Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval
The accuracy of an Artificial Neural Network (ANN) depends on the representativeness of the data used to train it. Although it is known that an ANN will function well as long as the pattern of the input data is similar to the testing data, there has been no research on the effect of data "simil...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/41405 |
| _version_ | 1848756136980774912 |
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| author | Chai, S. Veenendaal, Bert West, Geoff Walker, J. |
| author_facet | Chai, S. Veenendaal, Bert West, Geoff Walker, J. |
| author_sort | Chai, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The accuracy of an Artificial Neural Network (ANN) depends on the representativeness of the data used to train it. Although it is known that an ANN will function well as long as the pattern of the input data is similar to the testing data, there has been no research on the effect of data "similarity" on the accuracy of the network outputs. In this paper, an ANN model is used to retrieve soil moisture from the H- and V-polarized brightness temperature obtained. The research discussed in this paper is focused on the standard deviation of the data used for training and testing of the ANN. It is shown that similarity in standard deviation is a good indicator to choose representative training and testing data set. By doing this, the accuracy of retrieval increases from around 22% volume/volume (v/v) of Root Mean Square Error (RMSE) to around 2%(v/v). ©2008 IEEE. |
| first_indexed | 2025-11-14T09:07:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-41405 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:07:25Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-414052017-09-13T14:12:16Z Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval Chai, S. Veenendaal, Bert West, Geoff Walker, J. The accuracy of an Artificial Neural Network (ANN) depends on the representativeness of the data used to train it. Although it is known that an ANN will function well as long as the pattern of the input data is similar to the testing data, there has been no research on the effect of data "similarity" on the accuracy of the network outputs. In this paper, an ANN model is used to retrieve soil moisture from the H- and V-polarized brightness temperature obtained. The research discussed in this paper is focused on the standard deviation of the data used for training and testing of the ANN. It is shown that similarity in standard deviation is a good indicator to choose representative training and testing data set. By doing this, the accuracy of retrieval increases from around 22% volume/volume (v/v) of Root Mean Square Error (RMSE) to around 2%(v/v). ©2008 IEEE. 2008 Conference Paper http://hdl.handle.net/20.500.11937/41405 10.1109/IGARSS.2008.4779087 restricted |
| spellingShingle | Chai, S. Veenendaal, Bert West, Geoff Walker, J. Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval |
| title | Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval |
| title_full | Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval |
| title_fullStr | Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval |
| title_full_unstemmed | Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval |
| title_short | Input pattern according to standard deviation of backpropagation neural network: Influence on accuracy of soil moisture retrieval |
| title_sort | input pattern according to standard deviation of backpropagation neural network: influence on accuracy of soil moisture retrieval |
| url | http://hdl.handle.net/20.500.11937/41405 |