Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.2166/wcc.2021.221 |
| Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches | |
| Samantaray, Sandeep; Ghose, Dillip Kumar | |
| Corresponding Author | Ghose, DK (corresponding author), Natl Inst Technol, Dept Civil Engn, Silchar, Assam, India. |
| Journal | JOURNAL OF WATER AND CLIMATE CHANGE
 |
| ISSN | 2040-2244 |
| EISSN | 2408-9354 |
| Year Published | 2022 |
| Abstract in English | Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. Rainfall-runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash-Sutcliffe), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93-0.95 and 0.96-0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2-3%. |
| Keyword in English | PSR-SVM-FFA rainfall simulator runoff SVM SVM-FFA |
| Subtype | Article |
| Language | 英语 |
| OA Type | gold |
| Indexed By | SCI-E |
| WOS ID | WOS:000711939000001 |
| WOS Keyword | PHASE-SPACE RECONSTRUCTION ; SUPPORT VECTOR MACHINES ; FIREFLY ALGORITHM ; NEURAL-NETWORKS ; STREAMFLOW ; MODELS ; WIND |
| WOS Subject | Water Resources |
| WOS Research Area | Water Resources |
| Document Type | 期刊论文 |
| Identifier | http://119.78.100.177/qdio/handle/2XILL650/367595 |
| Affiliation | [Samantaray, Sandeep; Ghose, Dillip Kumar] Natl Inst Technol, Dept Civil Engn, Silchar, Assam, India |
| Recommended Citation GB/T 7714 | Samantaray, Sandeep,Ghose, Dillip Kumar. Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches[J],2022. |
| APA | Samantaray, Sandeep,&Ghose, Dillip Kumar.(2022).Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches.JOURNAL OF WATER AND CLIMATE CHANGE. |
| MLA | Samantaray, Sandeep,et al."Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches".JOURNAL OF WATER AND CLIMATE CHANGE (2022). |
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