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Determining the weights of influencing factors of construction lands with a neural network algorithm: a case study based on Ya’an City

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Abstract

In-depth quantitative studies regarding the nonlinear development laws of a region and the calculation of construction land area (CLA) and weight coefficients of different influencing factors are of great significance for establishing the developmental change model of a region. In this study, a novel calculation method using correlation coefficients was introduced. First, models of three neural network algorithms, namely, back propagation neural network (BPNN), grey model neural network (GMNN), and generalised regression neural network (GRNN), were constructed. Key attention was given to improvement of the BPNN. The correlation laws of different influencing factors in a region and the CLA were discovered and extracted using these three models. The coefficient of determination and coefficient of variation were applied to verify the validity of the simulation results for each neutral network algorithm model. The mean absolute error and root mean square error of the three algorithm models were calculated to select the neutral network algorithm model with the highest accuracy. Subsequently, the mean impact value algorithm was added to the selected algorithm model to calculate the weight coefficients for the different relevant factors. The calculated results were compared with the weight values of influencing factors, which were calculated using the analytic hierarchy process, thus forming a set of calculation methods for a more accurate judgement of the influencing factors and weight coefficients of CLA. In this study, the correlations between the influencing factors and CLA were calculated using the proposed method via a case study. The calculated results of the proposed method conformed well to the practical situation in Ya’an City, indicating that the proposed method is worthy of promotion and practice.

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Funding

The work is funded by National Key R&D Program of China (2018YFD1100804).

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Authors and Affiliations

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Lei Gao, Yazhou Zhou & Xiaofan Zhu

  2. School of Architecture and Urban Planning, Chongqing University, Chongqing, China

    Kairui Guo & Yong Huang

Authors
  1. Lei Gao
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  2. Yazhou Zhou
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  3. Kairui Guo
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  4. Yong Huang
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  5. Xiaofan Zhu
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Correspondence to Kairui Guo.

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Communicated by: H. Babaie

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Highlights

In this paper, A case study based on Ya’an City was performed. Key attention was given to the following aspects:

(1) How to use the machine-learning algorithm to extract laws during urban-rural development? What are the differences among the three applied machine-learning algorithms? How to realise these three algorithms? What are the optimisation and accuracy comparison methods of the three algorithms?

(2) What are the similarities and differences between the neural networks (using the MIV algorithm) and AHP in determining the weights of the variables?

(3) How to provide a universal method to determine the relative importance of influence values for other cities, towns, and villages?

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Gao, L., Zhou, Y., Guo, K. et al. Determining the weights of influencing factors of construction lands with a neural network algorithm: a case study based on Ya’an City. Earth Sci Inform 14, 1973–1985 (2021). https://doi.org/10.1007/s12145-021-00657-8

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  • DOI: https://doi.org/10.1007/s12145-021-00657-8

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