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image of Optimization Analysis of Urban and Rural Environmental Planning Based on Artificial Intelligence and Intelligent Information Processing Algorithms
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Abstract

Introduction

With rapid economic development and urbanization, urban and rural areas face environmental challenges. Traditional optimization methods struggle with complexity and often fail to find global optima.

Method

This study integrates a Bidirectional Long Short-Term Memory Network (BiLSTM) with Genetic Algorithm (GA)-Ant Colony Optimization (ACO) to improve environmental planning. BiLSTM captures long-term data correlations and predicts future trends, achieving an average Mean Squared Error (MSE) of 0.0217. GA-ACO, using GA-generated solutions as initial input for ACO, identifies optimal planning solutions.

Results

This approach enhances air quality indicators and provides robust predictions and optimizations for sustainable urban and rural development.

Conclusion

To sum up, future development needs comprehensive technical progress, policy support and public participation to form a multi-level and multi-field collaborative mechanism to achieve the real sustainable development goal.

© 2024 The Author(s). Published by Bentham Science Publishers
© 2024 The Author(s). Published by Bentham Science Publishers. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode.
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/content/journals/eng/10.2174/0118722121330025241009115648
2024-12-09
2025-01-15

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References

  1. Min L. Chen L. Yang X. Zhao F. Long S. Xu S. Characteristics and functional differences of soil microbial communities in urban and rural composite ecosystems: research progress and prospects. J. Soil Sci. 2021 58 6 1368 1380
    [Google Scholar]
  2. Wang S. Liu L. From corridors to the whole region-the construction of urban and rural blue-green space networks under the leadership of green urban design. Landscape Architecture 2021 28 8 45 50
    [Google Scholar]
  3. Wei W. Discussion on land resource management based on urban and rural planning and construction. Engineering Technology Research 2020 5 19 147 148
    [Google Scholar]
  4. Li C. Lin H. Wang X. Yang X. Chen Q. Urban and rural planning strategy based on the “double carbon” goal. Planner 2022 38 1 12 16
    [Google Scholar]
  5. Jun W. Research on the framework of smart city operating system based on new ICTs. American Journal of Artificial Intelligence 2020 4 1 36 41 10.11648/j.ajai.20200401.14
    [Google Scholar]
  6. Suanpang P. Pothipasa P. Netwrong T. Policies and platforms for fake news filtering on cybercrime in smart city using artificial intelligence and blockchain technology. Int. J. Cyber Criminol. 2021 15 1 143 157
    [Google Scholar]
  7. Yang C. Liu C. “Research on the evaluation system of urban and rural planning professional courses--Taking urban and rural ecology and environmental planning courses as an example.”Higher architectural. Education 2022 31 6 17 24
    [Google Scholar]
  8. Yang Ximei The influence of the concept of green ecology on urban and rural planning and design. Construct. Engin. Manag. 2024 2024 129 132
    [Google Scholar]
  9. Hu Z. Research on ecological architectural design in urban and rural planning and design. Engineering Research and Practice 2024 5 9 223 225
    [Google Scholar]
  10. Min Z. The application of green building design in urban and rural planning practice. Intell. Archit. 2022 4 11 73 75
    [Google Scholar]
  11. Wang Z. Liu F. Yang Q. e S. Du T. Spatio-temporal patterns of rural settlements in mountainous areas and optimization with ant colony algorithm: Evidence from Chengjiang town in Chongqing. Ziran Ziyuan Xuebao 2022 37 8 2065 2084 10.31497/zrzyxb.20220810
    [Google Scholar]
  12. Degerli B. Çetin M. Evaluation from rural to urban scale for the effect of NDVI-NDBI indices on land surface temperature, in Samsun, Türkiye. Turk.J.Agriculture - Food Sci. Technol. 2022 10 12 2446 2452 10.24925/turjaf.v10i12.2446‑2452.5535
    [Google Scholar]
  13. Li Y. Analysis of the current situation of land resource management and resource environment in urban and rural planning and management. Architect. Design. Res. 2023 2023 72 74
