Abstract：
The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed\'s hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model\'s output. The results of SHAP showed that river discharge has the strongest impact on the model\'s output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model\'s output (DL model is as black-box model) are recommended in future research.
摘要：
预测河流系统中的悬浮泥沙负荷（SSL）对于理解流域的水文学至关重要。因此,我们研究的新颖性是开发一种基于深度学习（DL）和Shapley加法迁移（SHAP）解释技术的可解释（可解释）模型，用于预测河流系统中的SSL。本文研究了四种DL模型的能力，包括密集深度神经网络(DDNN)，长短期记忆(LSTM)，门控经常性单位(GRU)，和简单的递归神经网络（RNN）模型，用于使用Taleghan河流域每日时间尺度的河流流量和降雨数据预测每日SSL，德黑兰西北部,伊朗。通过使用几个定量和图形标准来评估模型的性能。还研究了参数设置对深度模型在SSL预测上的性能的影响。最优优化算法，最大迭代(MI)，并获得批量大小(BC)用于对每日SSL进行建模，和模型结构对预测的影响显著。模型预测精度的比较表明，DDNN（R2=0.96，RMSE=333.46）优于LSTM（R2=0.75，RMSE=786.20），GRU（R2=0.73，RMSE=825.67），和简单的RNN（R2=0.78，RMSE=741.45）。此外，泰勒图证实了DDNN在其他型号中具有最高的性能。解释技术可以解决模型的黑箱性质，在这里,SHAP用于开发可解释的DL模型，以解释DL模型的输出。SHAP的结果表明，在估算SSL时，河流流量对模型的输出影响最大。总的来说,我们得出结论，DL模型在流域预测SSL方面具有很大的潜力。因此,在未来的研究中，建议使用不同的解释技术作为解释DL模型输出的工具(DL模型作为黑箱模型)。
