Iirs Artificial Naural network based Urban growth Modeling

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Artificial Naural network based Urban growth Modeling –sandeep Maithani, Husad, IIRS

Artificial Naural network based Urban growth Modeling –sandeep Maithani, Husad, IIRS

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  • 1. ARTIFICIAL NEURAL NETWORKBASED URBAN GROWTH MODELLING Sandeep Maithani S/E “SE” HUSAD, IIRS
  • 2. Need of Urban growth modelling•Urban areas are growing at a very fast rate•Need for Urban growth models to predict areas of future growth.•So that proper infrastructure facilities can be provided in these areas
  • 3. Gaps in urban growth modelling• Subjectivity• Model calibration is done by trail and error• Complicatedness• Models are non spatial in nature•. Grossness.
  • 4. Research Aims•Reduce subjectivity in urban growth modelling•Reduce the model calibration time•Need to couple GIS and RS with urban growth models for modelling urban growth
  • 5. In order to reduce the subjectivity and calibration time : Artificial Neural Network ( ANN) were used• They are able to learn the patterns directly from the database without much human intervention• ANN make no assumption regarding the distributional properties of data• Mixture of data types can be used• No restrictions on using non numeric data• They can solve highly non linear problems
  • 6. Urban growth = f ( dist. to city core, dist. to road, dist. to nearest built-up, Percentage of built up in neighbourhod )Urban growth = ANN ( dist. to city core, dist. to road, dist. to nearest built-up, Percentage of built up in neighbourhod )
  • 7. 11 Feb.1997 23 Dec.2001 13 March 2005
  • 8. 1997 Land cover maps of Dehradun 2001 2005
  • 9. Urban Growth 1997-2001 Urban Growth 2001-2005797 ha. Changed from Non Built-up to Built-up 1108ha. Changed from Non Built-up to Built-up
  • 10. Dist. to Roads Dist. to City CoreFour driving variable grids created in GIS Percentage of Built - upDist. to Built - up
  • 11. Dist. to nearest built-up Dist. To city core Dist. to roads Density of built up in neighbourhod dist. City core dist. Built-up dist. Road density Built-up Target value 0.1677317 0.0031023 0.0072796 0.2 1 0.1676008 0.0031023 0.0025737 0.36 1 0.1675815 0.0031023 0.0025737 0.2 0 0.1674457 0.0043873 0.0081388 0.32 1 0.1672564 0.0031023 0.0156554 0.2 1 0.1672126 0.0043873 0.0025737 0.08 0 0.1669403 0.0031023 0.0072796 0.52 1 0.1669403 0.0031023 0.0072796 0.52 1 0.1664677 0.0031023 0.0077212 0.52 1 0.1659447 0.0031023 0.005755 0.2 1 Training Data NN -output Urban growth 1997-01 0.7 0.8 1=Growth 0.5 0 = No growth ANN 0.55 0.3 0 0.3 0.6 0.7 0.9
  • 12. Multilayer perceptron (MLP) feed-forward Artificial neural network Network Architecture 2400 Training samples Target Value=1 0.85 0.9 e=1-.85 e=1-.9 Output LayerInput Layer Hidden Layer Supervised back-propagation learning algorithm (BP) has been used for training the network Stopping criteria: 1. Fixed number of iterations take place 2. Error drops below a certain level 3. The network starts over training. Input Layer = 4 Neurons 1200 Validation samples to prevent the network from overtraining 1200 Testing samples to ( 1= the generalization capability of the neural network Output Layer =1 Neuron test Growth, 0= No Growth)
  • 13. Network training Input layer Hidden layer Output layer Remote sensing GIS database data f1 f2 Network output Desired output f3 Change Error f4 detection Training dataset OptimalCA simulation weights Stop simulation f’1 Potential for urban growth (P) Database for study area f’2 f’3 yes no Stopping criteria fulfilled f’4 Masking of exclusionary areas Updation of database Allocate cell to built-up If P > threshold value
  • 14. Network Architecture 1.What should be the number of hidden layers 2.What is the number of neurons in each hidden layerThe architecture of neural network was decided1.Heuristically2.Trial and error. more than 50 ANNs were designedHeuristically :The number of nodes in a single hidden layerKanellopoulos and Wilkinson (1997) 3NiHush (1989) 3Ni 3Hecht-Nielsen (1987) 2Ni +1Wang (1994b) 2Ni /3 9Ripley (1993) (Ni+No)/2Paola (1994) 2+No *Ni+1/2 No (Ni2+Ni) -3 12 Ni + NoNo is number of input nodes, Ni is number of output nodes
  • 15. 1. Trial and error. More than 50 ANNs were designed, using single and double hidden layer. 4-6--1 4-3-3-1 4-6-3-1 4-9-3-1 4-12-3-1 4-15-3-1 4-18-3-1 4-21-3-1 4-15--1 4-3-6-1 4-6-6-1 4-9-6-1 4-12-6-1 4-15-6-1 4-18-6-1 4-21-6-1 4-18--1 4-3-9-1 4-6-9-1 4-9-9-1 4-12-9-1 4-15-9-1 4-18-9-1 4-21-9-1 4-21--1 4-3-12-1 4-6-12-1 4-9-12-1 4-12-12-1 4-15-12-1 4-18-12-1 4-21-12-1 4-3-15-1 4-6-15-1 4-9-15-1 4-12-15-1 4-15-15-1 4-18-15-1 4-21-15-1 4-3-18-1 4-6-18-1 4-9-18-1 4-12-18-1 4-15-18-1 4-18-18-1 4-21-18-1 4-3-21-1 4-6-21-1 4-9-21-1 4-12-21-1 4-15-21-1 4-18-21-1 4-21-21-1 Network having a single hidden layer with 9 neurons
  • 16. Dehradun Actual-2001 Simulated-2001 Moran index of 0.29 (Moran Index of 0.29 )(Percentage accuracy of 74%)
  • 17. Simulated-2005 Actual-2005 Moran index of 0.33 (Moran Index of 0.30 )(Percentage accuracy of 76%)
  • 18. 2011
  • 19. Conclusions•ANN helps to reduce the calibration time andsubjectivity in the modelling process•GIS is used for handling of spatial data , to obtain siteattributes and training data for neural network.• ANN is used to reveal the relationships between futureurban growth probability and site attributes
  • 20. Thank you for your kind attention!