An integrated approach of GIS and hybrid intelligence techniques applied for flood risk modeling

Flood is a typical natural disaster, which results in huge economic damage and human loss; therefore, accurately predicting flood-prone areas is important for preventing and mitigating the impacts of floods. The main objective of this study is to present new ensemble models, which are based on Index of Entropy (IOE), Fuzzy Membership Value (FMV), Frequency Ratio (FR), and Information Value (IV) for assessing flood susceptibility. For this purpose, data from a total of 78 flood events were taken into account as basic data for the training model and validation of results. Location and spatial characteristics of these historical flood events were used to identify the relevant criteria for flood susceptibility modeling (FSM) and in acquiring the contribution of each criterion in susceptibility of the region toward flood. The FMV-IOE, FR-IOE, and IV-IOE models were used to distinguish between presence and absence of flood and its mapping. These models were also employed to perform feature selection in order to reveal the variables, which may contribute for flood occurrence extensively. Finally, for the validation of results, the Area Under the Receiver Operating Characteristic (AUROC) was computed for each flood susceptibility map. The validation of results indicated that AUROC for three mentioned models varies from 0.963 to 0.969 (AUROC FMV-IOE = 96.9%, AUROC FR-IOE = 96.8%, and AUROC IV-IOE = 96.3%). Results acknowledged that the main drivers of flood occurrence were soil, land use, and SPI factors. The results of this research are of great importance for the task of mitigation and in reduction of the impacts of future losses, including land-use planning for the region under study. Current research makes a significant contribution to developing GISciences by means of proposing a new approach for GIS-based decision makings systems. From the methodological perspective, the results of this research are of great importance in analyzing the capability of hybrid intelligence techniques and their integration with fuzzy and GIS decision-making systems.HighlightsWe have developed three novel hybrid methods for flood modeling.Soil, land use, and distance to river were the effective factors.FMV-IOE model shows a better result in flood prediction.Using the results of this study, prevention measures can be taken in advance of a flood.