Abstract
Recently, floods are occurring more frequently every year around the world due to increased anthropogenic activities and climate change. There is a need to develop accurate models for flood susceptibility prediction and mapping, which can be helpful in developing more efficient flood management plans. In this study, the Partial Decision Tree (PART) classifier and the AdaBoost, Bagging, Dagging, and Random Subspace ensembles learning techniques were combined to develop novel GIS-based ensemble computational models (ABPART, BPART, DPART and RSSPART) for flood susceptibility mapping in the Quang Binh Province, Vietnam. In total, 351 flood locations were used in the model study. This data was divided into a 70:30 ratio for model training (70% ≅ 255 locations) and (30% ≅ 96 locations) for model validation. Ten flood influencing factors, namely elevation, slope, curvature, flow direction, flow accumulation, river density, distance from river, rainfall, land-use, and geology, were used for the development of models. The OneR feature selection method was used to select and prioritize important factors for the spatial modeling. The results revealed that land-use, geology, and slope are the most important conditioning factors in the occurrence of floods in the study area. Standard statistical methods, including the ROC curve (AUC), were used for the performance evaluation of models. Results indicated that the performance of all models was good (AUC > 0.9) and RSSPART (AUC = 0.959) outperformed the others. Thus, the RSSPART model can be used for accurately predicting and mapping flood susceptibility.
Biography
Mahdis Amiri, She is 27 years old, also her date of birth is May 16, 1994, Her Place of Birth is Shiraz, currently she is PhD student at Gorgan University of Agricultural Sciences and Natural Resources, Department of Watershed & Arid Zone Management, Gorgan, Iran. She in the sixth semester. She hold a Bachelor and Master of Science degrees from the University of Shiraz. Her majors are Natural resource, Her master’s thesis title is Spatial Modeling of Gully Erosion and. She is currently working on Spatial modeling and multi-hazards. Also, She is very interested in Spatial Modelling of Gully erosion, Landslide, Fire Forest and Flood susceptibility also groundwater potential.

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