Comprehensive performance analysis of training functions in flow prediction model using artificial neural network Article Swipe
K C Shikhar
,
Khem Prasad Bhattarai
,
Tang De Shan
,
Saurabh Mishra
,
Ishwar Joshi
,
Anurag Kumar Singh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.17159/wsa/2024.v50.i2.4099
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.17159/wsa/2024.v50.i2.4099
Higher Himalayan catchments are often poorly monitored for hydrological activities involving flood flow prediction for the safety of riverside communities and the successful operation of hydropower projects. This study aimed to estimate the comparative performance of artificial neural network (ANN) based flow prediction models using 10 years of daily river flow data of Kaligandaki catchment at Kotagaun, Nepal, which is a snow-fed catchment in the Himalayan region. The flow prediction models were trained and tested at a hydrological station using the previous 3 days’ river flow data to predict the 1-day ahead flow data. Eight different training functions were employed in an ANN model for comprehensive statistical assessment of accuracy and precision of each training function. The most significant and validated result obtained in this study is the comprehensive comparison of various training functions’ performance, and identification of the most efficient training function for the study case. Among the training functions investigated, the Levenberg-Marquardt backpropagation function exhibits the best performance for the model having Nash-Sutcliffe efficiency, root mean square error and mean absolute error values of 0.866, 209.578 and 75.422, respectively. This study provides a fundamental basis for accurate flow prediction of topographically challenged catchments where hydrological monitoring and data collection may be limited. In particular, this model will help to improve early warning system, hydrological planning, and the safety of riverside communities in the Himalayan region.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17159/wsa/2024.v50.i2.4099
- https://watersa.net/article/download/18543/21342
- OA Status
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- Cited By
- 2
- References
- 57
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- 10
- OpenAlex ID
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https://doi.org/10.17159/wsa/2024.v50.i2.4099Digital Object Identifier
- Title
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Comprehensive performance analysis of training functions in flow prediction model using artificial neural networkWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-04-30Full publication date if available
- Authors
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K C Shikhar, Khem Prasad Bhattarai, Tang De Shan, Saurabh Mishra, Ishwar Joshi, Anurag Kumar SinghList of authors in order
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https://doi.org/10.17159/wsa/2024.v50.i2.4099Publisher landing page
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https://watersa.net/article/download/18543/21342Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://watersa.net/article/download/18543/21342Direct OA link when available
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Artificial neural network, Computer science, Training (meteorology), Artificial intelligence, Machine learning, Physics, MeteorologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.years | 46 |
| abstract_inverted_index.0.866, | 176 |
| abstract_inverted_index.Higher | 0 |
| abstract_inverted_index.Nepal, | 57 |
| abstract_inverted_index.having | 163 |
| abstract_inverted_index.models | 43, 70 |
| abstract_inverted_index.neural | 37 |
| abstract_inverted_index.poorly | 5 |
| abstract_inverted_index.result | 121 |
| abstract_inverted_index.safety | 16, 219 |
| abstract_inverted_index.square | 168 |
| abstract_inverted_index.tested | 74 |
| abstract_inverted_index.values | 174 |
| abstract_inverted_index.209.578 | 177 |
| abstract_inverted_index.75.422, | 179 |
| abstract_inverted_index.days’ | 83 |
| abstract_inverted_index.improve | 211 |
| abstract_inverted_index.network | 38 |
| abstract_inverted_index.predict | 88 |
| abstract_inverted_index.region. | 66, 226 |
| abstract_inverted_index.station | 78 |
| abstract_inverted_index.system, | 214 |
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| abstract_inverted_index.various | 131 |
| abstract_inverted_index.warning | 213 |
| abstract_inverted_index.absolute | 172 |
| abstract_inverted_index.accuracy | 109 |
| abstract_inverted_index.accurate | 188 |
| abstract_inverted_index.employed | 99 |
| abstract_inverted_index.estimate | 31 |
| abstract_inverted_index.exhibits | 156 |
| abstract_inverted_index.function | 142, 155 |
| abstract_inverted_index.limited. | 203 |
| abstract_inverted_index.obtained | 122 |
| abstract_inverted_index.previous | 81 |
| abstract_inverted_index.provides | 183 |
| abstract_inverted_index.snow-fed | 61 |
| abstract_inverted_index.training | 96, 114, 132, 141, 149 |
| abstract_inverted_index.Himalayan | 1, 65, 225 |
| abstract_inverted_index.Kotagaun, | 56 |
| abstract_inverted_index.catchment | 54, 62 |
| abstract_inverted_index.different | 95 |
| abstract_inverted_index.efficient | 140 |
| abstract_inverted_index.function. | 115 |
| abstract_inverted_index.functions | 97, 150 |
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| abstract_inverted_index.monitored | 6 |
| abstract_inverted_index.operation | 23 |
| abstract_inverted_index.planning, | 216 |
| abstract_inverted_index.precision | 111 |
| abstract_inverted_index.projects. | 26 |
| abstract_inverted_index.riverside | 18, 221 |
| abstract_inverted_index.validated | 120 |
| abstract_inverted_index.activities | 9 |
| abstract_inverted_index.artificial | 36 |
| abstract_inverted_index.assessment | 107 |
| abstract_inverted_index.catchments | 2, 194 |
| abstract_inverted_index.challenged | 193 |
| abstract_inverted_index.collection | 200 |
| abstract_inverted_index.comparison | 129 |
| abstract_inverted_index.hydropower | 25 |
| abstract_inverted_index.monitoring | 197 |
| abstract_inverted_index.prediction | 13, 42, 69, 190 |
| abstract_inverted_index.successful | 22 |
| abstract_inverted_index.Kaligandaki | 53 |
| abstract_inverted_index.communities | 19, 222 |
| abstract_inverted_index.comparative | 33 |
| abstract_inverted_index.efficiency, | 165 |
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| abstract_inverted_index.particular, | 205 |
| abstract_inverted_index.performance | 34, 159 |
| abstract_inverted_index.significant | 118 |
| abstract_inverted_index.statistical | 106 |
| abstract_inverted_index.functions’ | 133 |
| abstract_inverted_index.hydrological | 8, 77, 196, 215 |
| abstract_inverted_index.performance, | 134 |
| abstract_inverted_index.comprehensive | 105, 128 |
| abstract_inverted_index.investigated, | 151 |
| abstract_inverted_index.respectively. | 180 |
| abstract_inverted_index.Nash-Sutcliffe | 164 |
| abstract_inverted_index.identification | 136 |
| abstract_inverted_index.backpropagation | 154 |
| abstract_inverted_index.topographically | 192 |
| abstract_inverted_index.Levenberg-Marquardt | 153 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.77567594 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |