Validation of an agent-based model of shifting agriculture
1. Validation of an Agent-based Model of Shifting Agriculture A village case study from uplands of Vietnam The An Ngo Linda See Frances Drake
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5. Land Cover Transition An framework of Updating vegetation transition Transition probability = f { Cover (t-1) , Clearing, Logging, Vegetation growth } Vegetation growth = f { fallow-age, soil, neighbour-patches } Dense forest with dominant species Clearing and Continuous Intervention Burning Selected logging Natural succession C5. Secondary/regenerated forest (Dense forest) C4. Bushes and scattered wood trees (Open forest) C3. Tall grasses and shrubs C2. Short grasses or bare land C1. Crops
7. Validation Process Initial model 2. Calibration Selecting the range of values for the model parameters 1. Sensitivity Identifying significant parameters 3. Output validation Do predicted results match reality? Fully validated model
8. Data for Validation (Note: The starting year of the simulation is 2000) Validation process Data Measurement description (1) Sensitivity analysis Land cover map Simulated 2007 vs. satellite 2000 (2) Calibration (GAs) Land use, Land cover map Simulated 2005 vs. Satellite 2005 (3) Output validation: - Land cover change Land cover map Simulated 2006 vs. Satellite 2006
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12. Visual Comparison Between the Model Output and the Reference Data Reference map 2000 Simulated map 2006 Reference map 2006
Several routines: three important ones will be described
Fuzzy logic for assessing transition probability (linear monotonically increasing membership) Identify the threshold value of the transition by GAs
This is the “structural validation” (Carley, 1996)
Data: depends on the availability of the data
Sequential Bifurcation (SB) estimates the sensitivity of the parameters in the model by: Identifying the values of the parameters that could result in low and high model outputs, Switching on (use the high values) and off (use the low values) to determine the different in the model outputs Estimating the factor effects using the linear regression model
GA: Calibrated parameters are randomly assigned values (within their ranges) and used to run the model. Model output are then compared with the real data. The value set of parameters that produce the highest accuracy will be selected by the GA
MRG: compare the structure land cover between 2 maps. This is undertaken at various resolutions; start by 1x1 pixel and increase the size until they cover all the map. This measurement takes into the consideration about the patterns of land cover (not the only number of pixel correct matched as the other methods do) MRG compares: - Null model: 2000 map (no change until 2006) with reference 2006 Complete random predicted map with reference 2006 Simulated map in 2006 with reference map (real data) higher than all others The acceptable Ft values from previous research often range from 7.0 – 9.0. The result (8.1) is between this range
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