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Rural Planning Evaluation Based on Artificial Neural Network.

Yumei Liu^1,2, Xuezhou Huang¹

¹School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.

Computational and Mathematical Methods in Medicine

|June 21, 2022

Summary

This summary is machine-generated.

This study introduces an improved LM-BP neural network for evaluating rural infrastructure planning. The new model offers higher accuracy and reliability in assessing rural development projects.

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Rural Development and Infrastructure Planning
Artificial Intelligence in Urban and Regional Planning
Civil Engineering and Construction Management

Background:

Human civilization's progress is tied to rural development, with infrastructure as its cornerstone.
China's recent large-scale rural infrastructure initiatives face challenges in construction and require effective leadership and evaluation.
Current rural planning evaluation models lack accuracy and efficiency, often suffering from neural network limitations like local extreme values.

Purpose of the Study:

To address the limitations of existing rural planning evaluation models.
To propose and validate an improved LM-BP neural network for accurate rural planning assessment.
To guide future rural construction planning and implementation in China.

Main Methods:

An enhanced LM-BP neural network was developed for rural planning evaluation.
The model incorporates five key factors: industrial construction, population distribution, facility utilization, public facilities, and policy effects.
The LM-BP network was trained by converting it into a least squares problem, with the LM method optimizing hidden layer nodes.

Main Results:

The developed LM-BP neural network model demonstrated high accuracy in evaluating rural planning.
The model achieved a small evaluation error, outperforming similar existing models.
The experimental results confirm the reliability of the LM-BP neural network for rural planning assessment.

Conclusions:

The LM-BP neural network provides a reliable and accurate method for evaluating rural planning and construction.
This approach can effectively guide China's supervision and inspection of rural construction, promoting development.
The improved model enhances the efficiency and precision of rural infrastructure planning assessments.