A Novel Intelligent ELM-BBO Technique for Predicting Distance of Mine Blasting-Induced Flyrock     

Murlidhar  Bhatawdekar Ramesh  Kumar  Deepak  Jahed Armaghani  Danial  Mohamad  Edy Tonnizam  Roy  Bishwajit  Pham  Binh Thai 《Natural Resources Research》2020,29(6):4103-4120

Natural Resources Research - Blasting is an economical technique for rock breaking in hard rock excavation. One of its complex undesired environmental effects is flyrock, which may result in human...  相似文献   

Kinetics of Fe(III) precipitation in aqueous solutions at pH 6.0-9.5 and 25 °C     

A. Ninh Pham 《Geochimica et cosmochimica acta》2006,70(3):640-650

The kinetics of Fe(III) precipitation in synthetic buffered waters have been investigated over the pH range 6.0-9.5 using a combination of visible spectrophotometry, ⁵⁵Fe radiometry combined with ion-pair solvent extraction of chelated iron and numerical modeling. The rate of precipitation, which is first order with respect to both dissolved and total inorganic ferric species, varies by nearly two orders of magnitude with a maximum rate constant of 16 ± 1.5 × 10⁶ M⁻¹ s⁻¹ at a pH of around 8.0. Our results support the existence of the dissolved neutral species, Fe(OH)₃⁰, and suggest that it is the dominant precursor in Fe(III) polymerization and subsequent precipitation at circumneutral pH. The intrinsic rate constant of precipitation of Fe(OH)₃⁰ was calculated to be allowing us to predict rates of Fe(III) precipitation in the pH range 6.0-9.5. The value of this rate constant, and the variation in the precipitation rate constant over the pH range considered, are consistent with a mechanism in which the kinetics of iron precipitation are controlled by rates of water exchange in dissolved iron hydrolysis species.  相似文献   

Phytoplankton distribution and productivity in a highly turbid, tropical coastal system (Bach Dang Estuary, Vietnam)     

Rochelle-Newall EJ  Chu VT  Pringault O  Amouroux D  Arfi R  Bettarel Y  Bouvier T  Bouvier C  Got P  Nguyen TM  Mari X  Navarro P  Duong TN  Cao TT  Pham TT  Ouillon S  Torréton JP 《Marine pollution bulletin》2011,62(11):2317-2329

Phytoplankton diversity, primary and bacterial production, nutrients and metallic contaminants were measured during the wet season (July) and dry season (March) in the Bach Dang Estuary, a sub-estuary of the Red River system, Northern Vietnam. Using canonical correspondence analysis we show that phytoplankton community structure is potentially influenced by both organometallic species (Hg and Sn) and inorganic metal (Hg) concentrations. During March, dissolved methylmercury and inorganic mercury were important factors for determining phytoplankton community composition at most of the stations. In contrast, during July, low salinity phytoplankton community composition was associated with particulate methylmercury concentrations, whereas phytoplankton community composition in the higher salinity stations was more related to dissolved inorganic mercury and dissolved mono and tributyltin concentrations. These results highlight the importance of taking into account factors other than light and nutrients, such as eco-toxic heavy metals, in understanding phytoplankton diversity and activity in estuarine ecosystems.  相似文献   

A numerical model of beach morphological evolution due to waves and currents in the vicinity of coastal structures   总被引：2，自引：0，他引：2  

Pham Thanh Nam  Magnus Larson  Hans Hanson  Le Xuan Hoan 《Coastal Engineering》2011

A numerical model was developed of beach morphological evolution in the vicinity of coastal structures. The model includes five sub-models for random wave transformation, surface roller development, nearshore wave-induced currents, sediment transport, and morphological evolution. The model was validated using high-quality data sets obtained during experiments with a T-head groin and a detached breakwater in the basin of the Large-scale Sediment Transport Facility at the Coastal and Hydraulics Laboratory in Vicksburg, Miss, USA. The simulations showed that the model reproduced well the wave conditions, wave-induced currents, and beach morphological evolution in the vicinity of coastal structures. Both salient and tombolo formation behind a T-head groin and a detached breakwater were simulated with good agreement compared to the measurements.  相似文献   

A Method to Represent a Well in a Three-Dimensional Discrete Fracture Network Model     

Hai Pham  Rishi Parashar  Nicole Sund  Karl Pohlmann 《Ground water》2021,59(2):281-286

