This study investigates the effectiveness of using groundwater inventory data for groundwater spring potential mapping in the Haraz watershed located in Norther Iran. From a total of 917 groundwater inventory dataset, six random inventory scenarios of 917, 690, 450, 230, 92, and 46 were generated. We trained two learning classifiers, namely the Support Vector Machine (SVM) and Random Forest (RF) based on each scenario to determine which one(s) would be more suitable for spring potential mapping. In each of the scenarios, 70% of the dataset was used for training whereas 30% was used for testing. The end results (classified maps) for each classifier and their respective dataset were quantitatively assessed based on the Area under Curve (AUC) metric. The prediction accuracies for the spring potential maps being produced for each scenario ranged from 0.693 to 0.736 using the SVM, and 0.608 to 0.895 for RF. Our findings indicate that 46 random points of inventory data did not produce a desirable outcome. On the contrary, more points yield better results, i.e. 450 random points produced the highest ROC when using SVM (0.736) followed by 917 and 690 random points using RF (0.895 and 0.877, respectively).
The work in this paper deals with moving object detection (MOD) for single/multiple moving objects from unmanned aerial vehicles (UAV). The proposed technique aims to overcome limitations of traditional pairwise image registrationbased MOD approaches. The first limitation relates to how potential objects are detected by discovering corresponding regions between two consecutive frames. The commonly used gray level distance-based similarity measures might not cater well for the dynamic spatio-temporal differences of the camera and moving objects. The second limitation relates to object occlusion. Traditionally, when only frame-pairs are considered, some objects might disappear between two frames. However, such objects were actually occluded and reappear in a later frame and are not detected. This work attempts to address both issues by firstly converting each frame into a graph representation with nodes being segmented superpixel regions. Through this, object detection can be treated as a multi-graph matching task. This allows correspondences to be tracked more reliably across frames, which does not necessarily have to be limited to frame pairs. Building upon this, all detected objects and candidate objects are reanalyzed where a graph-coloring algorithm performs occlusion detection by considering multiple frames. The proposed framework was evaluated against a public dataset and a self-captured dataset. Precision and recall are calculated to evaluate and validate overall MOD performance. The proposed approach is also compared with Support vector machine (SVM), linear SVM classifier, and Canny edge detector detection algorithms. Experimental results show promising results with precision and recall at 94% and 89%, respectively.
Landslide is among the most common geologic hazard around the world. They cause many formidable damages and numerous deaths annually. There are multiple causes for every landslide. Landslide causes are classified into three categories, namely, physical, natural, and human. Maps are a valuable and appropriate tool for presenting information on landslide. Landslide susceptibility maps is one of the most useful source of information for landuse planners. A landslide susceptibility map represents zones that have the potential for landslide. These zones are identified by correlation between the past distribution of landslide occurrence and conditioning factors that contribute to landslide. This study employs logistic regression (LR) and artificial neural networks (ANN) models to assess landslide susceptibility in Dodangeh Watershed, Mazandaran Province, Iran. The spatial database included landslide inventory, altitude, slope angle and aspect, plan and profile curvatures, distance from faults and from stream, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), terrain roughness index (TRI), landuse and lithology. Validation of the models using receiver operating characteristics and overall accuracy indicates that both models display satisfactory performance, and LR model exhibits the most stable and best performance. Given the outcomes of the study, the LR model, which has an AUC value of 0.872 and an overall accuracy of 82.59%, and the ANN model, which has a AUC value of 0.77 and an overall accuracy of 71% are promising techniques for landslide susceptibility mapping.