Floods are one of the major climate-related disasters, leading to substantial economic loss and social safety issue. However, the confidence in predicting changes in fluvial floods remains low due to limited evidence and complex causes of regional climate change. The recent development in machine learning techniques has the potential to improve traditional hydrological models by using monitoring data. Although Recurrent Neural Networks (RNN) perform remarkably with multivariate time series data, these models are blinded to the underlying mechanisms represented in a process-based model for flood prediction. While both process-based models and deep learning networks have their strength, understanding the fundamental mechanisms intrinsic to geo-spatiotemporal information is crucial to improve the prediction accuracy of flood occurrence. This paper demonstrates a neural network architecture (HydroDeep) that couples a process-based hydro-ecological model with a combination of Deep Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to build a hybrid baseline model. HydroDeep outperforms the performance of both the independent networks by 4.8% and 31.8% respectively in Nash-Sutcliffe efficiency. A trained HydroDeep can transfer its knowledge and can learn the Geo-spatiotemporal features of any new region in minimal training iterations.
翻译:虽然经常神经网络(RNN)利用多变时间序列数据显著地改善了传统水文模型,但这些模型与基于过程的洪水预测模型所代表的基本机制视而不见。尽管基于过程的模型和深层学习网络都具有实力,但了解地理空间信息所固有的基本机制对于提高洪水发生预测的准确性至关重要。本文展示了神经网络结构(HydroDepep),这种网络将基于过程的水文生态模型与深层神经网络(CNN)和长期短期内存(LSTM)网络相结合,以建立一个混合基线模型。经过培训的氢深线网络可以在Nash-Suttliffe节效率方面学习最低程度的知识和学习。