This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R²), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R², and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models.

本研究评估并比较了基于过程的水文 SWAT（土壤和水评估工具）与基于机器学习的多层感知器 (MLP) 模型的性能，用于模拟上印度河盆地的水流。研究期间从 1998 年到 2013 年，其中 SWAT 和 MLP 模型分别在 1998-2005 年和 2006-2013 年期间针对多个站点进行了校准/训练和验证/测试。使用纳什-萨特克利夫效率 (NSE)、决定系数 ( R ² )、百分比偏差 (PBIAS) 和平均绝对百分比误差 (MAPE)来评估两种模型的性能。结果表明，与 MLP 模型相比，SWAT 模型的性能相对较差。NSE，PBIAS，R ²和 SWAT (MLP) 模型在校准期间的 MAPE 范围从最小值 0.81 (0.90)、3.49 (0.02)、0.80 (0.25) 和 7.61 (0.01) 到最大值 0.86 (0.99)、9.84 (0.12)、0.87 (0.99) 和 15.71 (0.267)。与 MLP 相比，SWAT 的较差性能可能受到几个因素的影响，包括敏感参数的选择、可能不代表实际雪况的雪特定敏感参数的选择、用于模拟水流的 SCS-CN 方法的潜在局限性，以及缺乏 SWAT 捕捉印度河次流域（在 Shatial 桥和 Bisham Qila）水峰的能力。基于 MLP 模型的稳健性能，目前的研究建议开发和评估机器学习模型，并将 SWAT 模型与机器学习模型合并。