School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea.
School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Republic of Korea.
Water Environ Res. 2024 Aug;96(8):e11079. doi: 10.1002/wer.11079.
Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time-consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data-driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short-Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5-year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub-basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT-LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling. PRACTITIONER POINTS: We aimed to enhance process-based models for watershed water-quality modeling. The Soil and Water Assessment Tool-Long Short-Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP. It exhibited superior predictive performance when applied to other sub-basins. The hybrid model will overcome the constraints of conventional modeling. It will also enable more effective and efficient modeling.
流域水质模型旨在预测水质变化，是制定流域内有效管理策略的重要工具。基于过程的模型（PBM）通常用于模拟水质建模。在利用 PBM 进行流域建模时，通过适当设置模型参数来有效反映实际流域条件至关重要。然而，参数校准和验证是一个耗时的过程，存在固有不确定性。为了解决这些挑战，本研究旨在解决 PBM 校准和验证过程中遇到的各种挑战。为了实现这一目标，提出了一种混合模型，该模型将未经校准的 PBM 与数据驱动模型（DDM）（如深度学习算法）相结合。该混合模型旨在通过整合 PBM 和 DDM 的优势来增强流域建模。混合模型通过将未经校准的土壤和水评估工具（SWAT）与长短期记忆（LSTM）耦合来构建。SWAT 是一种具有代表性的 PBM，它是使用地理信息和五年来自延山河流域的观测数据构建的。未经校准的 SWAT 的输出变量，如流量、悬浮固体（SS）、总氮（TN）和总磷（TP）以及当天和前一天的观测降水，被用作深度学习模型的训练数据，以预测 TP 负荷。为了进行比较，对传统的 SWAT 模型进行了校准和验证，以预测 TP 负荷。结果表明，混合模型模拟的 TP 负荷比校准的 SWAT 模型预测的 TP 负荷更好。此外，混合模型反映了 TP 负荷的季节性变化，包括峰值事件。值得注意的是，当应用于没有特定训练的其他子流域时，混合模型始终优于校准的 SWAT 模型。总之，SWAT-LSTM 混合模型的应用可以成为减少模型校准不确定性和提高流域建模整体预测性能的有用工具。
我们旨在增强基于过程的流域水质模型。土壤和水评估工具-长短期记忆混合模型预测的总磷（TP）与观测到的 TP 相匹配。当应用于其他子流域时，它表现出了卓越的预测性能。混合模型将克服传统建模的限制。它还将实现更有效和高效的建模。
