Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.2166/nh.2020.141 |
| Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds | |
| Dehghanian, Naser; Saeid Mousavi Nadoushani, S.; Saghafian, Bahram; Damavandi, Morteza Rayati | |
| Corresponding Author | Nadoushani, SSM |
| Journal | HYDROLOGY RESEARCH
 |
| ISSN | 0029-1277 |
| EISSN | 2224-7955 |
| Year Published | 2020 |
| Volume | 51Issue:3Pages:423-442 |
| Abstract in English | An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds. |
| Keyword in English | artificial neural network (ANN) flood source areas (FSAs) hydrological homogenous regions (HHRs) ModClark semi-arid ungauged watersheds unit flood response (UFR) |
| Subtype | Article |
| Language | 英语 |
| OA Type | gold |
| Indexed By | SCI-E |
| WOS ID | WOS:000540590800004 |
| WOS Keyword | ARTIFICIAL NEURAL-NETWORKS ; FREQUENCY-ANALYSIS ; CLUSTER VALIDITY ; RUNOFF ; SEDIMENT ; STREAMFLOW ; ALGORITHM ; MAP |
| WOS Subject | Water Resources |
| WOS Research Area | Water Resources |
| Document Type | 期刊论文 |
| Identifier | http://119.78.100.177/qdio/handle/2XILL650/324766 |
| Affiliation | [Dehghanian, Naser; Saeid Mousavi Nadoushani, S.] Shahid Beheshti Univ, Fac Civil Water & Environm Engn, Dept Water Resources Management, Tehran, Iran; [Saghafian, Bahram] Islamic Azad Univ, Dept Civil Engn, Sci & Res Branch, Tehran, Iran; [Damavandi, Morteza Rayati] Islamic Azad Univ, Dept Tech & Engn, Qaemshahr, Iran |
| Recommended Citation GB/T 7714 | Dehghanian, Naser,Saeid Mousavi Nadoushani, S.,Saghafian, Bahram,et al. Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds[J],2020,51(3):423-442. |
| APA | Dehghanian, Naser,Saeid Mousavi Nadoushani, S.,Saghafian, Bahram,&Damavandi, Morteza Rayati.(2020).Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds.HYDROLOGY RESEARCH,51(3),423-442. |
| MLA | Dehghanian, Naser,et al."Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds".HYDROLOGY RESEARCH 51.3(2020):423-442. |
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