Spatial modeling of flood probability using geo-environmental variables and machine learning models,case study: Tajan watershed,Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.     

Design and fabrication of robotic gripper for grasping in minimizing contact force

This paper presents a new method to improve the kinematics of robot gripper for grasping in unstructured environments, such as space operations. The robot gripper is inspired from the human hand and kept the hand design close to the structure of human fingers to provide successful grasping capabilities. The main goal is to improve kinematic structure of gripper to increase the grasping capability of large objects, decrease the contact forces and makes a successful grasp of various objects in unstructured environments. This research will describe the development of a self-adaptive and reconfigurable robotic hand for space operations through mechanical compliance which is versatile, robust and easy to control. Our model contains two fingers, two-link and three-link, with combining a kinematic model of thumb index. Moreover, some experimental tests are performed to examine the effectiveness of the hand-made in real, unstructured tasks. The results represent that the successful grasp range is improved about 30% and the contact forces is reduced approximately 10% for a wide range of target object size. According to the obtained results, the proposed approach provides an accommodative kinematic model which makes the better grasping capability by fingers geometries for a robot gripper.