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Natural Hazards - In this study, we present a novel methodology that may be used to analyze tsunami risk along coastal regions. The application of the proposed methodology is demonstrated for the... 相似文献
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Yurdanur Sezginer Unal Elcin Tan S. Sibel Mentes 《Theoretical and Applied Climatology》2013,112(1-2):339-350
Global warming is one of the greatest environmental, economic, and social threats in the world. There are many assessments to estimate climate variability over many regions. A change in the Earth’s surface temperature leads to increase in extreme temperature events, which are harmful to the ecosystem, and moreover, they create danger on human health. In this study, we have selected the western part of Turkey as the study area, since climate change projections for Turkey point out that the highest temperature change can be expected on this region during summer, and the Turkish population is very dense here to be affected by extreme events. We have used apparent temperatures to define the heat waves which we have determined their frequencies for the summer months (June–August) of 1965–2006. Since the regional comparisons of station results are intended, we selected the 90th percentile value for each station as a threshold value to be used in the delineation of heat waves. Then, the number of heat waves is determined by imposing the constraint that apparent temperatures stay above the threshold value at least for three consecutive days. Then, the changes in the number of hot days and heat waves and also their durations are analyzed by using the linear least square method. We have found that the number of hot days, heat waves, and heat wave durations is increased between 1965 and 2006 on the western part of Turkey. Additionally, their rate of change is larger within the last decade and extremes are frequently observed after 1998. Regional distributions show that the tendency of the number of heat wave events increases towards the southern latitudes of the domain. Moreover, we investigated the relationship between the number of hot days and the sea surface temperatures of the Mediterranean Sea and Black Sea. Correlation analyses are carried out by the number of hot days and averaged sea surface temperatures on the regions of the western, central, and eastern Mediterranean Sea and the Black Sea. It is found that the number of hot days of west Turkey is better correlated with the sea surface temperatures averaged over eastern Mediterranean and Black Seas. The number of heat waves is found significantly correlated with the fire occurrences for most of the stations. 相似文献
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An Assessment of the Seismicity of the Bursa Region from a Temporary Seismic Network 总被引：2，自引：0，他引：2
A temporary earthquake station network of 11 seismological recorders was operated in the Bursa region, south of the Marmara
Sea in the northwest of Turkey, which is located at the southern strand of the North Anatolian Fault Zone (NAFZ). We located
384 earthquakes out of a total of 582 recorded events that span the study area between 28.50–30.00°E longitudes and 39.75–40.75°N
latitudes. The depth of most events was found to be less than 29 km, and the magnitude interval ranges were between 0.3 ≤ ML ≤ 5.4, with RMS less than or equal to 0.2. Seismic activities were concentrated southeast of Uludag Mountain (UM), in the
Kestel-Igdir area and along the Gemlik Fault (GF). In the study, we computed 10 focal mechanisms from temporary and permanents
networks. The predominant feature of the computed focal mechanisms is the relatively widespread near horizontal northwest-southeast
(NW–SE) T-axis orientation. These fault planes have been used to obtain the orientation and shape factor (R, magnitude stress ratio)
of the principal stress tensors (σ1, σ2, σ3). The resulting stress tensors reveal σ1 closer to the vertical (oriented NE–SW) and σ2, σ3 horizontal with R = 0.5. These results confirm that Bursa and its vicinity could be defined by an extensional regime showing a primarily normal
to oblique-slip motion character. It differs from what might be expected from the stress tensor inversion for the NAFZ. Different
fault patterns related to structural heterogeneity from the north to the south in the study area caused a change in the stress
regime from strike-slip to normal faulting. 相似文献
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Elcin Kentel Mustafa M. Aral 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(4):405-417
In risk assessment studies it is important to determine how uncertain and imprecise knowledge should be included into the
simulation and assessment models. Thus, proper evaluation of uncertainties has become a major concern in environmental and
health risk assessment studies. Previously, researchers have used probability theory, more commonly Monte Carlo analysis,
to incorporate uncertainty analysis in health risk assessment studies. However, in conducting probabilistic health risk assessment,
risk analyst often suffers from lack of data or the presence of imperfect or incomplete knowledge about the process modeled
and also the process parameters. Fuzzy set theory is a tool that has been used in propagating imperfect and incomplete information
in health risk assessment studies. Such analysis result in fuzzy risks which are associated with membership functions. Since
possibilistic health risk assessment studies are relatively new, standard procedures for decision-making about the acceptability
of the resulting fuzzy risk with respect to a crisp standard set by the regulatory agency are not fully established. In this
paper, we are providing a review of several available approaches which may be used in decision-making. These approaches involve
defuzzification techniques, the possibility and the necessity measures. In this study, we also propose a new measure, the
risk tolerance measure, which can be used in decision making. The risk tolerance measure provides an effective metric for evaluating the acceptability
of a fuzzy risk with respect to a crisp compliance criterion. Fuzzy risks with different membership functions are evaluated
with respect to a crisp compliance criterion by using the possibility, the necessity, and the risk tolerance measures and
the results are discussed comparatively. 相似文献
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Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates 总被引：1，自引：0，他引：1
Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results. 相似文献
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