Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province,Vietnam

Natural Hazards - Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In...     

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.     

An improved SPH method for saturated soils and its application to investigate the mechanisms of embankment failure: Case of hydrostatic pore‐water pressure

The method of smoothed particle hydrodynamics (SPH) has recently been applied to computational geomechanics and has been shown to be a powerful alternative to the standard numerical method, that is, the finite element method, for handling large deformation and post‐failure of geomaterials. However, very few studies apply the SPH method to model saturated or submerged soil problems. Our recent studies of this matter revealed that significant errors may be made if the gradient of the pore‐water pressure is handled using the standard SPH formulation. To overcome this problem and to enhance the SPH applications to computational geomechanics, this article proposes a general SPH formulation, which can be applied straightforwardly to dry and saturated soils. For simplicity, the current work assumes hydrostatic pore‐water pressure. It is shown that the proposed formulation can remove the numerical error mentioned earlier. Moreover, this formulation automatically satisfies the dynamic boundary conditions at a submerged ground surface, thereby saving computational cost. Discussions on the applications of the standard and new SPH formulations are also given through some numerical tests. Furthermore, techniques to obtain the correct SPH solution are also proposed and discussed throughout. As an application of the proposed method, the effect of the dilatancy angle on the failure mechanism of a two‐sided embankment subjected to a high groundwater table is presented and compared with that of other solutions. Finally, the proposed formulation can be considered a basic formulation for further developments of SPH for saturated soils. Copyright © 2011 John Wiley & Sons, Ltd.     

Lagrangian meshfree particles method (SPH) for large deformation and failure flows of geomaterial using elastic–plastic soil constitutive model

Simulation of large deformation and post‐failure of geomaterial in the framework of smoothed particle hydrodynamics (SPH) are presented in this study. The Drucker–Prager model with associated and non‐associated plastic flow rules is implemented into the SPH code to describe elastic–plastic soil behavior. In contrast to previous work on SPH for solids, where the hydrostatic pressure is often estimated from density by an equation of state, this study proposes to calculate the hydrostatic pressure of soil directly from constitutive models. Results obtained in this paper show that the original SPH method, which has been successfully applied to a vast range of problems, is unable to directly solve elastic–plastic flows of soil because of the so‐called SPH tensile instability. This numerical instability may result in unrealistic fracture and particles clustering in SPH simulation. For non‐cohesive soil, the instability is not serious and can be completely removed by using a tension cracking treatment from soil constitutive model and thereby give realistic soil behavior. However, the serious tensile instability that is found in SPH application for cohesive soil requires a special treatment to overcome this problem. In this paper, an artificial stress method is applied to remove the SPH numerical instability in cohesive soil. A number of numerical tests are carried out to check the capability of SPH in the current application. Numerical results are then compared with experimental and finite element method solutions. The good agreement obtained from these comparisons suggests that SPH can be extended to general geotechnical problems. Copyright © 2008 John Wiley & Sons, Ltd.     

Sustainable groundwater development in the coastal Tra Vinh province in Vietnam under saltwater intrusion and climate change

Three-dimensional transient groundwater flow and saltwater transport models were constructed to assess the impacts of groundwater abstraction and climate change on the coastal aquifer of Tra Vinh province (Vietnam). The groundwater flow model was calibrated with groundwater levels (2007–2016) measured in 13 observation wells. The saltwater transport model was compared with the spatial distribution of total dissolved solids. Model performance was evaluated by comparing observed and simulated groundwater levels. The projected rainfalls from two climate models (MIROC5 and CRISO Mk3.6) were subsequently used to simulate possible effects of climate changes. The simulation revealed that groundwater is currently depleted due to overabstraction. Towards the future, groundwater storage will continue to be depleted with the current abstraction regime, further worsening in the north due to saltwater intrusion from inland trapped saltwater and on the coast due to seawater intrusion. Notwithstanding, the impact from climate change may be limited, with the computed groundwater recharge from the two climate models revealing no significant change from 2017 to 2066. Three feasible mitigation scenarios were analyzed: (1) reduced groundwater abstraction by 25, 35 and 50%, (2) increased groundwater recharge by 1.5 and 2 times in the sand dunes through managed aquifer recharge (reduced abstraction will stop groundwater-level decline, while increased recharge will restore depleted storage), and (3) combining 50% abstraction reduction and 1.5 times recharge increase in sand dune areas. The results show that combined interventions of reducing abstraction and increasing recharge are necessary for sustainable groundwater resources development in Tra Vinh province.

