排序方式： 共有97条查询结果，搜索用时 406 毫秒
61.
Amir Karimian Torghabeh Reza Rezaee Reza Moussavi-Harami Biswajeet Pradhan Mohammad Reza Kamali Ali Kadkhodaie-Ilkhchi 《Central European Journal of Geosciences》2014,6(3):393-402
Identifying reservoir electrofacies has an important role in determining hydrocarbon bearing intervals. In this study, electrofacies of the Kockatea Formation in the Perth Basin were determined via cluster analysis. In this method, distance data were initially calculated and then connected spatially by using a linkage function. The dendrogram function was used to extract the cluster tree for formations over the study area. Input logs were sonic log (DT), gamma ray log (GR), resistivity log (IND), and spontaneous potential (SP). A total of 30 reservoir electrofacies were identified within this formation. Integrated geochemical and petrophysics data showed that zones with electrofacies 3, 4, 9, and 10 have potential for shale gas production. In addition, the results showed that cluster analysis is a precise, rapid, and cost-effective method for zoning reservoirs and determining electrofacies in hydrocarbon reservoirs. 相似文献
62.
Hojjat Ollah Safari Saeid Pirasteh Biswajeet Pradhan Hossein Amid 《Arabian Journal of Geosciences》2013,6(4):1101-1112
The Pisa 2 tunnel with 740 m in length and 20° N trend is located along the Kazerun fault zone in Simply Folded Belt of Zagros, Iran. This tunnel has been excavated in the fractured incompetent marl layers with high expansive pressure of up to 2 kg/cm2. In this study, the geological hazards along the tunnel have been recognized and categorized. This study revealed that, in the long-term usage of the tunnel, the lining did not endure against the loading and the secondary leakages. It is mainly attributed due to the non-efficiencies of drainage and isolation systems in the tunnel site. Therefore, it caused asphalt damage, drainage damage, and wall distortion. FLAC3D software has been used in this research. We conducted various analyses for pre-excavation stress states, syn-excavation, and post-excavation strain states. The results showed no indication of instability and critical deformations during the excavation time. It also revealed that due to the non-efficiencies of drainage and isolation systems against secondary leakages and consequently marl expansion, the volumetric and shear strains (i.e., expansions and displacements) have exceeded from the critical states of strain along the tunnel. For various remedy purpose, this paper attempted several measures that can be taken in order to modify the drainage and isolation systems along the tunnel area. The reconstruction of drainage systems with suitable reinforced concrete and adequate slope has been proposed. The width of channel and isolation of backside of lining and implementation of multi-order outlets (i.e., backside of lining) for draining of groundwater into where the main drainage systems are located in the tunnel gallery were suggested. 相似文献
63.
Samih Al Rawashdeh Rami Ruzouq A’kif Al-Fugara Biswajeet Pradhan S. H. Abu-Hamatteh Ziad Abu Rumman Ghayda 《Arabian Journal of Geosciences》2013,6(9):3241-3248
Remote sensing (RS) and geographic information systems (GIS) are very useful for environmental-related studies, particularly in the field of surface water studies such as monitoring of lakes. The Dead Sea is exposed to very high evaporating process with considerable scarcity of water sources, thus leading to a remarkable shrinkage in its water surface area. The lake suffers from dry out due to the negative balance of water cycle during the previous four decades. This paper discusses the application of RS, GIS, and Global Positioning System to estimate the lowering and the shrinkage of Dead Sea water surface over the period 1810–2005. A set of multi-temporal remote sensing images were collected and processed to show the lakes aerial extend shrinkage from 1973 up to 2004. Remote sensing data were used to extract spatial information and to compute the surface areas for Dead Sea for various years. The current study aims at estimating the fluctuation of Dead Sea level over the study period with special emphasis on the environmental impact assessment that includes the degradation level of the Dead Sea. The results indicated that there is a decrease of 20 m in the level of the Dead Sea that has occurred during the study period. Further, the results showed that the water surface area of the Dead Sea has shrunk from 934.26 km2 in 1973 to 640.62 km2 in 2004. 相似文献
64.
65.
Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran 总被引：41，自引：8，他引：33
The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose. 相似文献
66.
