Abstract

Deep neural networks has been widely used in runoff forecasting and has achieved better performance than of conceptual hydrological models. However, most existing studies only use a single type of neural network model to build runoff forecasting models, which fails to fully explain the role of different types of neural networks in runoff forecasting. In this study, the convolutional neural networks (CNN), long short-term memory (LSTM) networks, and convolutional LSTM (ConvLSTM) were used to design a hybrid deep neural network model (HydroDL) for runoff prediction by referring to the structure of the conceptual hydrological model. The proposed model was used to predict the daily runoff at the Xinlong and Daofu hydrological stations in the upper reaches of the Yalong River, and several other runoff prediction models based on neural networks and conceptual hydrological models were developed for comparative study. Daily scale meteorological, hydrological and topographic data from January 2011 to December 2020 were used to train and validate the above models. The results show that: (1) the proposed HydroDL model has higher prediction accuracy and more stable prediction performance than runoff prediction models based on a single type of neural network. (2) effects of different parts in the HydroDL model are distinct, among which the terrain feature extraction and runoff conversion has the most significant and least significant effect, respectively, on improving the forecast results.

中文翻译：

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基于混合深度神经网络的径流预报模型评估与解释

摘要

深度神经网络已广泛应用于径流预测，并取得了比概念水文模型更好的性能。然而，现有研究大多仅使用单一类型的神经网络模型来构建径流预测模型，未能充分解释不同类型的神经网络在径流预测中的作用。本研究借鉴卷积神经网络（CNN）、长短期记忆网络（LSTM）和卷积LSTM（ConvLSTM）的结构，设计了用于径流预测的混合深度神经网络模型（HydroDL）。概念水文模型。该模型用于预测雅砻江上游新龙水文站和道孚水文站的日径流，并开发了其他几种基于神经网络和概念水文模型的径流预测模型进行比较研究。利用2011年1月至2020年12月逐日尺度气象、水文、地形数据对上述模型进行训练和验证。结果表明：（1）所提出的HydroDL模型比基于单一类型神经网络的径流预测模型具有更高的预测精度和更稳定的预测性能。 (2)HydroDL模型中不同部分的效果差异明显，其中地形特征提取和径流转换对改善预报结果的影响分别最显着和最不显着。