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Operation analysis of Eleviyan irrigation reservoir dam by optimization and stochastic simulation 总被引：1，自引：0，他引：1
M. Taghi Sattari Halit Apaydin Fazli Ozturk 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(8):1187-1201
Rainfall distributions in Iran are spatially and temporally heterogeneous, a fact probably linked to the mostly arid and semi-arid
climate of the country. On the other hand, water demand is increasing with increasing population and improving life style.
At present, the optimal utilization of water resources and irrigation dams is the primary concern of water resource managers.
The Eleviyan dam (with a capacity of 60 hm3) was constructed to meet the irrigation and municipal water needs of the Maraghan region (Northwestern Iran). In this study,
the efficiency of the Eleviyan irrigation dam system was investigated in three phases by setting up the optimization model
that maximized the water release for irrigation purposes after municipal water need were met. In the first phase, the inflows
measured in the 21 years prior to the construction of the reservoir, and in the second, the inflows generated by the Monte
Carlo simulation method, and in the third phase, the inflows after the construction of the reservoir were used. The results
demonstrate that the capacity determined during the preliminary studies was accurate and the operation carried out in the
recent periods of operation life was up to a satisfactory standard. 相似文献
2.
Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model 
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下载免费PDF全文 Mohammad Taghi Sattari Rasoul Mirabbasi Reza Shamsi Sushab John Abraham 《Ground water》2018,56(4):636-646
The Ardebil plain, which is located in northwest Iran, has been faced with a recent and severe decline in groundwater level caused by a decrease of precipitation, successive long‐term droughts, and overexploitation of groundwater for irrigating the farmlands. Predictions of groundwater levels can help planners to deal with persistent water deficiencies. In this study, the support vector regression (SVR) and M5 decision tree models were used to predict the groundwater level in Ardebil plain. The monthly groundwater level data from 24 piezometers for a 17‐year period (1997 to 2013) were used for training and test of models. The model inputs included the groundwater levels of previous months, the volume of entering precipitation into every cell, and the discharge of wells. The model output was the groundwater level in the current month. In order to evaluate the performance of models, the correlation coefficient (R) and the root‐mean‐square error criteria were used. The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain. However, the results obtained from the M5 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR. The highest accuracy was obtained using the SVR model to predict the groundwater level from the Ghareh Hasanloo and Khalifeloo piezometers with R = 0.996 and R = 0.983, respectively. 相似文献
3.
Shamloo Nazila Sattari Mohammad Taghi Apaydin Halit 《Theoretical and Applied Climatology》2022,149(1-2):39-51
Theoretical and Applied Climatology - Drought is a natural, global and recurring phenomenon caused by climatic anomalies and inevitable meteorological changes. Lake Urmia in northwestern Iran has... 相似文献
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In this study, daily rainfall–runoff relationships for Sohu Stream were modelled using an artificial neural network (ANN)
method by including the feed-forward back-propagation method. The ANN part was divided into two stages. During the first stage,
current flows were estimated by using previously measured flow data. The best network architecture was found to utilise two
neurons in the input layer (the delayed flows from the first and second days), two hidden layers, and one output layer (the
current flow). The coefficient of determination (R
2) in this architecture was 81.4%. During the second stage, the current flows were estimated by using a combination of previously
measured values for precipitation, temperature, and flows. The best architecture consisted of an input layer of 2 days of
delayed precipitation, 3 days of delayed flows, and temperature of the current. The R
2 in this architecture was calculated to be 85.5%. The results of the second stage best reflected the real-world situation
because they accounted for more input variables. In all models, the variables with the highest R
2 ranked as the previous flow (81.4%), previous precipitation (21.7%), and temperature. 相似文献
5.
This study investigate the potential of M5 model tree in predicting daily stream flows in Sohu river located within the municipal borders of Ankara, Turkey. The results of the M5 model tree was compared with support vector machines. Both modelling approaches were used to forecast up to 7-day ahead stream flow. A comparison of correlation coefficient and root mean square value indicates that M5 model tree approach works equally well to the SVM for same day discharge prediction. The M5 model tree also works well up to 7-day ahead discharge forecasting in comparison of SVM with this data set. An advantage of using M5 model tree approach is the availability of simple linear models to predict the discharge as well use of less computational time. 相似文献
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Natural Hazards - The Urmia Lake basin is one of the most important basins in Iran, facing many problems due to poor water management and rainfall reduction. Under current circumstances, it becomes... 相似文献
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S. Mehdi Saghebian M. Taghi Sattari Rasoul Mirabbasi Mahesh Pal 《Arabian Journal of Geosciences》2014,7(11):4767-4777
A decision tree-based approach is proposed to predict ground water quality based on the United States Salinity Laboratory (USSL) diagram using the data from aquifers in agricultural lands of Ardebil province, northwest of Iran. Several combinations of hydro chemical parameters of groundwater and monthly precipitation with different lag time were considered to find an accurate and economical alternative for groundwater quality classification. The performance evaluation was based on the number of correctly classified instances (CCI) and kappa statistics. The results suggested the suitability of decision tree-based classification approach for the used data sets. The overall average of CCI and kappa statistic for the prediction of groundwater quality classes based on the USSL diagram was 0.88 and 0.83 %, respectively. Principal component analysis (PCA) was also used to determine the important parameters for groundwater quality classification. The results showed that groundwater quality classification by decision tree is more precise and efficient in comparison with PCA. The best alternative could evaluate groundwater quality class with only two parameters: electrical conductivity and cumulative precipitation of 11 months earlier. The developed model is able to predict water quality class by only two variables and this lead to a reduction in the number of variables analyzed on a routine basis, resulting in a significant reduction in laboratory costs and latency times between the sampling moment and the outcome of the laboratory analyses. 相似文献
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