1. College of Agricultural Engineering, Hohai University, Nanjing 210098, China 2. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China 3. Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China
The Gaussian mutation operator was introduced into the basic artificial bee colony algorithm, and the initial nectar location was optimized in order to verify the feasibility and superiority of radial basis function (RBF) neural network model based on improved artificial bee colony algorithm in groundwater depth prediction. A RBF neural network model was designed based on improved artificial bee colony algorithm (IABC-RBF). The groundwater depth was predicted by inputting the annual rainfall, the amount of annual water intake in the first ditch, the amount of annual field irrigating, the annual groundwater exploitation and the groundwater depth in the previous year of Jinghui irrigation district. The error is very small compared with the measured groundwater depth data. The model was compared with the RBF neural network model and the RBF neural network model based on the basic artificial bee colony algorithm (ABC-RBF). Results show that the RBF neural network model based on the improved artificial bee colony algorithm has faster convergence speed, the least error of the prediction result and the highest accuracy.
Guang-cheng SHAO,Kun ZHANG,Zhi-yu WANG,Xiao-jun WANG,Jia LU. Groundwater depth prediction model based on IABC-RBF neural network. Journal of ZheJiang University (Engineering Science), 2019, 53(7): 1323-1330.
Fig.1Dynamic relationship between influence factors and groundwater depth
Fig.2Schematic diagram of radial basis function (RBF) neural network model
Fig.3Optimization process diagram of improved artificial bee colony algorithm
Fig.4Schematic diagram of RBF neural network model
序号
参数
Goal
Spread
MN
1
0.000 862
1.826 070
8
2
0.000 498
2.933 067
10
3
0.000 798
1.688 562
12
4
0.000 789
1.825 936
12
5
0.000 254
1.326 492
11
6
0.000 467
2.526 777
14
7
0.000 207
1.522 293
9
8
0.000 309
1.318 309
8
9
0.000 763
1.383 367
7
10
0.000 168
2.421 241
8
Tab.1Parameter training results of RBF neural network model
Fig.5Comparison of predictive value and measured value of IABC-RBF neural network model
年份
实测值/m
模型
预测值/m
MRE/%
最优
最差
平均
2006
15.59
RBF
?
?
14.97
3.95
2006
15.59
ABC-RBF
15.76
16.07
15.85
1.66
2006
15.59
IABC-RBF
15.74
15.92
15.83
1.51
2007
15.57
RBF
?
?
14.56
6.46
2007
15.57
ABC-RBF
15.73
16.27
16.02
2.91
2007
15.57
IABC-RBF
15.79
16.18
15.97
2.58
2008
15.78
RBF
?
?
15.15
4.01
2008
15.78
ABC-RBF
15.69
15.32
15.55
1.44
2008
15.78
IABC-RBF
15.71
15.42
15.59
1.20
2009
16.21
RBF
?
?
17.19
6.08
2009
16.21
ABC-RBF
16.06
14.97
15.66
3.38
2009
16.21
IABC-RBF
16.06
15.59
15.75
3.04
2010
16.25
RBF
?
?
14.78
9.05
2010
16.25
ABC-RBF
16.25
16.04
16.20
0.68
2010
16.25
IABC-RBF
16.25
16.11
16.25
0.03
Tab.2Comparison of prediction results of three prediction models
Fig.6Average relative error comparison of three models' prediction results
模型
MRE/%
σ/m
RBF模型
5.91
0.992 2
ABC-RBF模型
2.01
0.356 5
IABC-RBF模型
1.67
0.305 9
Tab.3Comparison of average relative error and variance of three model prediction results
Fig.7Best cost of each iteration
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