Missing River Discharge Data Imputation Approach using Artificial Neural Network
The issue with missing data in hydrological models are very common and it occurs when no data value was stored during observation. In modelling, the missing data can affect the conclusion that can be drawn from the dataset. This paper presents the study on Levenberg-Marquadt back propagation algorit...
| الحاوية / القاعدة: | ARPN Journal of Engineering and Applied Sciences |
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| المؤلف الرئيسي: | |
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
Asian Research Publishing Network
2015
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| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101170010&partnerID=40&md5=eab1dcbfa5795d1840e563222fff4331 |
| الملخص: | The issue with missing data in hydrological models are very common and it occurs when no data value was stored during observation. In modelling, the missing data can affect the conclusion that can be drawn from the dataset. This paper presents the study on Levenberg-Marquadt back propagation algorithm in predicting missing stream flow data in Langat River Basin. Data series from the upper part of Langat River Basin were applied to build the Artificial Neural Network model. The result indicated good performance of the model with Pearson Correlation, r = 0.97261 for training data and overall data shows r = 0.91925. The study reveals that Levenberg-Marquadt back propagation algorithm for ANN can simulate well in the daily missing stream flow prediction if the model customizes with good configuration. © 2006-2015. Asian Research Publishing Network (ARPN). All rights reserved. |
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| تدمد: | 18196608 |