This study emphasizes the importance of accurate calibration in sediment transport models and highlights the transformative role of artificial intelligence (AI), specifically machine learning, in improving accuracy and computational efficiency. Extensive experiments were carried out in the Riba-Roja reservoir, which is located in the northeastern Iberian Peninsula. The accumulated sediment volume (ASV) curve was used to calibrate these experiments. The optimal ASV curve was found to be very close to the experimental data, with only minor differences in upstream areas. The results revealed a consistent rate of sediment transport and settling. Furthermore, the study investigated the capabilities of deep neural networks (DNNs) in predicting ASV curves and observing variable performance. In essence, the study highlights AI's potential for enhancing sediment transport models.

中文翻译：

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使用蒙特卡罗模拟和深度神经网络 (DNN) 优化泥沙输运模型：Riba-Roja 水库案例研究

这项研究强调了沉积物输送模型中精确校准的重要性，并强调了人工智能（AI）（特别是机器学习）在提高准确性和计算效率方面的变革性作用。在位于伊比利亚半岛东北部的里巴-罗哈水库进行了广泛的实验。累积沉积物体积（ASV）曲线用于校准这些实验。发现最佳 ASV 曲线与实验数据非常接近，上游区域仅存在微小差异。结果显示沉积物迁移和沉降的速率一致。此外，该研究还调查了深度神经网络 (DNN) 预测 ASV 曲线和观察变量性能的能力。从本质上讲，该研究强调了人工智能增强沉积物运输模型的潜力。