Streamflow prediction is a significant undertaking for water resources planning and management. Accurate forecasting of streamflow always being a challenging task for the hydrologist due to its high stochasticity and dynamic patterns. Several traditional and the deep learning models have been applied to simulate the complex nature of the hydrological system. However, to develop and explore a better expert system for prediction is a continuous exertion for hydrological studies. In this study, a deep neural network, namely a one-dimensional convolutional neural network (1D-CNN) and extreme learning machine (ELM) are explored for one-step-ahead streamflow forecasting for three-time horizons (daily, weekly and monthly) in Gilgit River, Pakistan. The 1D-CNN model gained incredible popularity due to its state-of-the-art performance and nominal computational complexity; while ELM model performed superfast as compared to traditional/deep learning architecture, gives comparable performance with fast execution rate. A comparative analysis is presented to assess the performance of the 1D-CNN related to the ELM model. The performance measurement matrices defined as the correlation coefficient (R²), mean absolute error (MAE) and root mean square error (RMSE) computed between the observed and predicted streamflow to evaluate the 1D-CNN and ELM model efficacy. The results indicated that the ELM model performed relatively better than the 1D-CNN model based on predefined statistical measures in three-time scale. In numerical terms, the superiority of ELM over 1D-CNN model was demonstrated by R² = 0.99, MAE = 18.8, RMSE = 50.14, and R² = 0.97, MAE = 136.59, RMSE = 230.9, for daily streamflow (testing phase) respectively. Based on our findings, it can be concluded that the ELM model would be an alternative to the 1D-CNN model for highly accurate streamflow forecasting in mountainous regions of the world.

中文翻译：

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水文时间序列预测的深度学习方法-以吉尔吉特河流域为例

流量预报是水资源规划和管理的重要工作。由于其高的随机性和动态模式，对水流的准确预测一直是水文学家一项艰巨的任务。几种传统的深度学习模型已被应用于模拟水文系统的复杂性。但是，开发和探索更好的预测专家系统是水文研究的不懈努力。在这项研究中，研究了深度神经网络，即一维卷积神经网络（1D-CNN）和极限学习机（ELM），用于三阶段（每天，每周和每月）的一步一步流量预测。 ）在巴基斯坦吉尔吉特河。1D-CNN模型由于其最新的性能和标称的计算复杂性而获得了令人难以置信的普及。与传统/深度学习体系结构相比，ELM模型的执行速度超快，可提供相当的性能和快速的执行速度。进行了比较分析，以评估与ELM模型相关的1D-CNN的性能。定义为相关系数（R²），计算观察到的和预测的流量之间的平均绝对误差（MAE）和均方根误差（RMSE），以评估1D-CNN和ELM模型的功效。结果表明，基于三倍尺度的预定义统计度量，ELM模型的性能相对优于1D-CNN模型。用数字表示， 每天流量（测试阶段）的R ²  = 0.99，MAE = 18.8，RMSE = 50.14，R ² = 0.97，MAE = 136.59，RMSE = 230.9证明了ELM优于1D-CNN模型分别。根据我们的发现，可以得出结论，ELM模型将替代1D-CNN模型，以便在世界山区进行高精度流量预测。