SCIENTIFIC PYTHON IN DISASTER SIMULATION AND VISUALIZATION : MODELING AND SIMULATION OF WILDLAND FIRE SPREAD USING CAN GUGAN SELVARAJ

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1 SCIENTIFIC PYTHON IN DISASTER SIMULATION AND VISUALIZATION : MODELING AND SIMULATION OF WILDLAND FIRE SPREAD USING CAN GUGAN SELVARAJ

2 Objectives Present the frame work of Cellular Automaton Network (CAN) to simulate the forest fire spread Study the reliability of the proposed framework 2

3 Need for the Study Forest Fire Affected Forests Safe Forests Others 9% 81% 10% Severe Fires 6.17 % State of Forests in India Causes (Mostly) Impacts Need Forest Map of India 3

4 Fire Behavior in Forests Generally, Fire behavior depends on three components such as Air, Heat and Fuel characteristics. But in case of forest environment, in addition to those it also depends on the topography and weather characteristics. Dynamic Factor Temperature, Wind velocity, Precipitation, Hu midity, Atmospheric Stability etc. Individually affects the behavior Static Factor Elevation, Aspect, Steep ness, Position, Shape etc. Affects the rate of spread significantly Most Important Factor Fuel load, Size and Shape, Density, Chemical comp., Moisture Content, Porosity, Type of Fuel etc. Individually affects the behavior COMPLEX BEHAVIOR 4

5 Fire Spread Principle The spread of fire in forests also depends on complex interaction between the heat transfer mechanisms (conduction, convection or radiation) leading to formation of surface or crown fires. Crown Fire Convection & Radiation Spatially, its spreads in all the directions radially from the fire point. Surface Fire Conduction & Radiation 5

6 Forest Fire Simulation using CA Cellular Automata Space time models that represents a computational paradigm for complex phenomena that evolve basis of local interactions. The efficiency of simulation depends on type of grid, neighborhood, transition rule and global states. Process Flow of CA Simulation Fire Spread Phenomena CA Neighborhood & 1. Choice of CA Grid and Possible States. 2. Formulate Neighborhood & Transition Rule based on the phenomena. 3. Formulate the iterative process for simulation. Transition Rule 6

7 Model Description Grid : Rectangular Global States : Combustible, In- Combustible, Burnt / Burning Neighborhood : Moore Fire Spread Rule Conventional Fire Spread Rule (CFSR) Improved Fire Spread Rule (IFSR) In case of CFSR the fire spreads in a square pattern but in case of IFSR the fire spreads in a approx. circular pattern as given by Cunbin et.al. using Rothermel model and Huygens principle. Thus IFSR is found to be more reliable. Homogenous Forest Model Fire Spread Trend (Analytical) Fire Spread Trend (CFSR) Fire Spread Trend (IFSR) 7

8 Cellular Automata Network (CAN) Model Fire Simulation Model Elevation Slope Aspect Fuel Model Canopy Cover Canopy Height Crown Base Ht. Crown Density Fuel Type Model Description Grid : Rectangular Global States : Combustible, In- Combustible, Burnt/B- -urning Neighborhood : IFSR CAN Layers considered For study Extended version of conventional CA approach Provides flexible and reliable framework to simulate multivariate phenomenon Slope Elevation Fuel Type Fuel Density 8

9 Fire Simulation using CAN Model A Case Study : Mizoram Mizoram Location Map Land Cover 21,078 sq.km Forest Cover 91% Terrain Char. Steep Slopes Climate Wet, High Rainfall, equable temp., High Relative Humidity Forest Fire Very High Vulnerability CAN Layers Considered For study Elevation : Obtained from CARTOSAT DEM V3 Slope : Computed from CARTOSAT DEM V3 Fuel Type : Hypothetically Generated Fuel Density : Computed using FCD Model using IRS LISS III Imagery 2012 (Azazi et.al. 2008) 9

10 CAN Layers Elevation Slope Morphometry Forest Density Fuel Type 10

11 a Forest Fire Simulation using CAN : Results Fire Spread Trend Fire Spread Trend Cases b a Conventional CA Model with Fuel Type b CAN Model with Fuel Type + Slope c d c CAN Model with Fuel Type + FCD d CAN Model with Fuel Type + Slope + FCD 11

12 Forest Fire Simulation using CAN : Results CA Vs CAN Model a a d CA Model d CAN Model (FT + FS + FCD) 12

13 Forest Fire Simulation using CAN : Results CCA Vs CAN Model CAN Models b b FT + FS c c d d FT + FCD FT + FS + FCD 13

14 Conclusions CA models are found to be more suitable to study the behavior of fire in the forest environment. CAN models are more preferable over the conventional CA approach for their accuracy because of their multivariate considerations. CAN models with more precise layers are found to be more reliable and accurate. 14

15 Packages Used Numpy For matrix manioulations Matplotlib For the graphs, contours Math for mathematical expressions Image Image processing Sys, Shutil for kernel or terminal excecutions GDAL Satelite image processing FFMpeg Videos. and more 15

16 Future Scope Inclusion of Influence of wind Crown fire spread behavior Random spot fire behavior Incorporating more number of influential factors such as canopy height, aspect, etc. in CAN model Application of CAN model to study the, Post effects of forest fires on the stability of forest slopes Statistics of loss of fauna and flora Behavior of fire spread Etc. Can be reverse engineered,to find the origin of the fire 16

17 Thank You For your kind attention Research is creating a new knowledge