Effects on fringe area development status around pune city on transportation scenario

Swapnali Sutar; Bajarang Hanamant Sutar

doi:10.22712/susb.20240003

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2024 Vol.15, Issue 1 Preview Page Next Page

General Article

Effects on fringe area development status around pune city on transportation scenario

31 March 2024. pp. 23-42

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Abstract

This study examines the haphazard development of fringe areas beyond municipal limits, primarily driven by migration and local populations. Historically, this development has been a source of debate and controversy, with concerns surrounding its causes and consequences. This paper aims to equip stakeholders with a systematic approach to manage and develop such land in a way that promotes sustainable and environmentally friendly growth. A Convolutional Neural Network (SDRN) is proposed to predict the effects of fringe area development on the transportation scenario surrounding Pune city. The proposed FADTS-CNN-KMOA technique analyzes spatial data to capture relevant features and predict future outcomes. The CNN analyzes spatial data, such as satellite images or land use grids, and captures spatial features that influence transportation scenarios.Then the Komodo Mlipir Optimization Algorithm (KMOA) method is used to optimize the predicted output from CNN. The system utilizes data from the Landsat 5 dataset and undergoes pre-processing and feature extraction using principal component analysis (PCA) before feeding the extracted features into the CNN for prediction. Statistical analysis of the pre-processed data is conducted to create guidelines for fringe area development. The proposed FADTS-CNN-KMOA is implemented in MATLAB, using the Landsat 5 dataset for calculation. Numerous performance metrics like precision, accuracy, traffic congestion, and economic growth are utilized to measure the efficiency of the proposed method. The obtained result shows that the FADTS-CNN-KMOA technique significantly outperformed existing methods, achieving 98% prediction accuracy, 99% economic growth prediction, 99% accuracy, and 98% precision, compared to existing methods such as FADTS-RNN, FADTS-RFA, and FADTS-SDRN. These results suggest that FADTS-CNN-KMOA can be a valuable technique for stakeholders seeking to manage fringe area development sustainably and effectively.

Keywords

fringe area

social and political pressure

implementation

local planning authority

development control rules

urban pressure

policy

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Information

Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
Publisher(Ko) :건설구조물 내구성혁신 연구센터
Journal Title :International Journal of Sustainable Building Technology and Urban Development
Volume : 15
No :1
Pages :23-42
Received Date : 2023-10-20
Accepted Date : 2023-12-19
DOI :https://doi.org/10.22712/susb.20240003

International Journal of Sustainable Building Technology and Urban Development ISSN:2093-761X(Print) 2093-7628(Online)

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