Afshin Honarbakhsh; Seyed Javad Sadatinejad; Moslem Heydari; Mohamadreza Mozdianfard
Volume 9, Issue 1 , October 2011
Abstract
Lag time is a parameter that appears often in theoretical and conceptual models associated with river basin. The river basin lag time is an important factor in linear modeling of river basin response. Generally, all hydrologic analyses require at least one of the time parameters of river basin and, in ...
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Lag time is a parameter that appears often in theoretical and conceptual models associated with river basin. The river basin lag time is an important factor in linear modeling of river basin response. Generally, all hydrologic analyses require at least one of the time parameters of river basin and, in the majority of cases, time of concentration or lag time are used. In this research, storm data from 6 stations in the North Karoon river basin (in Iran) were analyzed. From this analysis, 23 events were selected. Then, in one experimental sub-basin located in this river basin, the lag time was calculated using field method. In this method, performed in the Darehbeed-Samsami study area, lag time was computed from a hydrograph generated by discharge measurement of a triangular scaled spillway. After that, 23 events were divided into two groups, including, one for a newly developed empirical model (70 percent) and another for validation of this model (30 percent). The results obtained from this research based on coefficient of determination (R2), root mean square error (RMSE) and relative error (%RE) statistical measures showed that the agreement between the computed(from new empirical model) and measured data is good.
Seyed Javad Sadatinejad,; Somayeh Angabini,; Mohammad Reza Mozdian fard
Volume 8, Issue 2 , January 2011
Abstract
Exact estimation of evapotranspiration is an important parameter in water cycle, study, design and management of irrigation systems. In this study, the efficiency of intelligent models such as fuzzy rule base, fuzzy regression and Artificial Neural Networks for estimating daily evapotranspiration has ...
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Exact estimation of evapotranspiration is an important parameter in water cycle, study, design and management of irrigation systems. In this study, the efficiency of intelligent models such as fuzzy rule base, fuzzy regression and Artificial Neural Networks for estimating daily evapotranspiration has been examined and the results are compared to real data measured by lysimeter on the basis of a grass reference crop. Using daily climatic data from Ekbatan station in Hamadan in western Iran, including maximum and minimum temperatures, maximum and minimum relative humidities, wind speed and sunny hours, evapotranspiration was estimated by the aforementioned intelligent models. The predicted evapotranspiration values from fuzzy rule base, fuzzy linear regression and artificial neural network provided root mean square error (RMSE) of 0.72, 0.86 and 0.74 mm/day and determination coefficient (R2) of 0.88, 0.86 and 0.84, respectively. The fuzzy rule base was hence found to be the most appropriate method employed for estimating evapotranspiration.
