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JSRI 2019, 16(1): 211-228 Back to browse issues page
The Ability of Artificial Neural Networks in Learning Dependency of Spatial Data‎
Abbas Tavasoli, Yadollah Waghei *1, Alireza Nazemi
1- , ywaghei@birjand.ac.ir
Abstract:   (197 Views)
 ‎In conventional methods of spatial data analysis‎, ‎such as Kriging‎, ‎the dependency structure of data is estimated‎, ‎modeled‎, ‎and then used for data prediction‎. ‎In contrast‎, ‎the Artificial Neural Network (ANN) approach‎, ‎which is a data-driven approach‎, ‎does not model the data dependency structure‎. ‎Therefore‎, ‎an important question may arise here‎: ‎Does ANN use‎, ‎indirectly‎, ‎spatial dependency structure in data prediction? In this paper‎, ‎we want to answer this question through a simulation study‎. ‎Different dependent and independent spatial data sets are simulated under two spatial structures‎, ‎and the prediction accuracy of ANNs is compared for simulated data‎. ‎It is shown that neural network error for predicting dependent spatial data is much less than that of independent spatial data‎. ‎We conclude that the network can indirectly learn spatial dependence between the observations‎. ‎We also applied the ANN method to an experimentally obtained data set and compared its prediction accuracy with Kriging as a common geostatistical method‎. ‎The results showed that the neural network can be used as an alternative method for spatial data prediction.‎
 
Keywords: Artificial Neural Networks, Spatial dependency, Spatial prediction.‎
Full-Text [PDF 3685 kb]   (55 Downloads)    
Type of Study: Research | Subject: General
Received: 2020/08/4 | Accepted: 2021/01/2 | Published: 2019/09/19
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Tavasoli A, Waghei Y, Nazemi A. The Ability of Artificial Neural Networks in Learning Dependency of Spatial Data‎. JSRI. 2019; 16 (1) :211-228
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