Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India

Vishal Chettry; Keerti Manisha

doi:10.1007/978-981-19-0836-1_30

Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India

Vishal Chettry^*
, Keerti Manisha

^*Corresponding author for this work

Manipal School of Architecture and Planning, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

Globally, rapid and haphazard urban growth has induced land use land cover (LULC) transformations in cities and their surroundings. The cities located in the Himalayan foothills have experienced tremendous urban growth in recent years. In this context, urban growth modeling integrated with remote sensing and geoinformatics assists to predict the future urban growth pattern. Therefore, the urban growth pattern of Jammu Urban Agglomeration (UA) from 1991 to 2021 is assessed in this paper. Shannon’s entropy index assesses the trend of built-up expansion in Jammu UA. Further, the upcoming urban growth for the year 2031 was predicted by integrating artificial neural network-multi-layer perceptron (ANN-MLP) and cellular automata (CA) model. The results revealed a substantial rise in built-up land cover while fallow land, vegetation, agriculture, and water body land cover decreased during 1991–2021. The occurrence of dispersed growth in Jammu UA was specified by the entropy index. The predicted urban growth pattern for the year 2031 showcased a further escalation in the built-up land cover while other land cover categories continued declining trend. Overall, such an urban growth pattern is unsustainable for Jammu UA, and there is an urgent requirement of urban containment measures.

Original language	English
Title of host publication	Modeling, Simulation and Optimization - Proceedings of CoMSO 2021
Editors	Biplab Das, Ripon Patgiri, Sivaji Bandyopadhyay, Valentina Emilia Balas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	387-397
Number of pages	11
ISBN (Print)	9789811908354
DOIs	https://doi.org/10.1007/978-981-19-0836-1_30
Publication status	Published - 2022
Event	International Conference on Modeling, Simulation and Optimization, CoMSO 2021 - Silchar, India Duration: 16-12-2021 → 18-12-2021

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	292
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	International Conference on Modeling, Simulation and Optimization, CoMSO 2021
Country/Territory	India
City	Silchar
Period	16-12-21 → 18-12-21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

All Science Journal Classification (ASJC) codes

General Decision Sciences
General Computer Science

Access to Document

10.1007/978-981-19-0836-1_30

Cite this

Chettry, V., & Manisha, K. (2022). Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India. In B. Das, R. Patgiri, S. Bandyopadhyay, & V. E. Balas (Eds.), Modeling, Simulation and Optimization - Proceedings of CoMSO 2021 (pp. 387-397). (Smart Innovation, Systems and Technologies; Vol. 292). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-0836-1_30

Chettry, Vishal ; Manisha, Keerti. / Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India. Modeling, Simulation and Optimization - Proceedings of CoMSO 2021. editor / Biplab Das ; Ripon Patgiri ; Sivaji Bandyopadhyay ; Valentina Emilia Balas. Springer Science and Business Media Deutschland GmbH, 2022. pp. 387-397 (Smart Innovation, Systems and Technologies).

@inproceedings{094eea41479149f3ae25dd7f204718e8,

title = "Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India",

abstract = "Globally, rapid and haphazard urban growth has induced land use land cover (LULC) transformations in cities and their surroundings. The cities located in the Himalayan foothills have experienced tremendous urban growth in recent years. In this context, urban growth modeling integrated with remote sensing and geoinformatics assists to predict the future urban growth pattern. Therefore, the urban growth pattern of Jammu Urban Agglomeration (UA) from 1991 to 2021 is assessed in this paper. Shannon{\textquoteright}s entropy index assesses the trend of built-up expansion in Jammu UA. Further, the upcoming urban growth for the year 2031 was predicted by integrating artificial neural network-multi-layer perceptron (ANN-MLP) and cellular automata (CA) model. The results revealed a substantial rise in built-up land cover while fallow land, vegetation, agriculture, and water body land cover decreased during 1991–2021. The occurrence of dispersed growth in Jammu UA was specified by the entropy index. The predicted urban growth pattern for the year 2031 showcased a further escalation in the built-up land cover while other land cover categories continued declining trend. Overall, such an urban growth pattern is unsustainable for Jammu UA, and there is an urgent requirement of urban containment measures.",

author = "Vishal Chettry and Keerti Manisha",

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Chettry, V & Manisha, K 2022, Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India. in B Das, R Patgiri, S Bandyopadhyay & VE Balas (eds), Modeling, Simulation and Optimization - Proceedings of CoMSO 2021. Smart Innovation, Systems and Technologies, vol. 292, Springer Science and Business Media Deutschland GmbH, pp. 387-397, International Conference on Modeling, Simulation and Optimization, CoMSO 2021, Silchar, India, 16-12-21. https://doi.org/10.1007/978-981-19-0836-1_30

Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India. / Chettry, Vishal; Manisha, Keerti.
Modeling, Simulation and Optimization - Proceedings of CoMSO 2021. ed. / Biplab Das; Ripon Patgiri; Sivaji Bandyopadhyay; Valentina Emilia Balas. Springer Science and Business Media Deutschland GmbH, 2022. p. 387-397 (Smart Innovation, Systems and Technologies; Vol. 292).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India

AU - Chettry, Vishal

AU - Manisha, Keerti

PY - 2022

Y1 - 2022

N2 - Globally, rapid and haphazard urban growth has induced land use land cover (LULC) transformations in cities and their surroundings. The cities located in the Himalayan foothills have experienced tremendous urban growth in recent years. In this context, urban growth modeling integrated with remote sensing and geoinformatics assists to predict the future urban growth pattern. Therefore, the urban growth pattern of Jammu Urban Agglomeration (UA) from 1991 to 2021 is assessed in this paper. Shannon’s entropy index assesses the trend of built-up expansion in Jammu UA. Further, the upcoming urban growth for the year 2031 was predicted by integrating artificial neural network-multi-layer perceptron (ANN-MLP) and cellular automata (CA) model. The results revealed a substantial rise in built-up land cover while fallow land, vegetation, agriculture, and water body land cover decreased during 1991–2021. The occurrence of dispersed growth in Jammu UA was specified by the entropy index. The predicted urban growth pattern for the year 2031 showcased a further escalation in the built-up land cover while other land cover categories continued declining trend. Overall, such an urban growth pattern is unsustainable for Jammu UA, and there is an urgent requirement of urban containment measures.

AB - Globally, rapid and haphazard urban growth has induced land use land cover (LULC) transformations in cities and their surroundings. The cities located in the Himalayan foothills have experienced tremendous urban growth in recent years. In this context, urban growth modeling integrated with remote sensing and geoinformatics assists to predict the future urban growth pattern. Therefore, the urban growth pattern of Jammu Urban Agglomeration (UA) from 1991 to 2021 is assessed in this paper. Shannon’s entropy index assesses the trend of built-up expansion in Jammu UA. Further, the upcoming urban growth for the year 2031 was predicted by integrating artificial neural network-multi-layer perceptron (ANN-MLP) and cellular automata (CA) model. The results revealed a substantial rise in built-up land cover while fallow land, vegetation, agriculture, and water body land cover decreased during 1991–2021. The occurrence of dispersed growth in Jammu UA was specified by the entropy index. The predicted urban growth pattern for the year 2031 showcased a further escalation in the built-up land cover while other land cover categories continued declining trend. Overall, such an urban growth pattern is unsustainable for Jammu UA, and there is an urgent requirement of urban containment measures.

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Chettry V, Manisha K. Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India. In Das B, Patgiri R, Bandyopadhyay S, Balas VE, editors, Modeling, Simulation and Optimization - Proceedings of CoMSO 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 387-397. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-981-19-0836-1_30

Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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