Application of multimedia methodology for investigation of karst water in highland regions of Ha Giang Province, Vietnam     

Ngoc Thach Nguyen  Ngoc Hai Pham  Xuan Canh Pham  Thi Thuy Hang Nguyen  Van Lam Nguyen  Thi Thanh Thuy Duong 《Environmental Earth Sciences》2013,70(2):531-542

Ha Giang is one of the largest, northern border provinces of Vietnam, consisting of four districts: Yen Minh, Quan Ba, Dong Van and Meo Vac. This province features varied karst landscape of Carboniferous–Permian limestone. The region has been recognized by UNESCO as one of the 77 geological parks in the world and the second in Southeast Asia on 3 October 2012. In the dry season, little or no rain is recorded; therefore, surface water is very scarce. For this reason, proper delineation and exploitation of the groundwater resource is critical for sustainable water supply. This has been identified as an important challenge under the scientific project KC-08-10 in the national program KC-08. Remote sensing and GIS were used to decipher the signature of karst water in the highland of Ha Giang. Information layers generated were subjected to multi-criteria evaluation using analytic hierarchy process for decision making to identify ideal locations for groundwater prospecting. The study resulted in delineation of ten zones for all regions and 18 ideal drilling sites in Tam Son Town of Quan Ba District. Drilling and resistivity soundings were performed to assess the success of the interpretation. Deep resistivity survey confirmed low resistivity (200–300 Ωm) near the identified potential sites in Tam Son Town of Quan Ba District. Further, successful drilling at site LKTS1 with a discharge of 7–9 l/s is observed, proving the potential of this methodology for rapid exploration of groundwater in water-scare karst terrains of Vietnam.  相似文献   

Rotenone treatment has a short-term effect on New Zealand stream macroinvertebrate communities     

Lan Pham  David West  Gerard P. Closs 《新西兰海洋与淡水研究杂志》2018,52(1):42-54

Invasive fish eradication is a key management strategy in aquatic ecosystems, and is often accomplished using piscicides such as rotenone. However, the effects of piscicides on aquatic invertebrate communities are poorly understood, particularly over long time scales. We monitored invertebrate communities in two treatment and two reference streams prior to and for one year following the use of rotenone to eradicate trout in Zealandia wildlife sanctuary, Wellington. Immediately following treatment, invertebrate density and taxonomic richness declined significantly, and community composition diverged markedly relative to reference streams, with pollution sensitive taxa declining greater than more tolerant taxa. Treatment streams recovered to pre-treatment conditions within 4–12 months of rotenone application, indicating minor long-term impacts on invertebrate communities. Speed of recovery of individual taxa appeared to be associated with life history variables, e.g. generation times and dispersal ability. Untreated upstream reaches and nearby water bodies likely facilitated successful invertebrate community recovery. Our results demonstrate that rapid recovery of New Zealand stream invertebrate communities is possible within one year of rotenone application.  相似文献   

豫西地区秦岭造山带武当群Nd-Hf同位素组成及其物源特征   总被引：1，自引：0，他引：1  

祝禧艳  陈福坤  杨力  王伟  HIEU Pham Trung  苗来成  张福勤 《岩石学报》2009,25(11):3017-3028

武当群变质沉积-火山岩组合是南秦岭地体中重要的基底岩石,其形成时代和地球化学特征可以为理解秦岭造山带的构造演化提供重要的证据.本文报道豫西地区武当群上部沉积岩和下部中-酸性火山岩Sm-Nd同位素和锆石Lu-Hf同位素组成,探讨火山岩成因和沉积岩物源的同位素特征.上部沉积岩的碎屑锆石初始ε_(Hf)值变化在-30～+10之间,对应的模式年龄值t_(DM2)在1.0Ga至3.2Ga之间,初始ε_(Nd)值在-4.0至-6.0之间.沉积物源表现为主要与扬子陆块有亲缘关系的地壳物质和近源的下部火山岩混合的特征.火山岩的锆石初始εHf值变化在-35～+15之间,对应的模式年龄值t_(DM2)在0.8Ga至3.5Ga之间,集中于1.5～1.8Ga和2.2～2.4Ga两个峰值.2个变质石英角斑岩样品初始ε_(Nd)值分别为-9.2和-10.7,而报道的湖北武当群的玄武-安山质熔岩的初始ε_(Nd)值以正值为主.因此,武当群不同类型的火山岩可能存在着成因差异.具有低初始ε_(Nd)值和ε_(Hf)值特征的火山岩可能由地壳物质的重熔而形成的;有些火山岩具有初始ε_(Hf)值变化范围较大(-35～+15)或正初始ε_(Nd)值的特点,可能是壳、幔物质混合成因,有显著的幔源或新生地壳物质的贡献.武当群Nd-Hf同位素组成和碎屑锆石年龄分布特征表明,与扬子陆块有亲缘关系的南秦岭地体在元古代期间可能经历多期地壳增生和再造作用.  相似文献   