     

Prediction of Rock Size Distribution in Mine Bench Blasting Using a Novel Ant Colony Optimization-Based Boosted Regression Tree Technique

Natural Resources Research - In this paper, we used artificial intelligence (AI) techniques to investigate the relation between the rock size distribution (RSD) and blasting parameters for rock...     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Investigation of earthquake tsunami sources, capable of affecting Vietnamese coast

Based on the analysis of tectonic feature and geodynamic characteristics of regional faults systems in the southeast Asia, 9 source zones capable of generating tsunamis affecting Vietnamese coast were delineated in the South China Sea and adjacent sea areas. Statistical methods were applied to estimate the seismic hazard parameters for each source zone, which can be used for the detail tsunami hazard assessment in the future. Maximum earthquake magnitude is predicted for the Manila Trench (8.3?C8.7), the Sulu Sea (8.0?C8.4), and the Selebes Sea source zones (8.1?C8.5). Among the source zones, the Manila Trench, west of the Philippines is considered as a most potential tsunami source, affecting the Vietnamese coast. The estimated M _max values were used to develop simple scenarios (with a point source assumption) to calculate the tsunami travel time from each source zone to the Vietnamese coast. The results show that for the Manila Trench source zone, tsunami can hit the Vietnamese coast in 2?h at the earliest.     

Spatio-temporal analysis of recent groundwater-level trends in the Red River Delta, Vietnam

A groundwater-monitoring network has been in operation in the Red River Delta, Vietnam, since 1995. Trends in groundwater level (1995?C2009) in 57 wells in the Holocene unconfined aquifer and 63 wells in the Pleistocene confined aquifer were determined by applying the non-parametric Mann-Kendall trend test and Sen??s slope estimator. At each well, 17 time series (e.g. annual, seasonal, monthly), computed from the original data, were analyzed. Analysis of the annual groundwater-level means revealed that 35?% of the wells in the unconfined aquifer showed downward trends, while about 21?% showed upward trends. On the other hand, confined-aquifer groundwater levels experienced downward trends in almost all locations. Spatial distributions of trends indicated that the strongly declining trends (>0.3?m/year) were mainly found in urban areas around Hanoi where there is intensive abstraction of groundwater. Although the trend results for most of the 17 time series at a given well were quite similar, different trend patterns were detected in several. The findings reflect unsustainable groundwater development and the importance of maintaining groundwater monitoring and a database in the Delta, particularly in urban areas.     

Stable isotope-guided analysis of biomagnification profiles of arsenic species in a tropical mangrove ecosystem

We performed stable carbon and nitrogen-guided analyses of biomagnification profiles of arsenic (As) species, including total As, lipid-soluble As, eight water-soluble As compounds (arsenobetaine (AB), arsenocholine (AC), tetramethylarsonium ion (TETRA), trimethylarsine oxide (TMAO), dimethylarsinic acid (DMA), monomethylarsonic acid (MMA), arsenate (As[V]), and arsenite (As[III])), and non-extracted As in a tropical mangrove ecosystem in the Ba Ria Vung Tau, South Vietnam. Arsenobetaine was the predominant As species (65-96% of water-soluble As). Simple linear regression slopes of log-transformed concentrations of total As, As fractions or individual As compounds on stable nitrogen isotopic ratio (δ15N) values are regarded as indices of biomagnification. In this ecosystem, lipid-soluble As (slope, 0.130) and AB (slope, 0.108) were significantly biomagnified through the food web; total As and other water-soluble As compounds were not. To our knowledge, this is one of the first reports on biomagnification profiles of As compounds from a tropical mangrove ecosystem.