Study on rainfall-runoff simulation and prediction in lake basin based on dynamic data-driven deep recurrent network

Ming LIAO; Zongqian ZHAN; Wei GUO; Chao PANG; Yi LIU

doi:10.11834/jrs.20198027

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Study on rainfall-runoff simulation and prediction in lake basin based on dynamic data-driven deep recurrent network
Vol. 23, Issue 5, Pages: 911-923(2019)
Received：23 January 2018，

Accepted：24 April 2018，

Published：2019-09
DOI： 10.11834/jrs.20198027
稿件说明：

移动端阅览

廖明, 詹总谦, 呙维, 庞超, 刘异. 2019. 动态数据驱动模式下的湖泊流域降雨径流模拟. 遥感学报, 23(5): 911–923 DOI： 10.11834/jrs.20198027.

Liao M, Zhan Z Q, Guo W, Pang C and Liu Y. 2019. Study on rainfall-runoff simulation and prediction in lake basin based on dynamic data-driven deep recurrent network. Journal of Remote Sensing, 23(5): 911–923 DOI： 10.11834/jrs.20198027.

摘要

湖泊流域汇水径流过程的模拟预测是一种复杂系统中的时间序列分析问题。模型选择上，现有的机理模型法与辨识模型法各有利弊。同时，现有的模型多采用静态数据驱动模拟，不能有效利用传感网实时观测数据来改善模拟不确定性的问题。本文基于深度循环神经网络技术，提出一种适应动态数据驱动的模式，可融合遥感数据与原位传感器站点数据的DTSM(Dynamic Data Driven Time Series Model)时序模拟预测模型，并在观测值与数值模拟之间建立了一种能动态反馈、自适应调整的模拟框架，解决了传统辨识模型法对时序信息挖掘较弱导致模拟精度较低的问题。通过在鄱阳湖多个子流域入湖径流的案例中验证，显示静态数据驱动模式下，以不同数据源作为输入模拟时，本文DTSM模型的纳希效率系数

精度比机理模型提高10个百分点以上；相比静态模式，动态数据驱动模式的模拟精度有进一步提高，尤其是对于静态模式精度较低的流域，提高更为明显。

Abstract

Performing rainfall-runoff simulation and prediction in a lake basin is a time-series analysis problem in complex systems. Existing mechanistic and identification methods for model selection have advantages and disadvantages. Mechanism model has a clear physical explanation

but it requires professional data for support and the model-solving process is complicated. Identification model is flexible and its solution is simple

but it has difficulty building a universal model and the accuracy of the model is low. Existing simulation models are also based mostly on static data

which cannot effectively use real-time observation data from sensor networks to improve simulation uncertainty. This study aims to improve the problem of traditional identification models being incapable of effectively using timing information

which results in low simulation accuracy. Moreover

it establishes a simulation and simulation framework for dynamic feedback and adaptive adjustment between observation and numerical simulation. This research proposes a dynamic data-driven model based on deep recurrent neural network

named dynamic data-driven time sequence model

which consists of a multilayered long short-term memory loop body and a fully connected layer. The proposed model incorporates runoff remote sensing rainfall data and ground station observation rainfall data as static data input and recent ground station actual runoff observation data as dynamic data input to simulate catchment runoff process. Several cases of multiple sub-river runoff into Poyang Lake indicate that in static data-driven mode

with TRMM_3B42_V7 precipitation as input

the ENS accuracy of DTSM is 10 percentage points higher or more than that of the mechanism model. The cases also indicate that in static data-driven mode

with the fusion precipitation from TRMM_3B42_V7 and ground station as input

the ENS accuracy of DTSM is 29 percentage points higher or more than that of the mechanism model. Lastly

the dynamic data-driven model can further improve the accuracy of simulation compared with the static-driven model

and the improvement is substantial in the basin with lower accuracy in static data-driven mode. The model based on deep recurrent neural network can effectively extract the timing information from the data. The dynamic data-driven model can make adaptive adjustments to improve simulation uncertainty. Based on the above two reasons

the DTSM proposed in this paper can achieve the same or a better simulation accuracy than existing representative mechanism models. At the same time

DTSM is flexible and the solving process is simple.

关键词

Keywords

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