Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.1007/s12517-022-09531-3 |
| Random forest and naive Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt | |
| Abu El-Magd, Sherif Ahmed | |
| 通讯作者 | Abu El-Magd, SA (corresponding author)，Suez Univ, Fac Sci, Geol Dept, Suez 43518, Egypt. |
| 来源期刊 | ARABIAN JOURNAL OF GEOSCIENCES
 |
| ISSN | 1866-7511 |
| EISSN | 1866-7538 |
| 出版年 | 2022 |
| 卷号 | 15期号:3 |
| 英文摘要 | Machine learning (ML) algorithms are reliable approaches to address incomplete datasets in existing studies. In this study, the ML algorithms naive Bayes (NB) and random forest (RF) were used to generate a flash flood forecasting model in Wadi El-Dib on the Gulf of Suez Coast at the Eastern Desert of Egypt. A total of 1117 point locations of field data and remote sensing data were mapped to prepare a flood inventory map. The relationships between the flood controlling factors were assessed and evaluated based on the implemented approaches. Slope degree, distance from streams, topographic wetness index, and elevation are the most important controlling factors out of the input seven themes. The proposed prediction model for the identification of flooding and nonflooding areas achieved reliable accuracy for the implemented approaches according to the area under the curve. Results demonstrate that the flash flood model was able to simulate flooding and nonflooding areas with improved accuracy. The NB and RF models achieved predictive performance with an accuracy of 85% to 88%, respectively. The susceptibility map was classified into flooding zones and nonflooding zones, which might be helpful for urbanization planning and management. Our findings indicate that about 83% of the field data were plotted into susceptible flooding zones and that eastern areas with gentle slopes have high potential for flash floods. ML can extract and generate useful information, and related models could be applied in such studies and similar areas. |
| 英文关键词 | Machine learning Flash flood model Naive Bayes Random forest Wadi El-Dib Egypt |
| 类型 | Article |
| 语种 | 英语 |
| 收录类别 | SCI-E |
| WOS记录号 | WOS:000746100000011 |
| WOS关键词 | SUPPORT VECTOR MACHINE ; MODELS |
| WOS类目 | Geosciences, Multidisciplinary |
| WOS研究方向 | Geology |
| 资源类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/376473 |
| 作者单位 | [Abu El-Magd, Sherif Ahmed] Suez Univ, Fac Sci, Geol Dept, Suez 43518, Egypt |
| 推荐引用方式 GB/T 7714 | Abu El-Magd, Sherif Ahmed. Random forest and naive Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt[J],2022,15(3). |
| APA | Abu El-Magd, Sherif Ahmed.(2022).Random forest and naive Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt.ARABIAN JOURNAL OF GEOSCIENCES,15(3). |
| MLA | Abu El-Magd, Sherif Ahmed."Random forest and naive Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt".ARABIAN JOURNAL OF GEOSCIENCES 15.3(2022). |
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