Discriminating Weathering Degree by Integrating Optical Sensor and SAR Satellite Images for Potential Mapping of Groundwater Resources in Basement Aquifers of Semiarid Regions     

Magaia  Luís André  Koike  Katsuaki  Goto  Tada-nori  Masoud  Alaa Ahmed 《Natural Resources Research》2019,28(3):1197-1215

Natural Resources Research - Unlike in coastal and sedimentary basins, regional-scale exploration of groundwater resources using only geophysical methods is costlier in consolidated rocks such as...  相似文献   

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms   总被引：2，自引：0，他引：2  

Alireza ARABAMERI  Biswajeet PRADHAN  Khalil REZAE  Masoud SOHRABI  Zahra KALANTARI 《山地科学学报》2019,(3)

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.  相似文献   

GPS radio occultation constellation design with the optimal performance in Asia Pacific region     

Milad Asgarimehr  Masoud Mashhadi Hossainali 《Journal of Geodesy》2015,89(6):519-536

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Spatio‐temporal evaluation of the groundwater quality in Kafr Al‐Zayat District,Egypt     

Alaa A. Masoud 《水文研究》2013,27(20):2987-3002

Eighteen groundwater well sites located in Kafr Al‐Zayat (Egypt) were sampled monthly from January 2009 to November 2011 for microbial content, Mn⁺², Fe⁺², total dissolved solids (TDS), total hardness, NO₃^?, and turbidity. The data were analyzed combining the integrated use of factor and cluster analyses as well as the geostatistical semi‐variogram modeling. The prime objectives were to assess the groundwater suitability for drinking, to document the factors governing the spatio‐tempral variability, and to recognize distinctive groundwater quality patterns to help enable effective sustainability and proactive management of the limited resource. The groundwater microbial, Mn⁺², Fe⁺², TDS, and total hardness contents violated the drinking water local standards while the turbidity and the nitrate content complied with them. Factor analysis indicated that the microbial content is the most influential factor raising the variability potential followed, in decreasing order, by Mn²⁺, Fe²⁺, TDS, NO₃^?, turbidity, and finally the total hardness. Turbidity resulting from urban and agricultural runoff was strongly associated with most of the quality parameters. Quality parameters fluctuate sporadically without concrete pattern in space and time while their variability scores peak in November every year. Three spatially distinctive quality patterns were recognized that were consistent with and affected by the cumulative effects of the local topography, depth to water table, thickness of the silty clay (cap layer), surface water, and groundwater flow direction and hence the recharge from contaminated surface canals and agricultural drains. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine     

Bayat  Parichehr  Monjezi  Masoud  Rezakhah  Mojtaba  Armaghani  Danial Jahed 《Natural Resources Research》2020,29(6):4121-4132

Natural Resources Research - It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted...  相似文献   

Technical and Non-technical Challenges of Development of Offshore Petroleum Reservoirs: Characterization and Production     

Seyyedattar  Masoud  Zendehboudi  Sohrab  Butt  Stephen 《Natural Resources Research》2020,29(3):2147-2189

Natural Resources Research - Offshore oil and gas reservoirs comprise a significant portion of the world’s reserve base, and their development is expected to help close a potential gap in the...  相似文献   

Late Cretaceous forearc ophiolites of Iran     

Hadi Shafaii Moghadam  Robert J. Stern 《Island Arc》2011,20(1):1-4

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Displacement reducer fuses for improving seismic performance of caisson quay walls     

Amirali Mostafavi Moghadam  Abbas Ghalandarzadeh  Majid Moradi  Ikuo Towhata  Pouria Hajialikhani 《Bulletin of Earthquake Engineering》2011,9(4):1259-1288

This research is to study the efficiency of displacement reducer fuses, installed behind the caisson quay walls for controlling the dynamic backfill thrust and minimizing the displacement, settlement and tilting of the walls. For this purpose, two types of fuses, Displacement Reducer Panels (DRP) and Displacement Reducer Elements (DRE), were constructed and installed behind the wall. The DRPs were constructed by hollow Polypropylene sheets to reproduce elastoplastic and plastic mechanical behaviors. The DREs were cylindrical stainless-steel dampers, working upon friction mechanism that can reproduce perfect plastic behavior. Here, two series of shaking table 1-g tests were performed with DRP and DRE applications. In this regard, different mechanical behaviors and capacities were considered for fuses against demand thrusts of backfill in order to compare the mitigation tests with no-mitigation cases. Harmonic base motions with constant amplitude and constant frequency were used in the model test. The foundation soil and the backfill soil were constructed with the relative densities of 85 and 25%, respectively, to reproduce non-liquefiable base layer and loose backfill situations in the model, respectively. The results showed remarkable reduction in kinetic energy, dynamic backfill thrust and consequently seaward movement, settlement and inclination of the caisson quay wall in case of using fuses with plastic behaviors behind the wall.  相似文献   

Superiority of neural networks for pillar stress prediction in bord and pillar method   总被引：1，自引：1，他引：0  

M. Monjezi  Seyed Masoud Hesami  Manoj Khandelwal 《Arabian Journal of Geosciences》2011,4(5-6):845-853

Estimation of pillar stress is a crucial task in underground mining. This is used to determine pillar dimensions, room width, roof conditions, and general mine layout. There are several methods for estimating induced stresses due to underground excavations, i.e., empirical methods, numerical solutions, and currently artificial intelligence (AI). AI based techniques are gradually gaining popularity especially for problems involving uncertainty. In this paper, an attempt has been made to predict stresses developed in the pillars of bord and pillar mining using artificial neural network. A comparison has also been done to compare the obtained results with the boundary element method as well as measured field values. For this purpose, a multilayer perceptron neural network model was developed. A number of architectures with different hidden layers and neurons were tried to get the best solution, and the architecture 5-20-8-1 was found to be an optimum solution. Sensitivity analysis was also carried out to understand the influence of important input parameters on pillar stress concentration.  相似文献   

Risk Assessment of Vegetation Degradation Using Geographic Information System: A Case Study of Qareh Aghaj Basin,Iran     

Masoud MASOUDI  Parviz JOKAR 《资源与生态学报(英文版)》2018,9(5):477-483

The entire land of Southern Iran faces problems arising out of various types of land degradation of which vegetation degradation forms one of the major types. The Qareh Aghaj basin (1 265 000 ha), which covers the upper reaches of Mond River, has been chosen for a test risk assessment of this type. The different kinds of data for indicators of vegetation degradation were gathered from the records and published reports of the governmental offices of Iran. A new model has been developed for assessing the risk of vegetation degradation. Taking into consideration nine indicators of vegetation degradation the model identifies areas with “Potential Risk” (risky zones) and areas of “Actual Risk” as well as projects the probability of the worse degradation in future. The preparation of risk maps based on the GIS analysis of these indicators will be helpful for prioritizing the areas to initiate remedial measures. By fixing the thresholds of severity classes of the nine indicators a hazard map for each indicator was first prepared in GIS. The risk classes were defined on the basis of risk scores arrived at by assigning the appropriate attributes to the indicators and the risk map was prepared by overlaying nine hazard maps in the GIS. Areas under actual risk have been found to be widespread (78%) in the basin and when the risk map classified into subclasses of potential risk with different probability levels the model projects a statistical picture of the risk of vegetation degradation.  相似文献