Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning

Line-Scanning Particle Image Velocimetry: An Optical Approach for Quantifying a Wide Range of Blood Flow Speeds in Live Animals

The ability to measure blood velocities is critical for studying vascular development, physiology, and pathology. A key challenge is to quantify a wide range of blood velocities in vessels deep within living specimens with concurrent diffraction-limited resolution imaging of vascular cells. Two-photon laser scanning microscopy (TPLSM) has shown tremendous promise in analyzing blood velocities hundreds of micrometers deep in animals with cellular resolution. However, current analysis of TPLSM-based data is limited to the lower range of blood velocities and is not adequate to study faster velocities in many normal or disease conditions.We developed line-scanning particle image velocimetry (LS-PIV), which used TPLSM data to quantify peak blood velocities up to 84 mm/s in live mice harboring brain arteriovenous malformation, a disease characterized by high flow. With this method, we were able to accurately detect the elevated blood velocities and exaggerated pulsatility along the abnormal vascular network in these animals. LS-PIV robustly analyzed noisy data from vessels as deep as 850 µm below the brain surface. In addition to analyzing in vivo data, we validated the accuracy of LS-PIV up to 800 mm/s using simulations with known velocity and noise parameters.To our knowledge, these blood velocity measurements are the fastest recorded with TPLSM. Partnered with transgenic mice carrying cell-specific fluorescent reporters, LS-PIV will also enable the direct in vivo correlation of cellular, biochemical, and hemodynamic parameters in high flow vascular development and diseases such as atherogenesis, arteriogenesis, and vascular anomalies