Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions
Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological v...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
MDPI AG
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833352483610558464 |
|---|---|
| author | Ehteram M. Ghotbi S. Kisi O. Ahmed A.N. Hayder G. Fai C.M. Krishnan M. Afan H.A. EL-Shafie A. |
| author2 | 57113510800 |
| author_facet | 57113510800 Ehteram M. Ghotbi S. Kisi O. Ahmed A.N. Hayder G. Fai C.M. Krishnan M. Afan H.A. EL-Shafie A. |
| author_sort | Ehteram M. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash-Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS-BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0.75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS-BA had more reliable performance compared to other models. Thus, the ANFIS-BA model has high potential for predicting SSL. � 2019 by the authors. |
| format | Article |
| id | my.uniten.dspace-24431 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | my.uniten.dspace-244312023-05-29T15:23:28Z Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions Ehteram M. Ghotbi S. Kisi O. Ahmed A.N. Hayder G. Fai C.M. Krishnan M. Afan H.A. EL-Shafie A. 57113510800 57210611785 6507051085 57214837520 56239664100 57214146115 57211283663 56436626600 16068189400 Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash-Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS-BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0.75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS-BA had more reliable performance compared to other models. Thus, the ANFIS-BA model has high potential for predicting SSL. � 2019 by the authors. Final 2023-05-29T07:23:28Z 2023-05-29T07:23:28Z 2019 Article 10.3390/app9194149 2-s2.0-85073276948 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073276948&doi=10.3390%2fapp9194149&partnerID=40&md5=b51fa04818b4899a77446ecb1b67a4db https://irepository.uniten.edu.my/handle/123456789/24431 9 19 4149 All Open Access, Gold MDPI AG Scopus |
| spellingShingle | Ehteram M. Ghotbi S. Kisi O. Ahmed A.N. Hayder G. Fai C.M. Krishnan M. Afan H.A. EL-Shafie A. Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions |
| title | Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions |
| title_full | Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions |
| title_fullStr | Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions |
| title_full_unstemmed | Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions |
| title_short | Investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions |
| title_sort | investigation on the potential to integrate different artificial intelligence models with metaheuristic algorithms for improving river suspended sediment predictions |
| url_provider | http://dspace.uniten.edu.my/ |