Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.

Earthquake hazards cause changes in landforms, economic losses, and human casualties. Seismic Vulnerability Mapping (SVM) is key information to prevent and predict the damage of earthquakes. The purpose of this study is to train and compare the results of the Classification Tree Analysis (CTA) learner model with three Gini, Entropy, Ratio split algorithms, and Fuzzy ARTMAP (FAM) model by the development of hybrid models for SVM. The Seismic Vulnerability Conditioning Factors (SVCFs) such as environmental, physical, and social were selected using experts' opinions and experience. Thirteen factors were edited and prepared as the seismic vulnerability conditioning factors (SVCFs) used in this study. In order to seismic vulnerability mapping and models training, a database of training sites was created by the Multi-Criteria Decision Analysis-Multi-Criteria Evaluation (MCDA-MCE) hybrid process. Then, 70% of the points were used for training and 30% were used to validate the models' results based on the holdout method. Moreover, Relative Operating Characteristics (ROC), Seismic Relative Index (SRI), and Frequency Ratio (FR) were used to validate the results. The Area under the curve (AUC) for the algorithms Gini, Entropy, Ratio, and FAM model are 0.895, 0.890, 0.876, and 0.783, respectively. The results of the three validation methods show the highest performance for the Gini splitting algorithm. Accordingly, the percentage of social and physical vulnerability of Sanandaj city was determined based on the MCE-Gini optimal model: 27% of the area and 62% of the population of Sanandaj are under high vulnerability to earthquakes. So that, various factors such as worn urban texture, high population density and environmental factors were among the most important factors affecting seismic vulnerability.

The frequency and intensity of dust storms in Iran has increased significantly in recent years. This study identifies the sources of dust using hybrid algorithms – probability density-index of entropy (PD-IOE), probability density-radial basic function neural network (PD-RBFNN), probability density-self-organizing map (PD-SOM), and probability density-fuzzy ARTMAP (PD-FAM). Hybrid models employed several effective environmental factors: land cover, slope, soil, land use, wind speed, geology, temperature, and precipitation. A random selection of 70% of the data points were used for training the spatial models and the remainder (30%) were used to test the effectiveness of the models to determine the best algorithm. The results reveal that the PD-FAM algorithm produced the most accurate predictions of dust sources. Geology, slope, and soil were the factors that were most effective predictors of dust generation in eastern Iran. Comprehensive management is needed to manage dust production in Iran and these findings may ease identification of locations most likely to produce dust.