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- Devendran, Aarthi Aishwarya, 1986-, et al.
(författare)
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A review on accuracy and uncertainty of spatial data and analyses with special reference to urban and hydrological modelling
- 2014
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Ingår i: ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.. - : Copernicus publications. ; , s. 171-178
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Konferensbidrag (refereegranskat)abstract
- Data quality for GIS processing and analysis is becoming an increased concern due to the accelerated application of GIS technology for problem solving and decision making roles. Uncertainty in the geographic representation of the real world arises as these representations are incomplete. Identification of the sources of these uncertainties and the ways in which they operate in GIS based representations become crucial in any spatial data representation and geospatial analysis applied to any field of application. This paper reviews the articles on the various components of spatial data quality and various uncertainties inherent in them and special focus is paid to two fields of application such as Urban Simulation and Hydrological Modelling. Urban growth is a complicated process involving the spatio-temporal changes of all socio-economic and physical components at different scales. Cellular Automata (CA) model is one of the simulation models, which randomly selects potential cells for urbanisation and the transition rules evaluate the properties of the cell and its neighbour. Uncertainty arising from CA modelling is assessed mainly using sensitivity analysis including Monte Carlo simulation method. Likewise, the importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures. The Soil and Water Assessment Tool (SWAT) is a continuous time watershed model to evaluate various impacts of land use management and climate on hydrology and water quality. Hydrological model uncertainties using SWAT model are dealt primarily by Generalized Likelihood Uncertainty Estimation (GLUE) method.
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| 2. |
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| 3. |
- 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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| 4. |
- 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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| 5. |
- Devendran, Aarthi Aishwarya, 1986-, et al.
(författare)
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Urban growth prediction using neural network coupled agents-based Cellular Automata model for Sriperumbudur Taluk, Tamil Nadu, India
- 2018
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Ingår i: Egyptian Journal of Remote Sensing and Space Science. - : Elsevier. - 1110-9823 .- 2090-2476. ; 21:3, s. 353-362
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Tidskriftsartikel (refereegranskat)abstract
- Unplanned urbanization would pose serious threats to both environment and mankind. Hence, urbangrowth model (UGM) becomes mandatory to predict future growth of a city. In the current study, urbangrowth of Sriperumbudur Taluk, Tamil Nadu, India was predicted using three types of Cellular Automata(CA) model namely Traditional CA (TCA) model, Agents based Cellular Automata (ACA) Model and NeuralNetwork coupled Agents- based Cellular Automata (NNACA) model. The urban maps of the study regionfor the years 2009, 2013 and 2016 along with the influencing agents of urbanization namely transporta-tion, industries, elevation and also hotspot locations based on the Government policy were used in themodeling. Analytical Hierarchical Process (AHP) technique was adopted to estimate the weights of theagents for suitability map preparation in ACA model. On validating 2016 predicted outputs, NNACAmodel proved to be the better urban model (kappa coefficient - 0.72) when compared to TCA and ACAmodels (kappa coefficient - 0.6 each). Shannon’s entropy measure revealed that the urbanization is con-centrated in the north-east direction and it is predicted to have an urban sprawl area of 157 km2in 2020using NNACA model while the observed urbanization is 113 km2of the area in 2016.
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