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571.
Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10RF and 10SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.  相似文献   
572.
Sustainable approach to tourism development is rarely addressed among authorities and participants. This research presents an approach based on an integrated use of analytic network process (ANP) and geographic information system (GIS) for sustainable tourism planning in Cameron Highlands, Malaysia. ANP was utilized to evaluate the relative priorities for the sustainable tourism development of the highlands based on chosen criteria. ANP results were then transferred into GIS environment and attached to their respective criterion maps. Subsequently, GIS-based sustainable tourism map was generated, which defines the various types of tourism activities that are compatible with the highlands according to the sensitivity of the highland’s environment. The final output was validated using Cameron Highlands Local Plan.  相似文献   
573.
In the present research, effect of silica fume as an additive and oil polluted sands as aggregates on compressive strength of concrete were investigated experimentally. The amount of oil in the designed mixtures was assumed to be constant and equal to 2% of the sand weight. Silica fume accounting for 10%, 15% and 20% of the weight is added to the designed mixture. After preparation and curing, concrete specimens were placed into the three different conditions: fresh, brackish and saltwater environments (submerged in fresh water, alternation of exposed in air & submerged in sea water and submerged in sea water). The result of compressive strength tests shows that the compressive strength of the specimens consisting of silica fume increases significantly in comparison with the control specimens in all three environments. The compressive strength of the concrete with 15% silica fume content was about 30% to 50% higher than that of control specimens in all tested environments under the condition of using polluted aggregates in the designed mixture.  相似文献   
574.
We analyze the problem of preventing biological invasions caused by ships transporting internationally traded goods between countries and continents. Specifically, we ask the following question: should a port manager have a small number of inspectors inspect arriving ships less stringently or should this manager have a large number of inspectors inspect the same ships more stringently? We use a simple queuing-theoretic framework and show that if decreasing the economic cost of regulation is very important then it makes more sense for the port manager to choose the less stringent inspection regime. In contrast, if reducing the damage from biological invasions is more salient then the port manager ought to pick the more stringent inspection regime.  相似文献   
575.
Air dispersion modeling by was recently conducted to predict the incremental ground level and inplant concentrations of toxic organic chemicals due to stack and fugitive emissions from Sama Jaya Free Industrial Zone (SJFIZ), Kuching, Sarawak, Malaysia. Simulations of organic air pollutants emitted from industrial facilities in SJFIZ from years 1996 to 2001 were carried out in September 2004 by members of Faculty of Engineering, Universiti Malaysia Sarawak (UNIMAS). The results indicated that there were negligible amount of maximum incremental ground level concentrations of less than 3×10?2 μg/m3 for 365-day average, and less than 18×10?2 μg/m3 for 24 h. average. For in-plant maximum incremental concentrations, it is found that the simulated results were much lower than TWA values, except xylene. The predicted in plant concentration of xylene was 119.21 (parts per million) ppm as compared to ACGIH TLV-TWA of 100 ppm approximately 19% higher than ACGIH recommended values. From this study, it was concluded that both nearby population and in plant workers were not potentially at risk to exposing organic chemicals far lower than the threshold limit levels set by ACGIH.  相似文献   
576.
A GPS-aided Inertial Navigation System (GAINS) is used to determine the orientation? position and velocity of ground and aerial vehicles. The data measured by Inertial Navigation System (INS) and GPS are commonly integrated through an Extended Kalman Filter (EKF). Since the EKF requires linearized models and complete knowledge of predefined stochastic noises? the estimation performance of this filter is attenuated by unmodeled nonlinearity and bias uncertainties of MEMS inertial sensors. The Attitude Heading Reference System (AHRS) is applied based on the quaternion and Euler angles methods. A moving horizon-based estimator such as Model Predictive Observer (MPO) enables us to approximate and estimate linear systems affected by unknown uncertainties. The main objective of this research is to present a new MPO method based on the duality principle between controller and observer of dynamic systems and its implementation in AHRS mode of a low-cost INS aided by a GPS. Asymptotic stability of the proposed MPO is proven by applying Lyapunov’s direct method. The field test of a GAINS is performed by a ground vehicle to assess the long-time performance of the MPO method compared with the EKF. Both the EKF and MPO estimators are applied in AHRS mode of the MEMS GAINS for the purpose of real-time performance comparison. Furthermore? we use flight test data of the GAINS for evaluation of the estimation filters. The proposed MPO based on both the Euler angles and quaternion methods yields better estimation performances compared to the classic EKF.  相似文献   
577.
The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.  相似文献   
578.
After the earthquake occurrence, collecting correct information about the extent of damage is essential for managing critical conditions and allocating limited resources. The prepared building damage maps sometimes bring about waste of time required for rescuing individuals under the rubble by wrongly conducting rescue teams toward regions with a lower rescue priority. In this research, an algorithm based on using a proposed standard at database level was developed to prioritize damaged buildings by considering five key elements of land use type, the degree of damage to buildings, the land use differentiation index, time of the highest population density in each land use, and time of disaster’s incidence. The steps of the proposed method which was implemented in the MATLAB environment include: detecting buildings on the pre- and post-event imagery, implementing texture features for each candidate building, choosing the optimal features by genetic algorithm, determining the degree of building damage in three classes of negligible damage, substantial damage, and heavy damage by using the difference between chosen features as inputs of the designed neurofuzzy inference system. Data collected from field observations were compared to the output obtained from the proposed algorithm. This comparison presented a general accuracy of 88% and Kappa coefficient of 79% in the classification of buildings into three damage classes. The proposed standard then was used for classifying damaged buildings into relief priorities of high, medium, and low. Findings revealed that the relief priority map could be a basis for correct guidance of relief and rescue teams during crucial times following earthquakes.  相似文献   
579.
A comprehensive paleoseismological investigations was carried out along Joggers Park Fault (striking NW-SE) reveals pervasive traces of active compressional tectonics (blind thrusting) and shallow-surface recent tectonic signatures along the fault. The geometry and trends in the tectonic deformation of the blind zone (shallow-subsurface stratigraphy) were examined with GPR and electrical resistivity tomography (ERT) and vertical electrical sounding (VES), strongly demonstrates severe tectonic deformation (persistent tectonic style) from Quaternary to Holocene times in a compressive stress regime. The surface manifestation of deformation (topographic expression) along the fault can be marked as hanging wall uplift and can be visually observed in the exposed sections at nearly half kilometer distance from the table land of Joggers Park in the form of a well-defined disconnected high angle thrust fault scarp (reverse fault). The reverse fault kinematically and intimately linked with underlying fault geometry provides adduced evidence to evaluate strike displacement profiles (displacement length relationship) along the fault. In addition, deformed stacked colluvial wedge below the scarp represents a powerful basis to calibrate recent and long term slip rates of the underlying fault.  相似文献   
580.
We present an analytical solution to estimate the minimum polymer slug size needed to ensure that viscous fingering of chase water does not cause its breakdown during secondary oil recovery. Polymer flooding is typically used to improve oil recovery from more viscous oil reservoirs. The polymer is injected as a slug followed by chase water to reduce costs; however, the water is less viscous than the oil. This can result in miscible viscous fingering of the water into the polymer, breaking down the slug and reducing recovery. The solution assumes that the average effect of fingering can be represented by the empirical Todd and Longstaff model. The analytical calculation of minimum slug size is compared against numerical solutions using the Todd and Longstaff model as well as high resolution first contact miscible simulation of the fingering. The ability to rapidly determine the minimum polymer slug size is potentially very useful during enhanced oil recovery (EOR) screening studies.  相似文献   
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