Remarkably uniform oxygen isotope systematics for co-existing pairs of gem-spinel and calcite in marble,with special reference to Vietnamese deposits     

Anthony E. Fallick  Gaston Giuliani  Thomas Rigaudier  Adrian J. Boyce  Van Long Pham  Vincent Pardieu 《Comptes Rendus Geoscience》2019,351(1):27-36

Oxygen isotope systematics for co-existing pairs of gem-spinel and calcite in marble from Vietnam and other worldwide deposits have been determined in order to characterize the O-isotope fractionation between calcite and spinel. In Vietnam, the Δ¹⁸O_cc–sp (= 3.7 ± 0.1‰ for six samples from the An Phu and Cong Troi deposits) is remarkably constant. The combination of these data with those obtained on calcite–spinel pairs of Paigutan (Nepal, n = 2), Ipanko (Tanzania, n = 1), and Mogok (Myanmar, = 2) are also consistent with an overall Δ¹⁸O_cc–sp of 3.6 ± 0.3‰ for all the spinel samples (n = 11). The straight line correlation δ¹⁸O_cc = 0.96 δ¹⁸O_sp + 4.4 is excellent despite their worldwide geographic spread. The increment method of calculating oxygen isotope fractionation gave a geologically unreasonable temperature of formation for both minerals at 1374 °C when compared to temperatures obtained by mineral assemblage equilibrium of these marble type deposits, between 610 and 750 °C. The constant Δ¹⁸O_cc–sp reflects a constant temperature for this amphibolite facies assemblage, whose current best estimate is calculated at 620 ± 40 °C, but unquantified uncertainties remain.  相似文献   

Reply     

V. N. Pham 《Geophysical Journal International》2005,162(2):337-338

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Bagging based Support Vector Machines for spatial prediction of landslides     

Binh Thai Pham  Dieu Tien Bui  Indra Prakash 《Environmental Earth Sciences》2018,77(4):146

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.  相似文献   

Influence of hydraulic characteristics on stability of unsaturated slope under transient seepage conditions     

Khanh Pham  Hyobum Lee  Dongku Kim  In-Mo Lee  Hangseok Choi 《Landslides》2018,15(9):1787-1799

This paper examines the current procedure for determining the soil-water characteristic curve (SWCC) model with a particular focus on its application to slope stability analysis under transient unsaturated seepage conditions. A series of laboratory experiments was performed to determine the SWCC of different soils, ranging from high plasticity clay to silty sand, found across the Korean Peninsula. The experimental results were utilized to identify the suitable SWCC model for each soil type based on the fitting criterion. Also, this paper developed a numerical framework for infinite slope stability analysis under transient unsaturated seepage conditions. The significant advantage of the proposed framework, from the practical viewpoint, is to directly predict the timing of failure and potential failure plane based on rainfall recording. The effect of choice of SWCC models on predictability in stability analysis was evaluated by adopting the present framework along with the identified SWCC models. Furthermore, a case study of landslides after a 3-month rainfall in Pohang, Korea, was revisited to assess the performance of the proposed framework. The obtained results demonstrate the significant role of SWCC model on the results of slope stability analysis. The analysis using the SWCC model satisfying the fitting criterion could still not capture the real behavior of unsaturated soil. The comprehensive transient analysis is strongly suggested as a complementary means to the current fitting criterion for determining the suitable SWCC model for stability analysis under transient seepage conditions.  相似文献   

Rainfall-induced catastrophic landslide in Quang Tri Province: the deadliest single landslide event in Vietnam in 2020     

Van Tien  Pham  Luong  Le Hong  Duc  Do Minh  Trinh  Phan Trong  Quynh  Dinh Thi  Lan  Nguyen Chau  Thuy  Dang Thi  Phi  Nguyen Quoc  Cuong  Tran Quoc  Dang  Khang  Loi  Doan Huy 《Landslides》2021,18(6):2323-2327

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Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran     

Phuong Thao Thi Ngo  Mahdi Panahi  Khabat Khosravi  Omid Ghorbanzadeh  Narges Kariminejad  Artemi Cerda  Saro Lee 《地学前缘(英文版)》2021,12(2):505-519

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.  相似文献   

GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms     

Sk Ajim Ali  Farhana Parvin  Jana Vojteková  Romulus Costache  Nguyen Thi Thuy Linh  Quoc Bao Pham  Matej Vojtek  Ljubomir Gigović  Ateeque Ahmad  Mohammad Ali Ghorbani 《地学前缘(英文版)》2021,12(2):857-876

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Na?ve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Na?ve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).  相似文献