Origin of aragonite in a continental saline seep,north Queensland

Aragonite, low‐magnesian calcite, gypsum and halite were identified by X‐ray diffraction and electron microbeam techniques in mineral precipitates near a salt seep 50 km southwest of Charters Towers in north Queensland. The chemistry of water from the creek and from the groundwater at the salt seep shows that Mg:Ca ratios are greater than or equal to 1.5 throughout the year. The formation of halite and gypsum is due to evaporative concentration of the water at the seep and that of the carbonates, in particular aragonite, is probably due to a combination of evaporation and photosynthetic activity by diatoms.     

Hyper-parameter optimization of gradient boosters for flood susceptibility analysis

Properly choosing hyper-parameters improves machine learning models' performance and reduces training time and resource requirements. In this study, we investigated the uses of the Bayesian optimization algorithm for hyper-parameter searches of two classifiers, namely LightGBM and XGBoost. The models were verified with a dataset from Vietnam, including historical flood locations from satellite images and survey data, and 11 features from three groups, namely physical, hydrological, and human-related factors. The models' performance was evaluated using Area under Receiver Operating Characteristic curves (AUC-ROC). Several strategies were applied to avoid over-fitting, and the results show that two tuned Gradient boosters reached considerably high AUC values (approximately 0.98) compared with the previous study with a similar dataset. The model interpretation was also implemented using the Shapley (SHAP) values to understand better how models work and the interactions between features. The search for optimal hyper-parameters is worth investigating in the future, particularly when there is growing work for novel optimization algorithms. The verification of such an approach is scientifically sound, and the models can be used as an alternative solution for natural hazard analysis in countries prone to hazards.     

Water losses in arid and semi-arid zone: Evaporation, evapotranspiration and seepage

The primary purpose of this study was to assess water losses by evapotranspiration, evaporation and seepage in arid zone.Normally, evaporation and seepage are the main causes of water losses.For modeling water losses,a combination of Genetic Programming(GP),Penman-Monteith(PM) and Penman combination model for measurement of evapotranspiration,evaporation and seepage has been developed.The results were found to be varying depending on how the evaporation and seepage phenomena are modeled.These results show that that there is an improvement in reducing evapotranspiration,evaporation and seepage losses in arid and semi-arid region.     

A novel fuzzy <Emphasis Type="Italic">K</Emphasis>-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC?=?0.944) and validation dataset (AUC?=?0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.     

A new model for flow in shale-gas reservoirs including natural and hydraulic fractures

In this work, we construct a new coupled Multiscale/Discrete Fracture Model for compressible flow in a multiporosity shale gas reservoir containing networks of natural and hydraulic fractures. The geological formation is characterized by four distinct length scales and levels of porosity. The window of observation of the finest (nanoscale) portraits the nanopores within organic matter containing adsorbed gas. At the microscale, the medium is formed by two solid phases: organic, composed by kerogen aggregates, and inorganic (clay, quartz, calcite). Such phases are separated by the network of partially-saturated interparticle pores where microscopic free gas flow influenced by Knudsen effects along with gas diffusion in the immobile water phase occur simultaneously. The upscaling of the local flow to the mesoscale gives rise to a nonlinear homogenized pressure equation in the shale matrix which lies adjacent to the system of natural fractures. Homogenization of the coupled matrix/preexisting fractures to the macroscale leads to a microstructural model of dual porosity type. Such homogenized model is subsequently coupled with the hydrodynamics in the network of induced fractures which, in the context of the discrete fracture modeling, are treated as (n ? 1), (n = 2, 3) lower dimensional objects. In order to handle numerically the nonlinear interaction between the different flow equations, we adopt a superposition argument, firstly proposed by Arbogast (1996), in each iteration of a fixed-point algorithm. The resultant governing equations are discretized by the finite element method and numerical simulations of gas production in stratified arrangements of the fracture networks are presented to illustrate the potential of the multiscale approach.     

A stochastic model of soil erosion

The erosion model computes the rill and inter-rill flow over a surface with random roughness, and the erosion caused by this flow. The measured roughness of a surface is analysed and used to generate random surfaces for the simulation process. Computations are carried out over a number of time intervals; the steady state condition is assumed for each interval. Changes in the surface geometry due to erosion during an interval are used to revise the surface for the subsequent interval. The model includes simplified mechanisms to simulate ponding, deposition and failure of side slopes of rills.     

Bagging based Support Vector Machines for spatial prediction of landslides

A hybrid Bagging based Support Vector Machines (BSVM) method, which is a combination of Bagging Ensemble and Support Vector Machine (SVM) classifier, was proposed for the spatial prediction of landslides at the district of Mu Cang Chai, Viet Nam. In the present study, 248 past landslides and fifteen geo-environmental factors (curvature, elevation, distance to rivers, slope, aspect, river density, plan curvature, distance to faults, profile curvature, fault density, lithology, distance to roads, rainfall, land use, and road density) were considered for the model construction. Different evaluation criteria were applied to validate the proposed hybrid model such as statistical index-based methods and area under the receiver operating characteristic curve (AUC). The single SVM and the Naïve Bayes Trees (NBT) models were selected for comparison. Based on the AUC values, the proposed hybrid model BSVM (0.812) outperformed the SVM (0.804) and NBT (0.8) models. Thus, the BSVM is a promising and better method for landslide prediction.     

Topography and landslides in weathered granitic rock areas—Hai Van landslide in central Vietnam

Many landslides occur every year during heavy rains at the Hai Van Pass and surrounding area in central Vietnam, where granitic rocks are distributed. As is common in granite areas, these landslides often occur as small-scale flow-type and slump-type landslides. However, several horseshoe-shaped loose slopes of widths and lengths of 500 to 800 m, which incorporate these landslides, are observed on slopes across the area. These topographies resemble those formed by past and present large-scale landslides. The presence of such a topography and the repeated occurrences of landslides within this topography are rare in granite areas, where shallow flow-type landslides are generally frequent. To understand the mechanism causing the landslides in the Hai Van region, and as a support for future risk assessment, the factors and processes leading to the formation of such a topography and their relationship with these landslides must be identified and assessed. This study investigated the history of past landslide movement in the Hai Van Pass and surrounding area through observations of drill cores and outcrops, and analysis of the direction of remanent magnetism in the granitic rocks. Mineral compositions, cracks, degrees of weathering, and topographic shapes of the granitic rocks and their relationship to the landslides occurring today were also investigated. The results of the study reveal no variation in the direction of remanent magnetism in the granitic rocks in the region that would indicate disturbance of the ground due to a past large-scale landslide. No evidence of such an event could also be found both in the drill cores and the rock outcrops. Further, results of the analysis of cracks and weathering pattern confirm that the topography of the region is affected by the weathering of the granitic rocks that progresses in concentric circles of various sizes. Thus, it can be concluded that these topographies were not formed by a singular large-scale landslide of the past, but rather by a composite of relatively shallow landslides occurring on the slope of dome structures unique to granite areas, which are formed by differential weathering and denudation regulated by cracks.