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Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia

Rainfall data can be regarded as the most essential input for various applications in hydrological sciences. Continuous rainfall data with adequate length is the main requirement to solve complex hydrological problems. Mostly in developing countries hydrologists are still facing problems of missing...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Nawaz, N., Harun, S., Othman, R., Heryansyah, A.
التنسيق: مقال
اللغة:English
منشور في: Penerbit UTM Press 2016
الموضوعات:
TK5101-6720 Telecommunication
الوصول للمادة أونلاين:http://eprints.utm.my/id/eprint/70050/1/NadeemNawaz2016_Neuro-FuzzySystemsApproachtoInfill.pdf
http://eprints.utm.my/id/eprint/70050/
http://dx.doi.org/10.11113/jt.v78.9227
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id my.utm.70050
record_format eprints
spelling my.utm.700502017-11-22T00:45:13Z http://eprints.utm.my/id/eprint/70050/ Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia Nawaz, N. Harun, S. Othman, R. Heryansyah, A. TK5101-6720 Telecommunication Rainfall data can be regarded as the most essential input for various applications in hydrological sciences. Continuous rainfall data with adequate length is the main requirement to solve complex hydrological problems. Mostly in developing countries hydrologists are still facing problems of missing rainfall data with inadequate length. Researchers have been applying a number of statistical and data driven approaches to overcome this insufficiency. This study is an application of neuro-fuzzy system to infill the missing rainfall data for Klang River catchment. Pettitt test, standard normal homogeneity test (SNHT) and Von Neumann Ratio (VNR) tests were performed to check the homogeneity of rainfall data. The neuro-fuzzy model performances were assessed both in calibration and validation stages based on statistical measures such as coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). To evaluate the performance of the neuro-fuzzy system model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The neuro-fuzzy system model gave better performances in both stages for the best input combinations. The missing rainfall data was predicted using the input combination with best performances. The results of this study showed the effectiveness of the neuro-fuzzy systems and it is recommended as a prominent tool for filling the missing data. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/70050/1/NadeemNawaz2016_Neuro-FuzzySystemsApproachtoInfill.pdf Nawaz, N. and Harun, S. and Othman, R. and Heryansyah, A. (2016) Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia. Jurnal Teknologi, 78 (42533). pp. 15-21. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v78.9227 DOI:10.11113/jt.v78.9227
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK5101-6720 Telecommunication
spellingShingle TK5101-6720 Telecommunication
Nawaz, N.
Harun, S.
Othman, R.
Heryansyah, A.
Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia
description Rainfall data can be regarded as the most essential input for various applications in hydrological sciences. Continuous rainfall data with adequate length is the main requirement to solve complex hydrological problems. Mostly in developing countries hydrologists are still facing problems of missing rainfall data with inadequate length. Researchers have been applying a number of statistical and data driven approaches to overcome this insufficiency. This study is an application of neuro-fuzzy system to infill the missing rainfall data for Klang River catchment. Pettitt test, standard normal homogeneity test (SNHT) and Von Neumann Ratio (VNR) tests were performed to check the homogeneity of rainfall data. The neuro-fuzzy model performances were assessed both in calibration and validation stages based on statistical measures such as coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). To evaluate the performance of the neuro-fuzzy system model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The neuro-fuzzy system model gave better performances in both stages for the best input combinations. The missing rainfall data was predicted using the input combination with best performances. The results of this study showed the effectiveness of the neuro-fuzzy systems and it is recommended as a prominent tool for filling the missing data.
format Article
author Nawaz, N.
Harun, S.
Othman, R.
Heryansyah, A.
author_facet Nawaz, N.
Harun, S.
Othman, R.
Heryansyah, A.
author_sort Nawaz, N.
title Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia
title_short Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia
title_full Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia
title_fullStr Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia
title_full_unstemmed Neuro-fuzzy systems approach to infill missing rainfall data for Klang River Catchment, Malaysia
title_sort neuro-fuzzy systems approach to infill missing rainfall data for klang river catchment, malaysia
publisher Penerbit UTM Press
publishDate 2016
url http://eprints.utm.my/id/eprint/70050/1/NadeemNawaz2016_Neuro-FuzzySystemsApproachtoInfill.pdf
http://eprints.utm.my/id/eprint/70050/
http://dx.doi.org/10.11113/jt.v78.9227
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score 13.251813

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