Modelling urban dynamics in rapidly urbanising Indian cities

Hariharan, Bharath; Chandan, M. C.; Vinay, S.; Ramachandra, T. V.

doi:10.1016/j.ejrs.2017.08.002

Cited by 75 publications

(33 citation statements)

References 41 publications

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“…The study is based on an evaluation of relevant sociophysical parameters in an indicator-based vulnerability assessment process to derive vulnerability score for the region. Later the score is put along with predicted land use map of 2050 (Bharath et al, 2017) to delineate hotspots needing major adaptation for sustainability. The hotspots are highly vulnerable areas with the potential of higher density, thus greater exposure to flood hazards in coming future.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Capacity Assessment for A Flood Vulnerable Region through Land Use Modelling and Socio Economic and Physical Indicators

Narendr¹,

Das²,

Shivamurthy³

et al. 2020

J ENVIRON INFORM LET

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Resistance from climate extremes requires robust adaptation strategy, especially in regions that have greater density and limited resource availability. Weather extremes, such as anomalous precipitation leading to floods have now become a frequent global phenomenon. These situations could lead to unforeseen disaster if the region remains under-equipped to adapt against the vulnerabilities. Therefore, the research objective aims to explore the current status of adaptability within proposed capital region of Andhra Pradesh on backdrop of future urban growth that region may undergo. Andhra Pradesh Capital Region (APCR) has experienced severe flood events in past due to intense rainfall, overflowing Krishna river and causing inundation due to sudden releases from reservoirs upstream. An index based assessment of Mandalas were carried out by selecting relevant factors through various literature reviews. The method comprises of assigning comparative scores to the mandals based on their performance for each parameter and categorizing them on a Likert scale of 1 to 5. The selected parameters are then prioritized by adding weights through pairwise comparison techniques using Analytical Hierarchical Process. Risk Map for the region was developed using weighted sum of flood vulnerability factors and urban development scenario of 2050. The results substantiate that the regional vulnerability is cumulatively influenced by exposure and sensitivity factors. The empirical findings identify female literacy and elevation as major contributor to flood vulnerability. Requirement for immediate interventions were suggested for the mandals with higher vulnerability and greater scope of urban transformation. The proposed method will help in quick identification of susceptible mandals that may suffer higher vulnerability in future. The method proposed will also be effective for formulation policies for redirecting scarce resources in areas needing adaptation against climate disasters.

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Section: Introductionmentioning

confidence: 99%

Adaptive Capacity Assessment for A Flood Vulnerable Region through Land Use Modelling and Socio Economic and Physical Indicators

Narendr¹,

Das²,

Shivamurthy³

et al. 2020

J ENVIRON INFORM LET

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“…While CA is a common algorithm to model urban growth, its precise calibration is knowledge‐intensive and time‐consuming due to the high degree of uncertainty (Roodposhti et al, 2020) and the process can be challenging, resulting in less precise urban growth modeling. Since some parts of the real‐world systems are not reproduced by the model, validating the results of the model is essential (Bharath, Chandan, Vinay, & Ramachandra, 2018; Liu, 2009). Calibration of CA is a process of estimating the best combination of CA values such that the modeled urban growth can better match the real process (Shan, Alkheder, & Wang, 2008).…”

Section: Introductionmentioning

confidence: 99%

Pattern‐based calibration of cellular automata by genetic algorithm and Shannon relative entropy

Momeni

Antipova

2020

Transactions in GIS

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While cellular automata (CA) are considered an effective algorithm to model urban growth, their precise calibration can be challenging. The Shannon relative index (SRI) is an indicator of urban sprawl accounting for dispersion or concentration of built‐up/non‐built‐up areas. This study uses SRIs directly in the calibration of CA as patterns, applying a genetic algorithm (GA). Moreover, the kappa coefficient is used in the calibration process. CA was calibrated using data for 2001 and 2006 and validated using 2011 data to model urban growth in Shelby County, TN. Results indicate that the kappa coefficient achieves the highest value using the proposed method (89.48%) compared with a GA without patterns (86.15%, which underestimates 32.22 km2) or logistic regression (85.83%, which underestimates 36.76 km2). A more precise calibration of urban growth using the proposed method helps city planners to provide more realistic models for the future of the region.

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“…Markov chain method is developed for the time-based Multi-Objective land distribution, multi-criteria analysis and cellular automata (CA) is used particularly for probable land use source. focus of results on the main three the primary was formed through involved variation inside the land cover categories, the second was conducted utilizing just two land cover maps for the standardization of method, and therefore the third was created supporting the idea that temporal multi-objective land allocation (Araya et al, 2010, H.A. Bharath, et al 2017.…”

Section: Introductionmentioning

confidence: 99%

Land Change Prediction using Markov Change Multi-Layer Perceptron in Navi Mumbai, Maharashtra, India

Patil¹,

Jamgade²

2019

IJITEE

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Long-term evaluation of land change and future prediction change is extremely important for planning and land use management. This research conducted for the analyze future prediction change in the study area Navi Mumbai. For this prediction analysis used satellite images year from 1998, 2008 and 2018 are taken. Thus, the change detection obtained from land use and land cover assist in most favourable solutions for the choice, planning, implementation, and observance of development schemes. To meet the increasing demands of human need, land management is required. In this work for upcoming predict year, Markov change model is used for simulating 2028 year. It will give vital and useful information on future development and planning. And also, is easy for continuing to monitor land change for the large area due to natural human activities and the effect of natural resources.

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Modelling urban dynamics in rapidly urbanising Indian cities

Cited by 75 publications

References 41 publications

Adaptive Capacity Assessment for A Flood Vulnerable Region through Land Use Modelling and Socio Economic and Physical Indicators

Adaptive Capacity Assessment for A Flood Vulnerable Region through Land Use Modelling and Socio Economic and Physical Indicators

Pattern‐based calibration of cellular automata by genetic algorithm and Shannon relative entropy

Land Change Prediction using Markov Change Multi-Layer Perceptron in Navi Mumbai, Maharashtra, India

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