Conditioning factors determination for landslide susceptibility mapping using support vector machine learning

This study investigates the effectiveness of two sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into two sets G1 and G2. Two Support Vector Machine (SVM) classifiers were constructed based on each dataset (SVM-G1 and SVM-G2) in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 160 landslide inventory datasets of the study area were used where 70% was used for SVM training and 30% for testing. The intra-relationships between parameters were explored based on variance inflation factors (VIF), Pearson's correlation and Cohen's kappa analysis. Other evaluation metrics are the area under curve (AUC)

Evaluation of the effect of hydroseeded vegetation for slope reinforcement

A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the water runoff of a slope. This paper introduces vegetation establishment as a low-cost, practical measure for slope reinforcement through the ground cover and the root of the vegetation. This study reveals the level of complexity of the terrain with regards to the evaluation of high and low stability areas and has produced a landslide susceptibility map. For this purpose, 12 conditioning factors, namely slope, aspect, elevation, curvature, hill shade, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distances to roads, distance to lakes, distance to trees, and build-up, were used through the analytic hierarchy process (AHP) model to produce landslide susceptibility map. Receiver operating characteristics (ROC) was used for validation of the results. The area under the curve (AUC) values obtained from the ROC method for the AHP model was 0.865. Four seed samples, namely ryegrass, rye corn, signal grass, and couch, were hydroseeded to determine the vegetation root and ground cover’s effectiveness on stabilization and reinforcement on a high-risk susceptible 65° slope between August and December 2020. The observed monthly vegetation root of couch grass gave the most acceptable result. With a spreading and creeping vegetation ground cover characteristic, ryegrass showed the most acceptable monthly result for vegetation ground cover effectiveness. The findings suggest that the selection of couch species over other species is justified based on landslide control benefits

Landslide susceptibility mapping: machine and ensemble learning based on remote sensing big data

Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors

Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis

The current study proposes a new method for oil palm age estimation and counting. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. It was integrated with height model and multiregression methods to accurately estimate the age of trees based on their heights in five different plantation blocks. Multiregression and multi-kernel size models were examined over five different oil palm plantation blocks to achieve the most optimized model for age estimation. The sensitivity analysis was conducted on four SVM kernel types (sigmoid (SIG), linear (LN), radial basis function (RBF), and polynomial (PL)) with associated parameters (threshold values, gamma γ, and penalty factor (c)) to obtain the optimal OBIA classification approaches for each plantation block. Very high-resolution imageries of WorldView-3 (WV-3) and light detection and range (LiDAR) were used for oil palm detection and age assessment. The results of oil palm detection had an overall accuracy of 98.27%, 99.48%, 99.28%, 99.49%, and 97.49% for blocks A, B, C, D, and E, respectively. Moreover, the accuracy of age estimation analysis showed 90.1% for 3-year-old, 87.9% for 4-year-old, 88.0% for 6-year-old, 87.6% for 8-year-old, 79.1% for 9-year-old, and 76.8% for 22-year-old trees. Overall, the study revealed that remote sensing techniques can be useful to monitor and detect oil palm plantation for sustainable agricultural management

Development of UAV-based PM2.5 monitoring system

This paper proposes a UAV-based PM2.5 air quality and temperature-humidity monitoring system. The system includes an air quality detector comprising four Arduino sensor modules. Specifically, it includes a dust (DSM501A) sensor and a temperature and humidity (DHT11) sensor. The NEO-6M GPS module and DS3231 real-time module are also included for input visualization. A DIY SD card logging shield and memory module is also available for data recording purposes. The Arduino-based board houses multiple sensors and all are programmable using the Arduino integrated development environment (IDE) coding tool. Measurements conducted in a vertical flight path show promise where comparisons with ground truth references data showed good similarity. Overall, the results point to the idea that a light-weight and portable system can be used for accurate and reliable remote sensing data collection (in this case, PM2.5 concentration data and environmental data)

High frequency of methicillin-resistant Staphylococcus aureus (MRSA) with SCCmec type III and spa type t030 in Karaj’s teaching hospitals, Iran

