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Mass displacement of materials such as landslide is considered among problematic phenomena in Baqi Basin located at southern slopes of Binaloud, Iran; since, it destroys agricultural lands and pastures and also increases deposits at the basin exit. Therefore, it is necessary to identify areas which are sensitive to landslide and estimate the significant volume. In the present study, in order to estimate the volume of landslide, information about depth and area of slides was collected; then, considering regression assumptions, a power regression model was given which was compared with 17 suggested models in various regions in different countries. The results showed that values of estimated mass obtained from the suggested model were consistent with observed data (P value = 0.000 and R = 0.692) and some of the existing relations which implies on efficiency of the suggested model. Also, relations that were created in small-area landslides were more suitable rather than the ones created in large-area landslides for using in Baqi Basin. According to the suggested relation, average depth value of landslides was estimated 3.314 meters in Baqi Basin which was close to the observed value, 4.609 m.

Spatial evaluation of flood-prone areas at the drainage basins is one of the basic strategies in the field of flood risk management. The present study aims to investigate the efficiency of the CN logistic and hydrological regression model for predicting and zoning floods. In the first stage, 13 runoff parameters, hydrologic soil groups (HSGs), slope, lithology, drainage density (DD), land curvature, elevation, distance to waterways/rivers, topographic wetness index (TWI), stream power index (SPI), rainfall, land use, and NDVI were employed. In the SCS-CN model of the drainage basin, the infiltration rate (S) and runoff amount (Q) were determined. The weights of the used layers were weighted by the AHP. Also, a flood zoning map of the drainage basin with different 5, 15, 25, and 50 year return periods was drawn by applying the weights of the layers. To ensure the accuracy of the zoning map with the logistic regression model, the ROC curve, and the area below the curve were used. The results showed that for the prediction rate, the AUC is 0.81%, indicating that the model has acceptable accuracy. The most important factors affecting flood are geological index; distance to waterways/rivers; and NDVI in the logistic regression model, and slope, DD, rainfall, and land use in the SCS-CN model respectively. 30 to 46% of the drainage basin area during 5 to 50 year periods has moderate flood potential, and 28 to 34% has high potential.

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