Flood forecasting and flood flow modeling in a river system using ANN
In terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2021-10-01
|
| Series: | Water Practice and Technology |
| Subjects: | |
| Online Access: | http://wpt.iwaponline.com/content/16/4/1194 |
| _version_ | 1818839409706926080 |
|---|---|
| author | S. Agarwal P. J. Roy P. Choudhury N. Debbarma |
| author_facet | S. Agarwal P. J. Roy P. Choudhury N. Debbarma |
| author_sort | S. Agarwal |
| collection | DOAJ |
| description | In terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of a storage parameter. Storage characteristics were presented implicitly and explicitly for various sections in a river system satisfying the continuity norm and mass balance flow. Furthermore, the multiple-input multiple-output (MIMO) model form having two base architectures, namely, MIMO-1 and MIMO-2, was accounted for learning fractional storage and actual storage variations and characteristics in a given model form. The model architecture was also obtained by using a trial-and-error approach, while the network architecture was acquired by employing gamma memory along with use of the multi-layer perceptron model form. Moreover, this paper discusses the comparisons and differences between both models. The model performances were validated using various statistical criteria, such as the root-mean-square error (whose value is less than 10% from the observed mean), the coefficient of efficiency (whose value is more than 0.90), and various other statistical parameters. This paper suggests applicability of these models in real-time scenarios while following the continuity norm. HIGHLIGHTS
Applicability of Continuity equation while forecasting using ANN.;
Use of storage variable in river flow prediction.;
Routing type dynamic ANN models implication.;
Use of MIMO (multiple input and multiple output) and MISO (multiple input and single output) model forms for forecasting approach.;
Model is applicable and useful in real time flood scenarios.; |
| first_indexed | 2024-12-19T03:53:50Z |
| format | Article |
| id | doaj.art-9371ce5520e54030b54765d432346ea0 |
| institution | Directory Open Access Journal |
| issn | 1751-231X |
| language | English |
| last_indexed | 2024-12-19T03:53:50Z |
| publishDate | 2021-10-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Practice and Technology |
| spelling | doaj.art-9371ce5520e54030b54765d432346ea02022-12-21T20:36:51ZengIWA PublishingWater Practice and Technology1751-231X2021-10-011641194120510.2166/wpt.2021.068068Flood forecasting and flood flow modeling in a river system using ANNS. Agarwal0P. J. Roy1P. Choudhury2N. Debbarma3 Department of Civil Engineering, National Institute of Technology Silchar, NIT Road, Silchar, Assam 788010, India Department of Civil Engineering, National Institute of Technology Silchar, NIT Road, Silchar, Assam 788010, India Department of Civil Engineering, National Institute of Technology Silchar, NIT Road, Silchar, Assam 788010, India Department of Civil Engineering, National Institute of Technology Agartala, Barjala, Jirania, Agartala, Tripura 799046, India In terms of predicting the flow parameters of a river system, such as discharge and flow depth, the continuity equation plays a vital role. In this research, static- and routing-type dynamic artificial neural networks (ANNs) were incorporated in the multiple sections of a river flow on the basis of a storage parameter. Storage characteristics were presented implicitly and explicitly for various sections in a river system satisfying the continuity norm and mass balance flow. Furthermore, the multiple-input multiple-output (MIMO) model form having two base architectures, namely, MIMO-1 and MIMO-2, was accounted for learning fractional storage and actual storage variations and characteristics in a given model form. The model architecture was also obtained by using a trial-and-error approach, while the network architecture was acquired by employing gamma memory along with use of the multi-layer perceptron model form. Moreover, this paper discusses the comparisons and differences between both models. The model performances were validated using various statistical criteria, such as the root-mean-square error (whose value is less than 10% from the observed mean), the coefficient of efficiency (whose value is more than 0.90), and various other statistical parameters. This paper suggests applicability of these models in real-time scenarios while following the continuity norm. HIGHLIGHTS Applicability of Continuity equation while forecasting using ANN.; Use of storage variable in river flow prediction.; Routing type dynamic ANN models implication.; Use of MIMO (multiple input and multiple output) and MISO (multiple input and single output) model forms for forecasting approach.; Model is applicable and useful in real time flood scenarios.;http://wpt.iwaponline.com/content/16/4/1194continuity equationgamma memorymultiple inputmultiple outputrmsestorage |
| spellingShingle | S. Agarwal P. J. Roy P. Choudhury N. Debbarma Flood forecasting and flood flow modeling in a river system using ANN Water Practice and Technology continuity equation gamma memory multiple input multiple output rmse storage |
| title | Flood forecasting and flood flow modeling in a river system using ANN |
| title_full | Flood forecasting and flood flow modeling in a river system using ANN |
| title_fullStr | Flood forecasting and flood flow modeling in a river system using ANN |
| title_full_unstemmed | Flood forecasting and flood flow modeling in a river system using ANN |
| title_short | Flood forecasting and flood flow modeling in a river system using ANN |
| title_sort | flood forecasting and flood flow modeling in a river system using ann |
| topic | continuity equation gamma memory multiple input multiple output rmse storage |
| url | http://wpt.iwaponline.com/content/16/4/1194 |
| work_keys_str_mv | AT sagarwal floodforecastingandfloodflowmodelinginariversystemusingann AT pjroy floodforecastingandfloodflowmodelinginariversystemusingann AT pchoudhury floodforecastingandfloodflowmodelinginariversystemusingann AT ndebbarma floodforecastingandfloodflowmodelinginariversystemusingann |