In discrete fracture network (DFN) modeling, fractures are randomly generated and placed in the model domain. The rock matrix is considered impermeable. Small fractures and isolated fractures are often ignored to reduce computational expense. As a result, the rock matrix between fractures could be large and intersections may not be found between a well introduced in the model and the hydraulically connected fracture networks (fracture backbones). To overcome this issue, this study developed a method to conceptualize a well in a three-dimensional (3D) DFN using two orthogonal rectangular fractures oriented along the well's axis. Six parameters were introduced to parameterize the well screen and skin zone, and to control the connectivity between the well and the fracture backbones. The two orthogonal fractures were discretized using a high-resolution mesh to improve the quality of flow and transport simulations around and along the well. The method was successfully implemented within dfnWorks 2.0 (Hyman et al. 2015) to incorporate a well in a 3D DFN and to track particles leaving an injection well and migrating to a pumping well. Verification of the method against MODFLOW/MODPATH found a perfect match in simulated hydraulic head and particle tracking. Using three examples, the study showed that the method ensured the connectivity between wells and fracture backbones, and honored the physical processes of flow and transport along and around wells in DFNs. Recommendations are given for estimating the values of the six introduced well parameters in a real-world case study.  相似文献   

A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling     

Binh T. Pham  Indra Prakash  Khabat Khosravi  Kamran Chapi  Phan T. Trinh  Trinh Q. Ngo 《国际地球制图》2013,28(13):1385-1407

Abstract

In this study, the main goal is to compare the predictive capability of Support Vector Machines (SVM) with four Bayesian algorithms namely Naïve Bayes Tree (NBT), Bayes network (BN), Naïve Bayes (NB), Decision Table Naïve Bayes (DTNB) for identifying landslide susceptibility zones in Pauri Garhwal district (India). First, landslide inventory map was built using 1295 historical landslide data, then in total sixteen influencing factors were selected and tested for landslide susceptibility modelling. Performance of the model was evaluated and compared using Statistical based index methods, Area under the Receiver Operating Characteristic (ROC) curve named AUC, and Chi-square method. Analysis results show that that the SVM has the highest prediction capability, followed by the NBT, DTNBT, BN and NB, respectively. Thus, this study confirms that the SVM is one of the benchmark models for the assessment of susceptibility of landslides.  相似文献   

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam     

Dieu Tien Bui  Binh Thai Pham  Quoc Phi Nguyen  Nhat-Duc Hoang 《International Journal of Digital Earth》2016,9(11):1077-1097

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction, named as DE–LSSVM_SLP. The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model. In this research, a GIS database with 129 historical landslide records in the Quy Hop area (Central Vietnam) has been collected to establish the hybrid model. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the performance of the newly constructed model. Experimental results show that the proposed model has high performances with approximately 82% of AUCs on both training and validating datasets. The model’s results were compared with those obtained from other methods, Support Vector Machines, Multilayer Perceptron Neural Networks, and J48 Decision Trees. The result comparison demonstrates that the DE–LSSVM_SLP deems best suited for the dataset at hand; therefore, the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.  相似文献   

Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms     

Parvin  Farhana  Ali  Sk Ajim  Calka  Beata  Bielecka  Elzbieta  Linh  Nguyen Thi Thuy  Pham  Quoc Bao 《Theoretical and Applied Climatology》2022,149(1-2):639-659

Theoretical and Applied Climatology - Flood is considered as the most devastating natural hazards that cause the death of many lives worldwide. The present study aimed to predict flood...  相似文献   

Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India     

Chakrabortty  Rabin  Pal  Subodh Chandra  Sahana  Mehebub  Mondal  Ayan  Dou  Jie  Pham  Binh Thai  Yunus  Ali P. 《Natural Hazards》2020,104(2):1259-1294

Natural Hazards - Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high...  相似文献   

Evaluation and comparison of LogitBoost Ensemble,Fisher’s Linear Discriminant Analysis,logistic regression and support vector machines methods for landslide susceptibility mapping     

Binh Thai Pham  Indra Prakash 《国际地球制图》2019,34(3):316-333

The purpose of this study was to investigate and compare the capabilities of four machine learning methods namely LogitBoost Ensemble (LBE), Fisher’s Linear Discriminate Analysis (FLDA), Logistic Regression (LR) and Support Vector Machines (SVM) to select the best method for landslide susceptibility mapping. A part of landslide prone area of Tehri Garhwal district of Uttarakhand state, India, was selected as a case study. Validation of models was carried out using statistical analysis, the chi square test and the Receiver Operating Characteristic (ROC) curve. Result analysis shows that the LBE has the highest prediction ability (AUC = 0.972) for landslide susceptibility mapping, followed by the SVM (0.945), the LR (0.873) and the FLDA (0.870), respectively. Therefore, the LBE is the best and a promising method in comparison to other three models for landslide susceptibility mapping.  相似文献