Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood locations and development of accurate flash flood susceptibility maps are important for proper flash flood management of a region. With this objective, we proposed and compared several novel hybrid computational approaches of machine learning methods for flash flood susceptibility mapping, namely AdaBoostM1 based Credal Decision Tree (ABM-CDT); Bagging based Credal Decision Tree (Bag-CDT); Dagging based Credal Decision Tree (Dag-CDT); MultiBoostAB based Credal Decision Tree (MBAB-CDT), and single Credal Decision Tree (CDT). These models were applied at a catchment of Markazi state in Iran. About 320 past flash flood events and nine flash flood influencing factors, namely distance from rivers, aspect, elevation, slope, rainfall, distance from faults, soil, land use, and lithology were considered and analyzed for the development of flash flood susceptibility maps. Correlation based feature selection method was used to validate and select the important factors for modeling of flash floods. Based on this feature selection analysis, only eight factors (distance from rivers, aspect, elevation, slope, rainfall, soil, land use, and lithology) were selected for the modeling, where distance to rivers is the most important factor for modeling of flash flood in this area. Performance of the models was validated and compared by using several robust metrics such as statistical measures and Area Under the Receiver Operating Characteristic (AUC) curve. The results of this study suggested that ABM-CDT (AUC = 0.957) has the best predictive capability in terms of accuracy, followed by Dag-CDT (AUC = 0.947), MBAB-CDT (AUC = 0.933), Bag-CDT (AUC = 0.932), and CDT (0.900), respectively. The proposed methods presented in this study would help in the development of accurate flash flood susceptible maps of watershed areas not only in Iran but also other parts of the world.

In the present days, remote sensing and geographic information system (GIS) techniques are comprehensive tools for the assessment of water resource, its management and conservation. In this study, remote sensing and GIS techniques are taken into consideration for zonation of different groundwater prospects of Pravara basin. Several contributing factors in which groundwater potential of an area entirely or partially depends such as lithology, geomorphology, slope, soil, lineament density, drainage density, land use and rainfall are assessed individually as well as together for making the different groundwater prospect maps through influencing factor (IF) and frequency ratio (FR) techniques. In ArcGIS software, all these thematic layers are prepared using different satellite imageries and conventional data sets, obtained from different sources. All these layers are transformed into high-resolution raster format and meshed together in GIS environment using IF and FR methods. Groundwater prospect maps are constructed as a result of overlay analysis. Based on the constructed map of groundwater prospect zones, as a result of this study, the study area is divided into five categories of different groundwater potential zones: very high, high, moderate, low and very low. Validation of the resultant maps has shown that frequency ratio technique is having higher accuracy (AUC = 73%) compared to the influencing factors (AUC = 69%). The present study of groundwater zonation provides a simple and less time-consuming technique; also, the results can directly be used for planning and sustainable management in Pravara basin.