Ali Mahmoud Mahmoud Mohamed Shendi Biswajeet Pradhan Fatma Attia 《Central European Journal of Geosciences》2009,1(3):347-367
The North-Western Coast of Egypt (NWCE) represents one of the high priority regions for future development in the country. El-Hammam area is located in the NWCE with an area of 94752 acres and is one of the main challenging regions for sustaianble development. In this study, we have used remote sensing and soil data in combination with GIS tools, for land use sustainable analysis (SLU) in El-Hammam area. The SLU was established based on various factors such as: land capability and suitability, water resources availability, economic return from water and financial return from land and water. A physiographic soil map for the study area was prepared using remote sensing and GIS. Multiple field surveys were carried out for collecting information on various soil map units (SMUs) and their profiles. Laboratory analysis for the collected samples was performed, and then the soil properties were stored as attributes in a geographical soil database linked with the SMUs. Furthermore, land capability assessment was done to define the suitable areas for agricultural production using a capability model built in ALES software. Results indicate that the area currently lacks high capability and moderate capability classes. By improving the soil properties, the soil can attain potential capability; and 55630 acres will become marginally capable. The assessment of soil physical suitability for different land use types (LUTs) were analysed in ALES software, in order to generate the most suitable areas. The results from the land suitability analysis indicated that, 17114 acres are moderately suitable for wheat and sorghum; whereas 15823 acres are moderately suitable for barley and 12752 acres are moderately suitable for maize, olive and figs. Finally, the SLU was investigated based on two scenarios; (1) the most SLU under the conditions of shortage of irrigation water: clover, barley and sorghum against figs, as the irrigation requirements for barley and sorghum are low; (2) the most sustainable land use in the conditions of irrigation availability will be wheat and maize against figs and guava. From the results it is quite evident that GIS combined with modeling approaches are powerful tools for decision making in the study area. 相似文献
67.
Evaluation of groundwater vulnerability to pollution using DRASTIC framework and GIS 总被引：1，自引：0，他引：1
Groundwater management has a prominent role in the world especially in arid and semi-arid areas which have a shortage of water, and due to this serious problem, many researchers work on that for prevention and managing the water recourses to conserve and monitor sources. DRASTIC index can be put forward for estimating of groundwater vulnerability to such pollution. The main purpose of using the groundwater vulnerability model is to map groundwater susceptibility to pollution in different areas. However, this method has been used in various areas without modification, disregarding the effects of pollution type and characteristics. Thus, this technique must be standardized and approved for Kerman plain. Vulnerability evaluation to explain areas that are more vulnerable to contamination from anthropogenic sources has become a prominent element for land use planning and tangible resource management. This contribution aims at evaluating groundwater vulnerability by applying the DRASTIC index as well as employ sensitivity analyses to evaluate the comparative prominent of the model parameters for groundwater vulnerability in Kerman plain in the southeastern part of Iran. Moreover, the potential of vulnerability to pollution is more accurately assessed by optimizing the weights of the DRASTIC parameters with the single-parameter sensitivity analysis (SPSA). The new weights were calculated. The result of the study revealed that the DRASTIC-Sensitivity analysis exhibit more efficiently than the traditional method for a nonpoint source pollution. Observation of ultimate nitrate showed the result of DRASTIC-SPSA has more accuracy. The GIS method offers an efficient environment for carrying out assessments and greater capabilities for dealing with a huge quantity of spatial data. 相似文献
68.
This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS)
and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs
and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing
tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are
topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance
from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation
amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility
was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis
were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%.
The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis
in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard
map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing
infrastructure like transportation network. 相似文献
69.
Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models 总被引：28，自引：9，他引：28
The aim of this study is to evaluate the landslide hazards at Selangor area, Malaysia, using Geographic Information System
(GIS) and Remote Sensing. Landslide locations of the study area were identified from aerial photograph interpretation and
field survey. Topographical maps, geological data, and satellite images were collected, processed, and constructed into a
spatial database in a GIS platform. The factors chosen that influence landslide occurrence were: slope, aspect, curvature,
distance from drainage, lithology, distance from lineaments, land cover, vegetation index, and precipitation distribution.
Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by frequency ratio and logistic
regression models. The results of the analysis were verified using the landslide location data and compared with probability
model. The comparison results showed that the frequency ratio model (accuracy is 93.04%) is better in prediction than logistic
regression (accuracy is 90.34%) model. 相似文献
70.
Dieu Tien Bui Tran Anh Tuan Harald Klempe Biswajeet Pradhan Inge Revhaug 《Landslides》2016,13(2):361-378
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. 相似文献