A new quality control procedure based on non-linear autoregressive neural network for validating raw river stage data
The main purpose of this work is the develop of a new quality control method based on non-linear autoregressive neural networks (NARNN) for validating hydrological information, more specifically of 10-min river stage data, for automatic detection of incorrect records. To assess the effectiveness of this new approach, a comparison with adapted conventional validation tests extensively used for hydro-meteorological data was carried out. Different parameters of NARNN and their stability were also analyzed in order to select the most appropriate configuration for obtaining the optimal performance. A set of errors of different magnitudes was artificially introduced into the dataset to evaluate detection efficiency. The NARNN method detected more than 90% of altered records, when the magnitude of error introduced was very high, while conventional tests detected only around 13%. In addition, the NARNN method maintained a similar efficiency at the intermediate and lower error ratios, while the conventional tests were not able to detect more than 6% of erroneous data. © 2013.
| Main Authors: | , , , |
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| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Elsevier
2014-03-14
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| Subjects: | Validation, Quality control, Non-linear autoregressive neural networks, River stage data, |
| Online Access: | http://hdl.handle.net/10261/90218 |
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| Summary: | The main purpose of this work is the develop of a new quality control method based on non-linear autoregressive neural networks (NARNN) for validating hydrological information, more specifically of 10-min river stage data, for automatic detection of incorrect records. To assess the effectiveness of this new approach, a comparison with adapted conventional validation tests extensively used for hydro-meteorological data was carried out. Different parameters of NARNN and their stability were also analyzed in order to select the most appropriate configuration for obtaining the optimal performance. A set of errors of different magnitudes was artificially introduced into the dataset to evaluate detection efficiency. The NARNN method detected more than 90% of altered records, when the magnitude of error introduced was very high, while conventional tests detected only around 13%. In addition, the NARNN method maintained a similar efficiency at the intermediate and lower error ratios, while the conventional tests were not able to detect more than 6% of erroneous data. © 2013. |
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