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Geotectonics - Based on geological classification of Iran, Oligocene Lakhshak pluton is situated in the Sistan suture zone that composed of siliciclastic and ophiolitic melanges. The Lakhshak... 相似文献
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Azadeh Ahmadi Ali Moridi Elham Kakaei Lafdani Ghasem Kianpisheh 《Journal of Earth System Science》2014,123(7):1603-1618
Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July. 相似文献
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A feature weighting and selection method for improving the homogeneity of regions in regionalization of watersheds 
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下载免费PDF全文 The method used for feature selection or feature weighting in regionalization of watersheds may affect the results of regionalization methods considerably. It can play a key role in forming hydrologically homogeneous regions for regional flood frequency analysis. In this study, a method based on exploring the nearest and farthest neighbours of data points is introduced for identifying salient features for regionalization of watersheds. The method includes options to relate watershed features to flood data records in order to increase the homogeneity of the regions. The nearest and farthest neighbours are identified based on the criteria such as the mutual information criterion and Spearman's rank correlation coefficient. Then, the watershed features more able to explain the relationships between the nearest and farthest neighbours are identified as salient features to form homogeneous features for regional flood frequency analysis. The results show that the optimum option of the proposed method improves the performances of the hard and fuzzy clustering algorithms in more than half of the cases based on the cluster validity indices. Furthermore, the results reveal that the optimum option can increase the number of the homogeneous regions formed by clustering algorithms to a great extent. By using the optimum option with 5 nearest and 5 farthest neighbours, longitude, drainage area, and run‐off coefficient are identified as the salient features to regionalize Sefidrud basin. The results show that the proposed method can be considered as an efficient method to form homogeneous regions for regional flood frequency analysis. 相似文献
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In this study, an approach is presented for handling hydraulic uncertainties in the prediction of floodplain. Different factors affect river flood characteristics. Furthermore, the high changeability of flooding conditions leads to high variability of the inundation. River morphology is one of the most effective factors in river flood characteristics. This factor is influenced by sedimentation and erosion in the river cross sections, which affects the discharge variation. The depth and the width of the river cross section lead to an increase or decrease in the river flow path. This results in changes in the extent of the floodplain based on the generated rainfall. The inundated region boundaries are determined by utilizing the mean first‐order second‐moment analysis. The proposed method is applied to the Kajoo River in the south‐eastern part of Iran. Determination of floodplain uncertainty is a damage‐reduction policy in this region. Also, it is useful to prepare the necessary activities for overcoming the flood hazards. Climate change is the second effective factor on the floodplain uncertainties. Climate change affects the magnitude, extent and depth of inundation and it may intensify the flood problem. Therefore, the future rainfall pattern of the study area under climate change is simulated to evaluate its impacts on the river flow characteristic. Subsequently, a hydraulic routing model is used to determine floodplain. Finally, the copula function is used to estimate the joint probability of the changes in the inundation area due to changes in river morphology and the rainfall changes due to impacts of climate change. Results show that the uncertainties of the extent of floodplain are affected by climate change and river morphology, leading to noticeable changes in the magnitude and frequency of floods. Evaluating these impacts and estimating corresponding river discharges will help in the study of river dynamics, and will also contribute towards devising effective mitigation and management strategies. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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Natural Hazards - In this study, an algorithm inspired by some concepts of the rough set theory is proposed for regionalization of watersheds. The algorithm includes a clustering step and a... 相似文献
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M. Karamouz Ph.D. B. Zahraie Ph.D. R. Kerachian Ph.D. N. Mahjouri Ph.D. A. Moridi 《International Journal of Environmental Science and Technology》2006,3(3):229-242
Rapid industrial growth in the province of Khuzestan in the south west of Iran has resulted in disposal of about 1750 tons of solid waste per day. Most of these industrial solid wastes including hazardous wastes are disposed without considering environmental issues. This has contributed considerably to the pollution of the environment. This paper introduces a framework in which to develop a master plan for industrial solid waste management. There are usually different criteria for evaluating the existing solid waste pollution loads and how effective the management schemes are. A multiple criteria decision making technique, namely Analytical Hierarchy Process (AHP), is used for ranking the industrial units based on their share in solid waste related environmental pollution and determining the share of each unit in total solid waste pollution load. In this framework, a comprehensive set of direct, indirect, and supporting projects are proposed for solid waste pollution control. The proposed framework is applied for industrial solid waste management in the province of Khuzestan in Iran and a databank including GIS based maps of the study area is also developed. The results have shown that the industries located near the capital city of the province, Ahwaz, produce more than 32 percent of the total solid waste pollution load of the province. Application of the methodology also has shown that it can be effectively used for development of the master plan and management of industrial solid wastes. 相似文献
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Probabilistic reservoir operation using Bayesian stochastic model and support vector machine 总被引：3，自引：0，他引：3
Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation. 相似文献
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