Starch-assisted sol–gel synthesis of magnetic CuFe<Subscript>2</Subscript>O<Subscript>4</Subscript> powder as photo-Fenton catalysts in the presence of oxalic acid

Magnetic photo-Fenton catalysts based on spinel CuFe₂O₄ were successfully prepared by the starch-assisted sol–gel method. Various synthetic conditions such as annealing temperatures (700, 800 and 900 °C) and molar ratios of Cu²⁺/Fe³⁺/C₆H₁₀O₅ in the precursor solution (from 1:2:2 to 1:2:4) were, respectively, used in order to study the influences of annealing temperatures and precursor starch contents on the magnetic and catalytic properties of CuFe₂O₄ powders. The photo-Fenton catalytic activity was evaluated via the degradation of methylene blue under ultraviolet and visible irradiation with H₂C₂O₄ as a new oxidizing agent. According to the results, when the annealing temperature increased to 800 °C, the spinel CuFe₂O₄ phase amount was increased, which strongly enhances the photo-Fenton catalytic performance. However, above 800 °C, the catalytic activity was reduced, due to the increase in particle size. The starch content also affected the surface Cu²⁺ content and the particle size of catalysts. The catalyst prepared at 800 °C with the molar Cu²⁺/Fe³⁺/C₆H₁₀O₅ ratio of 1:2:3 presented the best photo-Fenton performance, owing to its highest surface Cu²⁺ content. This catalyst also exhibits ferromagnetic properties (saturation magnetization of 25.836 emu/g and coercivity of 1010.23 Oe), which allows them to be easily separated from the solution by a magnet.     

A study of the human-fatality rate in near-fault regions using the Victim Attribute Database

After the 1999 Chi-Chi earthquake, the Taiwanese government immediately issued new guidelines prohibiting the construction of structures for human occupancy within the Chelungpu fault zone. However, these guidelines were not based upon an in-depth hazard analysis of the near-fault regions. The positions of more than 80% of the 2,492 victims of the Chi-Chi earthquake were found by our research team. A Victim Attribute Database has been compiled that includes the GPS coordinates of the positioned victims as well as other attribute data associated with the victims. The human-fatality rates in the near-fault regions have been analyzed with regard to distances from the Chelungpu fault, the hanging-wall and footwall areas, as well as building type. The severity at the human-fatality rates in the near-fault regions is inversely proportional to distances from the causative fault, i.e., the closer the distance, the higher the human-fatality rate observed. The human-fatality rate for victims who lived in closer proximity to the hanging-wall areas is also significantly higher than those who lived in closer proximity to the footwall areas, especially in areas on either side of the fault and within 1,000 m of the fault surface trace. In terms of different building types, factors that include the capacity of the buildings to resistant strong shaking and the level of strong ground-motion greatly affected the human-fatality rates in the hanging-wall and footwall areas. Therefore simply prohibiting the construction of buildings within the active fault zone would be an insufficient method of reducing the number of potential victims; a nationwide effort should be undertaken to upgrade the capacity of buildings to resist strong shaking.     

The role of atmospheric circulation in spatial and temporal variations in the structure of currents in the western South China Sea

Based on numerical simulations, we calculate the integral water circulation of the South China Sea on the eastern Vietnam shelf in the Vietnam coastal current area. The main objective of simulations was to study the hydrodynamic structures of this current in the winter–summer interseasonal period. The calculations were performed for the period from April to June 1999, which had the necessary primary field data. Two types of atmospheric processes were considered: the first is characterized by a small pressure gradient over the South China Sea and the second includes tropical cyclones in the southern part of the sea. The simulation results showed that there are three hydrodynamic gyres in the study area during the given time period: two anticyclonic gyres and a cyclonic gyre that separates them, which together form a complex pattern of the Vietnam current. These gyres persist for the given types of atmospheric processes and are quasi-stationary structures. The Vietnam current carries coastal water masses from south to north within the anticyclonic gyres in summer and from north to south within the cyclonic gyres in winter.     

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes,multilayer perceptron neural networks,and functional trees methods

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.     

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.     

A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards.     

The October 13, 2020, deadly rapid landslide triggered by heavy rainfall in Phong Dien,Thua Thien Hue,Vietnam

Landslides - At about 12:00 a.m., on October 13, 2020, a rapid rotational landslide induced by rainfall swept over Ranger Station-7 in Phong Xuan commune, Phong Dien district, Thua Thien Hue...     

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

Natural Resources Research - Blasting is a useful technique for rocks fragmentation in open-pit mines, underground mines, as well as for civil engineering work. However, the negative impacts of...     

Prediction of Blast-Induced Ground Vibration in Open-Pit Mines Using a New Technique Based on Imperialist Competitive Algorithm and M5Rules

Natural Resources Research - In this paper, blast-induced ground vibration (BIGV) was considered as the primary objective, and a new artificial intelligence system was proposed to predict BIGV with...