support vector machine algorithm

One of the most important issues in the field of financial management is that investors can distinguish favorable investment opportunities from unfavorable ones. One way to help investors is to anticipate the financial recovery (exit from helplessness) of companies with financial distress. Therefore, this study intends to provide a model for predicting financial recovery using the support vector machine algorithm for companies listed on the Tehran Stock Exchange. To achieve this goal, 54 financial variables were determined using the Lars feature selection algorithm and to test the accuracy of the results of the proposed model, the support vector learning algorithm was used. For this purpose, in the period of 2001-2018, the information of 167 helpless companies that were out of financial helplessness and revived was extracted. The research findings show that the research model accurately predicts the recovery time of the financially helpless company from financial distress with 74% accuracy.

One of the slopping processes which created much damage in many locations of Iran and the world is Landslide phenomenon. Identification of susceptible areas to landslide occurrence is one of the basic measures for reduction of the possible risk and management. The main goal of this research is to evaluate Landslide Susceptibility assessment using Support vector machine algorithm. At first, a landslide inventory map with 60 landslide locations for the study area was drawn from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide model and for the model validation.Training and testing of RBF Function the SVM algorithm was evaluated over an assembly of spatial attributes, which included slope angle, elevation, aspect, solar radiation, profile curvature, plan curvature, lithology, land use, distance to fault, distance to road and distance to river with respect of the referent model. Finally the study area was classified into five sensitivity classes’ very high, high, moderate, low and very low. Then Performance of the method has been evaluated using the ROC curve. The results show that area under the ROC curve (AUC) using training dataset is (0/950) and using validation dataset is (0/931). Therefore, analysis and comparison of the results show that RBF Function SVM model performed well for landslide susceptibility assessment in the study area and the results from this the results from this study can be useful for land use planning, mitigate landslide hazards and decision making in landslide prone areas.