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Optimization of Urban Renewal Planning Schemes and Community Vitality Enhancement Strategies Based on Deep Learning Algorithms and Smart City Evaluation

Xuan Han

Xuan Han

School of Architecture and Planning, Shenyang Jianzhu UniversityShenyang, China
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Han, Xuan
 y 
Baishu Xia

Baishu Xia

School of Architecture and Planning, Shenyang Jianzhu UniversityShenyang, China
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Xia, Baishu
  
21 mar 2025
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Publicado en línea: 21 mar 2025
Recibido: 21 oct 2024
Aceptado: 15 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0598
Palabras clave
© 2025 Xuan Han et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Based on the erg theory, the urban renewal planning plan evaluation system
Based on the erg theory, the urban renewal planning plan evaluation system

Figure 2.

The calculation principle of the AHP-RBF neural network model
The calculation principle of the AHP-RBF neural network model

Figure 3.

Artificial neural network model
Artificial neural network model

Figure 4.

RBF neural network model
RBF neural network model

Figure 5.

Comparison of prediction values and actual values of training sets
Comparison of prediction values and actual values of training sets

Figure 6.

Training training error
Training training error

Figure 7.

The comparison between the predicted value and the actual value
The comparison between the predicted value and the actual value

Figure 8.

Test training error
Test training error

Figure 9.

RBFNN output
RBFNN output

Figure 10.

The development structure of two cities
The development structure of two cities

The model training effect of different spread values

Parameter value 1 50 100 300 800 1000 1300 1500 2000
Training set RMSE 5.555 0.024 0.07 0.001 0.034 0.006 0.057 0.0707 0.0029
Training set R2 1 0.991 1.017 1.004 0.973 0.965 0.987 0.9599 0.9596
Training set MAPE 2.27% 0.16% 1.34% 2.38% 1.97% 4.56% 5.62% 6.29% 3.9%
Test set RMSE 10.301 1.878 0.62 0.139 0.053 0.116 0.083 0.058 0.1124
Test set R2 0.277 0.26 0.342 0.794 0.949 0.984 0.915 0.986 0.9181
Test set MAPE 240.63% 50.33% 18.87% 6.07% 3.12% 5.18% 4.89% 4.96% 5.38%

Intelligent urban construction effect evaluation index weight

Criterion layer Weighting Index layer Weighting Composite weight
x1 0.422 x11 0.523 0.155
x12 0.266 0.085
x13 0.211 0.182
x2 0.362 x21 0.362 0.101
x22 0.352 0.205
x23 0.286 0.056
x3 0.216 x31 0.302 0.063
x32 0.281 0.122
x33 0.417 0.031

Partial sample size

x11 x12 x13 x21 x22 x23 x31 x32 x33 y
0.2599 0.0444 0.1111 0.3838 0.0776 0.0045 0.9291 0.1555 0.0129 0.5
0.2824 0.0621 0.1544 0.7301 0.1431 0.00809 3.0395 0.2161 0.0153 0.9
0.3127 0.0798 0.1625 0.7442 0.1749 0.01763 3.1797 0.246 0.0147 1
0.3202 0.1572 0.2127 0.7716 0.1905 0.02515 3.3968 0.2754 0.0169 1.4
0.739 0.9236 0.8279 0.9185 0.3469 0.50903 8.6752 0.6648 0.1722 0.8
0.1579 0.0435 0.1305 0.0873 0.009 0.00464 0.2858 0.078 0.0061 0.9
0.2814 0.0456 0.1457 0.7232 0.1384 0.01016 3.0297 0.2135 0.0074 1
0.2909 0.1089 0.1601 0.7557 0.1832 0.01679 3.0861 0.2554 0.0142 1.3
0.3243 0.1567 0.1968 0.7654 0.1899 0.02635 3.4137 0.2687 0.02 1.5

Comprehensive grading of each index

Serial number Evaluation index Jade door Otago
1 x11 0.0545 0.0258
2 x12 0.0313 0.0377
3 x13 0.0436 0.0543
4 x21 0.0328 0.0632
5 x22 0.0499 0.0798
6 x23 0.0369 0.0249
7 x31 0.0194 0.0123
8 x32 0.0365 0.0307
9 x33 0.058 0.0632

The RD value of jade gate and Otago

City RD
Jade gate 0.04366
Otago 0.04628
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