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- Devendran, Aarthi Aishwarya, 1986-, et al.
(författare)
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Analysis and Prediction of Urban Growth Using Neural-Network-Coupled Agent-Based Cellular Automata Model for Chennai Metropolitan Area, Tamil Nadu, India
- 2019
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Ingår i: Journal of the Indian Society of Remote Sensing. - : Springer. - 0255-660X .- 0974-3006. ; 47:9, s. 1515-1526
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Tidskriftsartikel (refereegranskat)abstract
- Chennai is one of the most densely populated cities in India facing challenges in shifting the city to metropolitan or mega city in the last two decades with continuing agglomeration. To model the growth of Chennai city, we have used cellular automata-based urban growth models based on the historical datasets. In the present study, urban growth of Chennai Metropolitan Area (CMA) was predicted for the year 2017 based on 2010 and 2013 dataset and Chennai city master plan using neural-network-coupled agent-based cellular automata (NNACA) model. Eight different agents of urbanization including transportation, hotspots, and industries were used in the prediction modeling. On validating the 2017 predicted outputs, NNACA model with hotspots proved to be better (hits: 498.52 km2) than that of without hotspots (hits: 488.31 km2). Out of the total eight agents of urbanization, the most influencing agent of urbanization of 2017 was identified to be the neighborhood of ‘Existing built-up of 2013’ using ‘sensitivity analysis’. Further, the urban sprawl of CMA for 2010, 2013 and 2017 was measured through Shannon’s entropy. The study area was divided into five directional and distance-based zones with the State Secretariat as the center. Entropy values suggest the need for more careful planning for further development in the southern region of CMA which has undergone congested urban growth while urbanization is dispersed in the northern part of the study region which can be thought for future urban developments.
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| 2. |
- Devendran, Aarthi Aishwarya, 1986-, et al.
(författare)
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Comparison of Urban Growth Modeling Using Deep Belief and Neural Network Based Cellular Automata Model : A Case Study of Chennai Metropolitan Area, Tamil Nadu, India
- 2019
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Ingår i: Journal of Geographic Information System. - : Scientific Research Publishing. - 2151-1950 .- 2151-1969. ; 11:1, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Urban Growth Models (UGMs) are very essential for a sustainable development of a city as they predict the future urbanization based on the presentscenario. Neural Network based Cellular Automata models have proved topredict the urban growth more close to reality. Recently, deep learning basedtechniques are being used for the prediction of urban growth. In this currentstudy, urban growth of Chennai Metropolitan Area (CMA) of 2017 was predicted using Neural Network based Cellular Automata (NN-CA) model andDeep belief based Cellular Automata (DB-CA) model using 2010 and 2013urban maps. Since the study area experienced congested type of urbangrowth, “Existing Built-Up” of 2013 alone was used as the agent of urbanization to predict urban growth in 2017. Upon validating, DB-CA model provedto be the better model, as it predicted 524.14 km2 of the study area as urbanwith higher accuracy (kappa co-efficient: 0.73) when compared to NN-CAmodel which predicted only 502.42 km2 as urban (kappa co-efficient: 0.71),while the observed urban cover of CMA in 2017 was 572.11 km2. This studyalso aimed at analyzing the effects of different types of neighbourhood configurations (Rectangular: 3 × 3, 5 × 5, 7 × 7 and Circular: 3 × 3) on the prediction output based on DB-CA model. To understand the direction and type ofthe urban growth, the study area was divided into five distance based zoneswith the State Secretariat as the center and entropy values were calculated forthe zones. Results reveal that Chennai Corporation and its periphery experience congested urbanization whereas areas away from the Corporationboundary follow dispersed type of urban growth in 2017.
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