期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 701, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2019.134979
关键词
Flood susceptibility assessment; Naive Bayes tree; Alternating decision tree; Random forest
资金
- International Partnership Program of Chinese Academy of Sciences [115242KYSB20170022]
- National Natural Science Foundation of China [41807192, U1765206]
- Natural Science Basic Research Program of Shaanxi [2019JLM-7, 2019JQ-094]
- China Postdoctoral Science Foundation [2018T111084, 2017M613168]
- Shaanxi Province Postdoctoral Science Foundation [2017BSHYDZZ07]
- Iran National Science Foundation (INSF) [96004000]
Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naive Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets. (C) 2019 Elsevier B.V. All rights reserved.
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