An ANFIS-based approach for predicting the bed load for moderately sized rivers
A total of 346 sets of bed-load data obtained from the Kinta River, Pari River, Kerayong River and Langat River were analyzed using four common bed-load equations. These assessments, based on the median sediment size (d50), show that the existing equations were unable to predict the measured bed loa...
| الحاوية / القاعدة: | Journal of Hydro-Environment Research |
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| المؤلف الرئيسي: | |
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
2009
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| الوصول للمادة أونلاين: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-67349204543&doi=10.1016%2fj.jher.2008.10.003&partnerID=40&md5=9d10c48cd7769043e715af7ad5d43b8c |
| id |
Azamathulla H.Md.; Chang C.K.; Ab. Ghani A.; Ariffin J.; Zakaria N.A.; Abu Hasan Z. |
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| spelling |
Azamathulla H.Md.; Chang C.K.; Ab. Ghani A.; Ariffin J.; Zakaria N.A.; Abu Hasan Z. 2-s2.0-67349204543 An ANFIS-based approach for predicting the bed load for moderately sized rivers 2009 Journal of Hydro-Environment Research 3 1 10.1016/j.jher.2008.10.003 https://www.scopus.com/inward/record.uri?eid=2-s2.0-67349204543&doi=10.1016%2fj.jher.2008.10.003&partnerID=40&md5=9d10c48cd7769043e715af7ad5d43b8c A total of 346 sets of bed-load data obtained from the Kinta River, Pari River, Kerayong River and Langat River were analyzed using four common bed-load equations. These assessments, based on the median sediment size (d50), show that the existing equations were unable to predict the measured bed load accurately. All existing equations over-predicted the measured values, and none of the existing bed-load equations gave satisfactory performance when tested on local river data. Therefore, the present study applies a new soft computing technique, i.e. an adaptive neuro-fuzzy inference system (ANFIS), to better predict measured bed-load data. Validation of the developed network (ANFIS) was performed using a new set of bed-load data collected at Kulim River. The results show that the recommended network can more accurately predict the measured bed-load data when compared to an equation based on a regression method. © 2008 International Association for Hydraulic Engineering and Research, Asia Pacific Division. 15706443 English Article |
| author |
2-s2.0-67349204543 |
| spellingShingle |
2-s2.0-67349204543 An ANFIS-based approach for predicting the bed load for moderately sized rivers |
| author_facet |
2-s2.0-67349204543 |
| author_sort |
2-s2.0-67349204543 |
| title |
An ANFIS-based approach for predicting the bed load for moderately sized rivers |
| title_short |
An ANFIS-based approach for predicting the bed load for moderately sized rivers |
| title_full |
An ANFIS-based approach for predicting the bed load for moderately sized rivers |
| title_fullStr |
An ANFIS-based approach for predicting the bed load for moderately sized rivers |
| title_full_unstemmed |
An ANFIS-based approach for predicting the bed load for moderately sized rivers |
| title_sort |
An ANFIS-based approach for predicting the bed load for moderately sized rivers |
| publishDate |
2009 |
| container_title |
Journal of Hydro-Environment Research |
| container_volume |
3 |
| container_issue |
1 |
| doi_str_mv |
10.1016/j.jher.2008.10.003 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-67349204543&doi=10.1016%2fj.jher.2008.10.003&partnerID=40&md5=9d10c48cd7769043e715af7ad5d43b8c |
| description |
A total of 346 sets of bed-load data obtained from the Kinta River, Pari River, Kerayong River and Langat River were analyzed using four common bed-load equations. These assessments, based on the median sediment size (d50), show that the existing equations were unable to predict the measured bed load accurately. All existing equations over-predicted the measured values, and none of the existing bed-load equations gave satisfactory performance when tested on local river data. Therefore, the present study applies a new soft computing technique, i.e. an adaptive neuro-fuzzy inference system (ANFIS), to better predict measured bed-load data. Validation of the developed network (ANFIS) was performed using a new set of bed-load data collected at Kulim River. The results show that the recommended network can more accurately predict the measured bed-load data when compared to an equation based on a regression method. © 2008 International Association for Hydraulic Engineering and Research, Asia Pacific Division. |
| publisher |
|
| issn |
15706443 |
| language |
English |
| format |
Article |
| accesstype |
|
| record_format |
scopus |
| collection |
Scopus |
| _version_ |
1828987884442484736 |