    [Google Scholar]
  14. Yang Z. Li J. Sun M. Li J. Luna. “Spatial structure identification and optimization path analysis of rural regional systems in urban-rural integration areas--Taking Changsha County, Hunan Province as an example.”. Ziran Ziyuan Xuebao 2023 38 8 2076 2096 10.31497/zrzyxb.20230810
    [Google Scholar]
  15. Batool T. Abbas S. Alhwaiti Y. Saleem M. Ahmad M. Asif M. Sabri Elmitwally N. Intelligent model of ecosystem for smart cities using artificial neural networks. Intell.Auto. Soft Comput. 2021 29 3 513 525 10.32604/iasc.2021.018770
    [Google Scholar]
  16. Wei J. Liu S. Li Z. Liu C. Qin K. Liu X. Pinker R.T. Dickerson R.R. Lin J. Boersma K.F. Sun L. Li R. Xue W. Cui Y. Zhang C. Wang J. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environ. Sci. Technol. 2022 56 14 9988 9998 10.1021/acs.est.2c03834 35767687
    [Google Scholar]
  17. Ighalo J.O. Adeniyi A.G. Marques G. Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis. Model. Earth Syst. Environ. 2021 7 2 669 681 10.1007/s40808‑020‑01041‑z
    [Google Scholar]
  18. Podorozhniak A. Usage of intelligent methods for multispectral data processing in the field of environmental monitoring. Adv. Inf. Syst. 2021 5 3 97 102
    [Google Scholar]
  19. Sanchez T.W. Shumway H. Gordner T. Lim T. The prospects of artificial intelligence in urban planning. Int. J. Urban Sci. 2023 27 2 179 194 10.1080/12265934.2022.2102538
    [Google Scholar]
  20. Ma S. Zhou C. Chi C. Liu Y. Yang G. Estimating physical composition of municipal solid waste in China by applying artificial neural network method. Environ. Sci. Technol. 2020 54 15 9609 9617 10.1021/acs.est.0c01802 32628460
    [Google Scholar]
  21. Liu J. Zhao H. Luo X. Yun X. Research progress on batch normalization of deep learning and its related algorithms. J. Auto. 2020 46 6 1090 1120
    [Google Scholar]
  22. Ting Z. Wang H. Zhang J. Ran T. Kai Y. Arc fault detection method based on self-normalized neural network. J. Instrum. 2021 42 3 141 149
    [Google Scholar]
  23. Naoumi S. Bazzi A. Bomfin R. Chafii M. Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISAC. IEEE J. Sel. Top. Signal Process. 2024 1 15 10.1109/JSTSP.2024.3387299
    [Google Scholar]
  24. Wang Z. Su X. Ding Z. Long-term traffic prediction based on lstm encoder-decoder architecture. IEEE Trans. Intell. Transp. Syst. 2021 22 10 6561 6571 10.1109/TITS.2020.2995546
    [Google Scholar]
  25. Njima W. Bazzi A. Chafii M. DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning. IEEE Access 2022 10 69896 69909 10.1109/ACCESS.2022.3187837
    [Google Scholar]
  26. Singla P. Duhan M. Saroha S. An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network. Earth Sci. Inform. 2022 15 1 291 306 10.1007/s12145‑021‑00723‑1 34804244
    [Google Scholar]
  27. Sahragard A. Falaghi H. Farhadi M. Mosavi A. Estebsari A. Generation expansion planning in the presence of wind power plants using a genetic algorithm model. Electronics (Basel) 2020 9 7 1143 1143 10.3390/electronics9071143
    [Google Scholar]
  28. Hamdia K.M. Zhuang X. Rabczuk T. An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Comput. Appl. 2021 33 6 1923 1933 10.1007/s00521‑020‑05035‑x
    [Google Scholar]
  29. Singh S.S. Singh K. Kumar A. Biswas B. ACO-IM: maximizing influence in social networks using ant colony optimization. Soft Comput. 2020 24 13 10181 10203 10.1007/s00500‑019‑04533‑y
    [Google Scholar]
  30. Che G. Liu L. Yu Z. An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle. J. Ambient Intell. Humaniz. Comput. 2020 11 8 3349 3354 10.1007/s12652‑019‑01531‑8
    [Google Scholar]
/content/journals/eng/10.2174/0118722121330025241009115648
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Optimization Analysis of Urban and Rural Environmental Planning Based on Artificial Intelligence and Intelligent Information Processing Algorithms
Bentham Science Publishers ; https://doi.org/10.2174/0118722121330025241009115648
/content/journals/eng/10.2174/0118722121330025241009115648
/content/journals/eng/10.2174/0118722121330025241009115648
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  • Article Type:     Research Article
Keywords: genetic algorithm ; Urban and rural environment ; planning optimization ; bidirectional long short-term memory network ; ant colony optimization

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