Assessment and Zoning of Flood Risk in Golestan National Park

Identifying the flood susceptible areas is a vital and substantial element of disaster management to control and mitigate injuries of the natural hazards. The purpose of this study was to identify the important variables in creating flood areas and to present the potential hazard of flood in Golestan National Park (GNP) using machine learning techniques including random forest (RF), boosted regression tree (BRT) and maximum entropy (ME) models. In order to achieve these purposes, firstly, factors were determined by reviewing the relevant sources, and the databases were created by sorting out these factors. Finally, Flood risk modeling was done using machine learning techniques and the accuracy assessment were determined using the ROC method and real data recorded in nature. The results of the models showed the importance of elevation, distance from the river and transit road, moisture and maximum temperature variables in the event of flood hazard. So that the results of the BRT showed role elevation variable to 38.9%, mean temperature 19/2 %, Rainfall 13/6 % and distance from the rivers 13% and the results of ME showed role elevation, mean temperature and distance of road variables respectively 35.7, 22/4 and 13.8%. The results of the accuracy assessment models using 30% of the data that were not included in the modeling the ROC value showed BRT and RF model with 0.99 values, and the proper accuracy ME was with value of 0.89. Therefore, the maps obtained can be used to manage the flood crisis in the area