Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model based on the Cascade Forest Model (CFM) was developed for FSM. Satellite imagery, historical reports, and field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. The performance of the proposed CFM was evaluated over two study areas, and the results were compared with those of other six machine learning methods, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Deep Neural Network (DNN), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The result showed CFM produced the highest accuracy compared to other models over both study areas. The Overall Accuracy (AC), Kappa Coefficient (KC), and Area Under the Receiver Operating Characteristic Curve (AUC) of the proposed model were more than 95%, 0.8, 0.95, respectively. Most of these models recognized the southwestern part of the Karun basin, northern and northwestern regions of the Gorganrud basin as susceptible areas.

Mangroves are woody plant communities that appear in tropical and subtropical regions, mainly in intertidal zones along the coastlines. Despite their considerable benefits to humans and the surrounding environment, their existence is threatened by anthropogenic activities and natural drivers. Accordingly, it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations. These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings. Therefore, identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate. This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan, Iran. To this end, we applied a hybrid Fuzzy-DEMATEL-ANP (FDANP) model as a Multi-Criteria Decision Making (MCDM) approach to determine the relative importance of different criteria, combined with geospatial and remote sensing data. In this regard, ten relevant sources of environmental criteria, including meteorological, topographical, and geomorphological, were used in the modeling. The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification. Based on the final results, 6.10% and 20.80% of the study area were classified as very-high suitable and very-low suitable areas. The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning. Moreover, the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.