Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (3): 117-124.doi: 10.6040/j.issn.1672-3961.0.2019.504

• Electrical Engineering • Previous Articles     Next Articles

Research on BP neural network rainfall runoff forecasting model based on elastic gradient descent algorithm

Baoming JIN(),Guangyi LU,Wei WANG,Lunyue DU   

  1. College of Civil Engineering, Fuzhou University, Fuzhou 350116, Fujian, China
  • Received:2019-09-03 Online:2020-06-20 Published:2020-06-16
  • Supported by:
    福建省自然科学基金资助项目(2016J01734)

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Abstract:

The improved elastic gradient descent algorithm of back propagation was used, and 14 rainfall runoff processes from 1997 to 2014 in the upper reaches of Chongyang River were selected. The back propagation (BP) neural network rainfall-runoff forecasting model of the elastic gradient descent algorithm was established, which took the measured rainfall of six rainfall stations in Yangzhuang, Wubian, Da′an, Kengkou, Lingyang, and Langu in the basin and the preliminary flow data of Wuyishan Hydrological Station as inputs, and selected the corresponding flow of Wuyishan Hydrological Station as output. The 7-rainfall runoff process was used to test the model, the test results showed that the proposed method required fewer parameters and had higher operation speed than the traditional back propagation algorithm. The prediction accuracy of the model could meet the requirements, and provide the basis for flood control and disaster reduction.

Key words: elastic gradient descent algorithm, BP neural network, rainfall runoff, forecasting model

CLC Number: 

  • TV124

Fig.1

Forward propagation diagram"

Fig.2

River system of the upper Chongyang River"

Fig.3

BP neural network structure with elastic gradient decline"

Fig.4

Seven flood prediction flow hydrographs in the upper reaches of Chongyang river"

Table 1

Prediction error analysis table of seven flood discharge processes in the upper reaches of Chongyang river"

洪水场次绝对误差绝对值平均值/(m3·s-1)相对误差绝对值平均值/%确定性系数
1998·06·1422.63.40.988
1999·06·1714.59.20.997
2003·06·2516.28.10.984
2005·06·1935.27.60.973
2007·06·1427.815.70.978
2008·07·1943.610.70.991
2013·05·2712.07.90.986

Table 2

Prediction error analysis table of seven flood peak discharge in the upper reaches of Chongyang river"

洪水场次峰型实测流量/(m3·s-1)预报流量/(m3·s-1)绝对误差/(m3·s-1)相对误差/%
1998·06·14主峰3 0802 929-151-4.9
1998·06·14次峰2 7102 689-21-0.8
1999·06·17主峰1 1701 192221.9
1999·06·17次峰988975-131.3
2003·06·25主峰797872759.4
2005·06·19主峰1 2501 277272.2
2005·06·19次峰1 1801 244645.4
2007·06·14主峰887943566.3
2008·07·19主峰2 2102 044-1667.5
2013·05·27主峰474425-499.3
平均值-1 4751 459645.0

Table 3

Prediction error analysis table of runoff depth of seven flood in the upper reaches of Chongyang river"

洪水场次实测径流深/mm预报径流深/mm绝对误差/mm相对误差/%
1998·06·14299.6301.01.40.5
1999·06·1798.7101.01.61.6
2003·06·2583.988.14.25.0
2005·06·19116.2119.12.92.5
2007·06·1436.139.23.18.6
2008·07·198587.32.32.7
2013·05·2723.223.90.93.1
平均值106.1108.52.33.4
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