Spatial prediction of categorical variables with the Bayesian Maximum Entropy approach: the Ooypolder case study

2004 ◽  
Vol 55 (4) ◽  
pp. 763-775 ◽  
Author(s):  
D. D’Or ◽  
P. Bogaert
Keyword(s):  
Maximum Entropy ◽  
Spatial Prediction ◽  
Categorical Variables ◽  
Bayesian Maximum Entropy

Improving Bayesian maximum entropy and ordinary Kriging methods for estimating precipitations in a large watershed: a new cluster-based approach

2014 ◽  
Vol 51 (1) ◽  
pp. 43-55 ◽  
Author(s):  
Bardia Bayat ◽  
Mohsen Nasseri ◽  
Gholamreza Naser
Keyword(s):  
Maximum Entropy ◽  
Ordinary Kriging ◽  
Cluster Method ◽  
Annual Precipitation ◽  
Cluster Center ◽  
Bayesian Maximum Entropy ◽  
Mean Annual Precipitation ◽  
Secondary Variable

The main purpose of this research is to investigate spatial variations of mean annual precipitation in a watershed. As a case study, the research focused on the Namak Lake watershed in Iran. Literature provides various techniques for studying spatial patterns of precipitation in a watershed. These techniques often require a large dataset. On the other hand, nonuniform data distribution in a watershed can reduce the accuracy and reliability of the predictions. To overcome these problems, this research applied the cluster method coupled with ordinary Kriging and Bayesian maximum entropy techniques. An estimated point was modified based on the distance from the point to the cluster center. The research considered elevation variations as a secondary variable. A cross-validation technique was used for evaluating the results of mean annual precipitations. The research compared the results of ordinary Kriging and Bayesian maximum entropy methods with and without the application of the clustering method. The research concluded that the cluster-based method can estimate the dynamics of long-term mean annual precipitation more reliably and accurately. The research also revealed more informative results for the cluster-based method.

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