Methicillin-resistant Staphylococcus aureus (MRSA) has been one of the most important antibiotic-resistant pathogen in many parts of the world over the past decades. This cross-sectional study was conducted to investigate MRSA isolated between July 2013 and July 2014 in Karaj, Iran. All tested isolates were collected in teaching hospitals from personnel, patients, and surfaces and each MRSA was analyzed by SCCmec and spa typing. Antibiotic susceptibility testing was accomplished by disk diffusion method. Out of 49 MRSA isolates from the Karaj’s teaching hospitals, 82%, 10%, and 6% of the isolates were SCCmec types III, II, and I, respectively. The main spa type in this study was spa t030 with frequency as high as 75.5% from intensive care unit (ICU) of the hospitals and high rate of resistance to rifampicin (53%) was found in MRSA isolates. In conclusion, high frequency of spa t030 with SCCmec type III and MRSA phenotype illustrated circulating of one of the antibiotic-resistant strains in ICU of Karaj’s teaching hospitals and emphasizes the need for ongoing molecular surveillance, antibiotic susceptibility monitoring, and infection control

Moving objects detection from UAV captured videos using trajectories of matched regional adjacency graphs

Videos captured using cameras from unmanned aerial vehicles (UAV) normally produce dynamic footage that commonly contains unstable camera motion with multiple moving objects. These objects are sometimes occluded by vegetation or even other objects, which presents a challenging environment for higher level video processing and analysis. This thesis deals with the topic of moving object detection (MOD) whose intention is to identify and detect single or multiple moving objects from video. In the past, MOD was mainly tackled using image registration, which discovers correspondences between consecutive frames using pair-wise grayscale spatial visual appearance matching under rigid and affine transformations. However, traditional image registration is unsuitable for UAV captured videos since distancebased grayscale similarity fails to cater for the dynamic spatio-temporal differences of moving objects. Registration is also ineffective when dealing with object occlusion. This thesis therefore proposes a framework to address these issues through a two-step approach involving region matching and region labeling. Specifically, the objectives of this thesis are (i) to develop an image registration technique based on multigraph matching, (ii) to detect occluded objects through exploration of candidate object correspondences in longer frame sequences, and (iii) to develop a robust graph coloring algorithm for multiple moving object detection under different transformations. In general, each frame of the footage will firstly be segmented into superpixel regions where appearance and geometrical features are calculated. Trajectory information is also considered across multiple frames taking into account many types of transformations. Specifically, each frame is modeled/represented as a regional adjacency graph (RAG). Then, instead of pair-wise spatial matching as with image registration, correspondences between video frames are discovered through multigraph matching of robust spatio-temporal features of each region. Since more than two frames are considered at one time, this step is able to discover better region correspondences as well as caters for object(s) occlusion. The second step of region labeling relies on the assumption that background and foreground moving objects exhibit different motions properties when in a sequence. Therefore, their spatial difference is expected to drastically differ over time. Banking on this, region labeling assigns the labels of either background or foreground region based on a proposed graph coloring algorithm, which considers trajectory-based features. Overall, the framework consisting of these two steps is termed as Motion Differences of Matched Region-based Features (MDMRBF). MDMRBF has been evaluated against two datasets namely the (i) Defense Advanced Research Projects Agency (DARPA) Video Verification of Identity (VIVID) dataset and (ii) two self-captured videos using a mounted camera on a UAV. Precision and recall are used as the criteria to quantitatively evaluate and validate overall MOD performance. Furthermore, both are computed with respect to the ground-truth data which are manually annotated for the video sequences. The proposed framework has also been compared with existing stateof- the-art detection algorithms. Experimental results show that MDMRBF outperforms these algorithms with precision and recall being 94% and 89%, respectively. These results can be attributed to the integration of appearance and geometrical constraints for region matching using the multigraph structure. Moreover, the consideration of longer trajectories on multiple frames and taking into account all the transformations also facilitated in resolving occlusion. With regards to time, the proposed approach could detect moving objects within one minute for a 30-second sequence, which means that it is efficient in practice. In conclusion, the multiple moving object detection technique proposed in this study is robust to unknown transformations, with significant improvements in overall precision and recall compared to existing methods. The proposed algorithm is designed in order to tackle many limitations of the existing algorithms such as handle inevitable occlusions, model different motions from multiple moving objects, and consider the spatial information

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods