic of Korea.
Water Res. 2025 Oct 1;285:124059. doi: 10.1016/j.watres.2025.124059. Epub 2025 Jun 20.
Process-based models (PBMs) are widely used for simulating harmful algal blooms (HABs) but are constrained by high computational costs and parameter calibration challenges, limiting their efficiency for large-scale applications. This study develops a modular deep learning surrogate model to approximate PBM outputs while significantly improving computational efficiency and predictive accuracy. Applied to bloom-prone Daecheong Lake in South Korea during the calibration (2022) and validation (2023) periods, the framework emulates hydrodynamic (FLOW), water quality (WAQ), and phytoplankton dynamics (BLOOM) processes through a sequential structure, where outputs from FLOW serve as inputs for WAQ, and WAQ outputs feed into BLOOM, preserving key environmental interactions while reducing model complexity. Performance comparisons indicate that integrating surrogate model-generated data with probabilistic parameter optimization enhanced model performance. Surrogate model-based parameter optimization (SM-PO) achieved higher predictive accuracy than trial-and-error-based calibration (TE-PC) and TE-PC with data augmentation (DA) across all modules. For total cyanobacteria cell counts, SM-PO improved Nash-Sutcliffe Efficiency (NSE) from 0.644 (TE-PC) and 0.782 (TE-PC with DA) to 0.930 in 2022, and from 0.520 to 0.719 to 0.867 in 2023. Additionally, chlorophyll-a predictions achieved an RMSE reduction of approximately 40 % compared to TE-PC, demonstrating the effectiveness of integrating surrogate modeling with data augmentation and probabilistic parameter optimization. Furthermore, temporal dimensionality reduction significantly accelerated parameter optimization, reducing computation time by 87.5 % for hydrodynamic simulations and 96.4 % for water quality and phytoplankton modules, without sacrificing model accuracy. The modular structure enables targeted module updates, reducing retraining requirements and enhancing flexibility for different environmental conditions. Beyond accelerating parameter optimization, the trained surrogate model enables near real-time HAB forecasting. By leveraging daily observed environmental inputs, it generates one-day-ahead predictions without requiring full Delft3D simulations. The proposed framework provides a scalable and computationally efficient tool for HAB simulation, with broad applicability to various aquatic systems and potential for integration into operational water quality management. By bridging PBMs with deep learning, this approach offers an advanced framework for water resource management, ecological forecasting, and eutrophication mitigation in freshwater ecosystems.
基于过程的模型（PBMs）被广泛用于模拟有害藻华（HABs），但受到高计算成本和参数校准挑战的限制，从而限制了它们在大规模应用中的效率。本研究开发了一种模块化深度学习替代模型，以近似PBM的输出，同时显著提高计算效率和预测准确性。该框架应用于韩国易发生藻华的大清湖在校准期（2022年）和验证期（2023年），通过顺序结构模拟水动力（FLOW）、水质（WAQ）和浮游植物动态（BLOOM）过程，其中FLOW的输出作为WAQ的输入，WAQ的输出输入到BLOOM中，在降低模型复杂性的同时保留关键的环境相互作用。性能比较表明，将替代模型生成的数据与概率参数优化相结合可提高模型性能。基于替代模型的参数优化（SM-PO）在所有模块上均比基于试错法的校准（TE-PC）和带数据增强（DA）的TE-PC实现了更高的预测准确性。对于总蓝藻细胞计数，SM-PO在2022年将纳什-萨特克利夫效率（NSE）从0.644（TE-PC）和0.782（带DA的TE-PC）提高到0.930，在2023年从0.520提高到0.719再提高到0.867。此外，与TE-PC相比，叶绿素a预测的均方根误差（RMSE）降低了约40%，证明了将替代建模与数据增强和概率参数优化相结合的有效性。此外，时间维度约简显著加速了参数优化，水动力模拟的计算时间减少了87.5%，水质和浮游植物模块的计算时间减少了96.4%，同时不牺牲模型准确性。模块化结构允许有针对性地更新模块，减少重新训练的需求，并增强对不同环境条件的灵活性。除加速参数优化外，训练好的替代模型还能实现有害藻华的近实时预测。通过利用每日观测的环境输入，它无需完整的代尔夫特3D模拟即可生成提前一天的预测。所提出的框架为有害藻华模拟提供了一种可扩展且计算高效的工具，在各种水生系统中具有广泛的适用性，并有可能集成到运营水质管理中。通过将PBM与深度学习相结合，这种方法为淡水生态系统中的水资源管理、生态预测和富营养化缓解提供了一个先进的框架。
