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avalanche_protection_in_davos_switzerland [2019/05/16 15:28] – [4. Validation and sensitivity analysis] stritihaavalanche_protection_in_davos_switzerland [2023/04/21 15:30] (current) – external edit 127.0.0.1
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   p(Crown_cover_Lidar | Crown_cover) = NormalDist(Crown_cover_Lidar, Crown_cover, 0.12*Crown_cover)   p(Crown_cover_Lidar | Crown_cover) = NormalDist(Crown_cover_Lidar, Crown_cover, 0.12*Crown_cover)
      
-{{:measurement_cpt_distribution.png?200 |}}+{{:measurement_cpt_distribution.png?180 |}}
 // Distribution of actual crown cover, given a measurement of crown cover. The CPT of Crown cover (Lidar) is defined as a normal distribution around the actual crown cover.// // Distribution of actual crown cover, given a measurement of crown cover. The CPT of Crown cover (Lidar) is defined as a normal distribution around the actual crown cover.//
  
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 However, this procedure results in a very large CPT for the node (a line for each combination of parameters and predictor variables). Since the parameter nodes will not be modified with evidence, we can reduce the CPT by using the function “Absorb nodes”, which removes the nodes from the network, but retains the associated information in the reduced CPT.  However, this procedure results in a very large CPT for the node (a line for each combination of parameters and predictor variables). Since the parameter nodes will not be modified with evidence, we can reduce the CPT by using the function “Absorb nodes”, which removes the nodes from the network, but retains the associated information in the reduced CPT. 
  
-{{:empirical_cpt.png?800|}}+{{:empirical_cpt.png?700|}}
  
 //The parameters of an empirical model can be included explicitly as nodes in the network, to account for model uncertainty when calculating the CPT. Then, these nodes can be "absorbed" to reduce the size of the CPT.// //The parameters of an empirical model can be included explicitly as nodes in the network, to account for model uncertainty when calculating the CPT. Then, these nodes can be "absorbed" to reduce the size of the CPT.//
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 For nodes where no data was available (e.g. “Potential detrainment”), we used expert knowledge to quantify the CPT.  To avoid overconfidence, we used the “four-point estimation method” ([[https://doi.org/10.1111/j.1539-6924.2009.01337.x|Speirs-Bridge et al. 2010]]), where we asked the expert to estimate the lowest and highest value they would expect, the most likely value, and their confidence that the true value is within this range ([[https://doi.org/10.1016/j.biocon.2013.03.005|Metcalf and Wallace 2013]]). For example, for a dense evergreen forest on rough terrain, the expert estimated the lowest possible detrainment factor to be 24 Pa, the highest 96 Pa, and the best estimate at 48 Pa, with a confidence of 80%. This gives us the quantiles and mode of the distribution, to which we fitted a simple asymmetric triangular distribution (see figure below).  For nodes where no data was available (e.g. “Potential detrainment”), we used expert knowledge to quantify the CPT.  To avoid overconfidence, we used the “four-point estimation method” ([[https://doi.org/10.1111/j.1539-6924.2009.01337.x|Speirs-Bridge et al. 2010]]), where we asked the expert to estimate the lowest and highest value they would expect, the most likely value, and their confidence that the true value is within this range ([[https://doi.org/10.1016/j.biocon.2013.03.005|Metcalf and Wallace 2013]]). For example, for a dense evergreen forest on rough terrain, the expert estimated the lowest possible detrainment factor to be 24 Pa, the highest 96 Pa, and the best estimate at 48 Pa, with a confidence of 80%. This gives us the quantiles and mode of the distribution, to which we fitted a simple asymmetric triangular distribution (see figure below). 
  
-{{:expert_cpt.png?600|}}+{{:expert_cpt.png?500|}}
  
 //Expert-based distribution of potential detrainment for a dense evergreen forest on rough terrain.// //Expert-based distribution of potential detrainment for a dense evergreen forest on rough terrain.//
avalanche_protection_in_davos_switzerland.1558013302.txt.gz · Last modified: 2023/04/21 15:30 (external edit)