yaghoub dinpazhoh; sabereh darbandi
Abstract
Limited of water resources and the sustainable management, has made it impossible water supply of all needs, therefore, in order to supply water are required careful planning with high reliability. For this reason, is more than ever before the importance of water management in the catchment basin. In ...
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Limited of water resources and the sustainable management, has made it impossible water supply of all needs, therefore, in order to supply water are required careful planning with high reliability. For this reason, is more than ever before the importance of water management in the catchment basin. In this study used dynamics model (Vensim) and genetic programming to simulate the process of rainfall - runoff in Lighvan Watershd. The genetic programming model was tested to get the best combination of input model.The results show high accuracy Vensim model in the simulation of rainfall - runoff process than genetic programming.The results indicate that Vensim model for most events have a highly significant correlation of genetic programming, also is predicted peak flow and minimum absolute error.
M. Ghorbani Aghdam; yaghoub dinpazhoh; A. Fakheri Fard; S. Darbandi
Abstract
Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions, such as PCA are important tools in water resources planning. In this ...
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Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions, such as PCA are important tools in water resources planning. In this study factor analysis method as a tool for projecting the information space on the limited and specific axes, has been applied. The main aim of this study is regionalization of Urmia Lake basin from the view of drought using factor analysis. For this purpose monthly precipitation data of 30 weather stations in the period 1972-2009 were used. For each of the selected stations three and twelve months SPI value were calculated. Factor analysis conducted on SPI values to delineate the study area with respect to drought characteristics. Homogeneity of obtained regions tested using the S-statistics proposed by Wiltshire. Resultes of factor analysis of 3 (12) months SPI values showed that 5 (6) factors having eigen values greater than 1 acounted for 68.08 (78.88) percent of total variance. Urmia Lake basin delineated to 5 (6) distinct regions considering the eigenvectors following rotation using the 3 (12) month time scale. Results of homogeneity test indicated that all of the obtained regions were homogeneous.
B. Fallahi; A. Fakheri Fard; yaghoub dinpazhoh; S. Darbandi
Abstract
Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions, such as PCA are important tools in water resources planning. In this ...
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Having a correct view of the effective factors on climatic changes by explanation of a considerable part of the total variance in data with limited number of principal components the analytical methods of decreasing data dimensions, such as PCA are important tools in water resources planning. In this study PCA method as a projection tool for projecting the information space on the limited and specific axes, ward’s method as a hierarchical clustering and k-mean as partitioning clustering method has been applied in this research. Using this methods and application of daily precipitation data of 60 meteorological stations during a 35 years period (1970-2004), 4 types of delineated regions were come out on the basis of daily precipitations, distance-quantity index, time intervals and rainy days series. S statistic test algorithm was used for homogeneity test of the regions. Results showed the nature of the PCA method is such that projects the data space on the main axes and shows the real space. But in the hierarchical methods, clusters do not describe the real structure. Therefore we do expect that the resulting clusters of PCA would be more realistic than that of methods. But hierarchical methods have the advantage of containing the wider clustering information on the basis of homogeneity than the others.
