气候变化领域集成服务门户(CCPortal): Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, Iran

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DOI	10.1007/s11069-021-04860-0
	Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, Iran
	Rajabi A.; Shabanlou S.; Yosefvand F.; Kiani A.
发表日期	2021
ISSN	0921030X
英文摘要	Flood has always been a destructive natural hazard during the recent years. Hence, this research aimed to predict the potentiality and probability of flood phenomenon by using the two well-known models, i.e., the MARS algorithm (multivariate adaptive regression splines) and MaxEnt (maximum entropy) method in the Saliantapeh catchment, Golestan province, Iran, covering 4515.47 km2. First, documentary sources report and field surveys were used to provide a flood database map. Then, to prepare the flood spatial potentiality map (FSPM), we select sixteen influential variables as predictors. Furthermore, the relative contributions of predicting factors are estimated using the MaxEnt method. For the analysis of data sensitivity and the uncertainty of the proposed models, different scenarios including the sample size (50%/50%, 80%/20%, and 70%/30%, respectively, for training and validation), and the number of replications (5, 10, and 20) were used. Along with the area under the ROC curve (AUC), the highest accuracy for both models corresponds to the first scenario of sample size (80/20%). Contrarywise, it can be concluded that for this scenario, the MARS technique indicated higher predictive skill (AUC = 98.51%). Regarding the second scenario, which is corresponding to the replicate, the MARS model with 20 replications still has the highest accuracy (94.70%) compared to the other scenarios and the MaxEnt model. The findings of robustness demonstrated that the scenarios with the greatest AUC value have the highest robustness. This work demonstrates that the utilization of the best accurate model with high certainty along with FSPM may be useful to identify and manage the areas that are most susceptible to flood. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Data sensitivity Maximum entropy Models uncertainty ROC curve Spatial potentiality map
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206518
作者单位	Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
推荐引用方式 GB/T 7714	Rajabi A.,Shabanlou S.,Yosefvand F.,et al. Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, Iran[J],2021.
APA	Rajabi A.,Shabanlou S.,Yosefvand F.,&Kiani A..(2021).Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, Iran.Natural Hazards.
MLA	Rajabi A.,et al."Exploring the sample size and replications scenarios effect on spatial prediction of flood, using MARS and MaxEnt methods case study: saliantape catchment, Golestan, Iran".Natural Hazards (2021).