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21.
On the relationship of regional meteorological drought with SOI and NAO over southwest Iran 总被引：2，自引：1，他引：1
Amin K. Dezfuli Mohammad Karamouz Shahab Araghinejad 《Theoretical and Applied Climatology》2010,100(1-2):57-66
A fuzzy hierarchical clustering technique using the pairwise similarity matrix is employed to find the homogenous climate subregions over southwest Iran, based on the similarity of meteorological drought characteristics (i.e., duration, intensity, onset, and ending dates). The representative subregions are recognized for different rainy seasons; for each, the regional rainfall anomalies are computed. To find appropriate drought predictors, the lag relationships of regional rainfall with seasonal Southern Oscillation Index (SOI) and North Atlantic Oscillation (NAO) are examined using a conditional probability approach. The results suggest a significant negative correlation between autumn rainfall and June–August SOI. The NAO is also negatively correlated with autumn rainfall such that it is least likely for an extreme autumn drought to occur when June–August NAO is negative. A spring drought is preceded by an October–December NAO greater than 0.5. However, winter droughts do not appear to be lag-correlated with either SOI or NAO. In addition to the findings for droughts, these indices also emerged having considerable influence on wet seasons. A wet autumn tends to occur when either May–July SOI is less than ?0.5 or June–August NAO is less than about ?0.3. It is also apparent that the extreme wet springs are absent when October–December NAO is positive. This season is influenced most by NAO in both dry and wet spells. However, similar to droughts, the wet winter seasons are not found to be associated with either SOI or NAO. 相似文献
22.
A temporal artificial neural network‐based model is developed and applied for long‐lead rainfall forecasting. Tapped delay lines and recurrent connections are two different components that are used along with a static multilayer perceptron network to design a time‐delay recurrent neural network. The proposed model is, in fact, a combination of time‐delay and recurrent neural networks. The model is applied in three case studies of the Northwest, West, and Southwest basins of Iran. In addition, an autoregressive moving average with exogenous inputs (ARMAX) model is used as a baseline in order to be compared with the time‐delay recurrent neural networks developed in this study. Large‐scale climate signals, such as sea‐level pressure, that affect the rainfall of the study area are used as the predictors in the models, as well as the persistence between rainfall data. The results of winter‐spring rainfall forecasts are discussed thoroughly. It is demonstrated that in all cases the proposed neural network results in better forecasts in comparison with the statistical ARMAX model. Moreover, it is found that in two of three case studies the time‐delay recurrent neural networks perform better than either recurrent or time‐delay neural networks. The results demonstrate that the proposed method can significantly improve the long‐lead forecast by utilizing a non‐linear relationship between climatic predictors and rainfall in a region. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
23.
Evaluating the seismic active earth pressure on retaining walls is currently based on pseudo-static method in practices. In this method, however, it is not simple, choosing an appropriate value for earthquake coefficient, which should fully reflect the dynamic characteristics of both soil and loading is an important problem. On the other hand, by using only two extra dynamic parameters that are shear wave velocity of soil and predominant frequency of probable earthquake, one can benefit from another more accurate tool called pseudo-dynamic method to solve the problem of earth pressure.In this study in the framework of limit equilibrium analysis, pseudo-dynamic method has been applied into horizontal slice method of analysis to account for the effect of earthquake on lateral earth pressure history behind rigid retaining walls. The pressure history resulted from a number of analyses shows that before and after reaching the peak resultant force, different pressure distributions occur behind a wall that put more local pressure than the same at peak. This method would be a tool to control this phenomenon in wall design. 相似文献
24.
Sadra Shadkani Akram Abbaspour Saeed Samadianfard Sajjad Hashemi Amirhosein Mosavi Shahab S.Band 《国际泥沙研究》2021,36(4):512-523
Monitoring sediment transport is essential for managing and maintaining rivers.Estimation of the sediment load in rivers is fundamental for the study of sediment movement,erosion,and flood control.In the current study,three machine learning models-multi-layer perceptron(MLP),multi-layer perceptron-stochastic gradient descent(MLP-SGD),and gradient boosted tree(GBT)-were utilized to estimate the suspended sediment load(SSL)at the St.Louis(SL)and Chester(CH)stations on the Mississippi River,U.S.Four evaluation criteria including the Correlation Coefficient(CC),Nash Sutcliffe Efficiency(NSE),Scatter Index(SI),and Willmott’s Index(WI)were utilized to evaluate the performance of the used models.A sensitivity analysis of the models to the input variables revealed that the current day discharge variable had the most effect on the SSL at both stations,but in the absence of current-day discharge data(Qt),a combination of input parameters including SSLt-3,SSLt-2,SSLt-1,Qt-3,Qt-2,Qt-1 can be used to estimate the SSL.The comparative outcomes indicated the high accuracy of MLP-SGD-5 model with a CC of 0.983,SI of 0.254,WI of 0.991,and NSE of 0.967 at station CH and the MLP-SGD-6 model with a CC of 0.933,SI of 0.576,WI of 0.961,and NSE of 0.867,respectively,at station SL.The results of MLP models were improved by SGD optimization.Therefore,the MLP-SGD method is recommended as the most accurate model for SSL estimation. 相似文献