Computational approaches for annual maximum river flow series
Studies of annual peak discharge and its temporal variations are widely used in the planning and decision making process of water resources management. Very recently, soft computing techniques are gaining ground for time series analysis of hydrological events such as rainfall and runoff. In this stu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-03-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447915001422 |
| _version_ | 1818647233954840576 |
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| author | Harinarayan Tiwari Subash Pd. Rai Nayan Sharma Dheeraj Kumar |
| author_facet | Harinarayan Tiwari Subash Pd. Rai Nayan Sharma Dheeraj Kumar |
| author_sort | Harinarayan Tiwari |
| collection | DOAJ |
| description | Studies of annual peak discharge and its temporal variations are widely used in the planning and decision making process of water resources management. Very recently, soft computing techniques are gaining ground for time series analysis of hydrological events such as rainfall and runoff. In this study Artificial Neural Network (ANN) has been used in combination with wavelet to model the annual maximum flow discharge of rivers. The results of ANN-Wavelet (WANN) model indicate overall low coherence (R2 = 0.39) better than ANN (R2 = 0.31) in isolation. In the present analysis, the authors also conceded a probabilistic distributional analysis of river flow time series which has greater potential to better reflect peak flow dynamics. The results highlight that the overall performance of probability distribution models is superior to WANN model. Instead of that WANN is better than probabilistic models to find the global maxima of the series. |
| first_indexed | 2024-12-17T00:59:17Z |
| format | Article |
| id | doaj.art-88701ee9af9541b89887910cb38896ba |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-12-17T00:59:17Z |
| publishDate | 2017-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-88701ee9af9541b89887910cb38896ba2022-12-21T22:09:31ZengElsevierAin Shams Engineering Journal2090-44792017-03-0181515810.1016/j.asej.2015.07.016Computational approaches for annual maximum river flow seriesHarinarayan TiwariSubash Pd. RaiNayan SharmaDheeraj KumarStudies of annual peak discharge and its temporal variations are widely used in the planning and decision making process of water resources management. Very recently, soft computing techniques are gaining ground for time series analysis of hydrological events such as rainfall and runoff. In this study Artificial Neural Network (ANN) has been used in combination with wavelet to model the annual maximum flow discharge of rivers. The results of ANN-Wavelet (WANN) model indicate overall low coherence (R2 = 0.39) better than ANN (R2 = 0.31) in isolation. In the present analysis, the authors also conceded a probabilistic distributional analysis of river flow time series which has greater potential to better reflect peak flow dynamics. The results highlight that the overall performance of probability distribution models is superior to WANN model. Instead of that WANN is better than probabilistic models to find the global maxima of the series.http://www.sciencedirect.com/science/article/pii/S2090447915001422ProbabilityWaveletANNKosiPeak discharge |
| spellingShingle | Harinarayan Tiwari Subash Pd. Rai Nayan Sharma Dheeraj Kumar Computational approaches for annual maximum river flow series Ain Shams Engineering Journal Probability Wavelet ANN Kosi Peak discharge |
| title | Computational approaches for annual maximum river flow series |
| title_full | Computational approaches for annual maximum river flow series |
| title_fullStr | Computational approaches for annual maximum river flow series |
| title_full_unstemmed | Computational approaches for annual maximum river flow series |
| title_short | Computational approaches for annual maximum river flow series |
| title_sort | computational approaches for annual maximum river flow series |
| topic | Probability Wavelet ANN Kosi Peak discharge |
| url | http://www.sciencedirect.com/science/article/pii/S2090447915001422 |
| work_keys_str_mv | AT harinarayantiwari computationalapproachesforannualmaximumriverflowseries AT subashpdrai computationalapproachesforannualmaximumriverflowseries AT nayansharma computationalapproachesforannualmaximumriverflowseries AT dheerajkumar computationalapproachesforannualmaximumriverflowseries |