دانلود ترجمه مقاله تخمین ضریب تبخیر تشتکی با رویکرد عصبی – ژنتیکی

فهرست مطالب:

چکیده
مقدمه
مواد و روش ها
نتایج
بحث
نتیجه گیری

بخشی از ترجمه:

در این مطالعه یک رویکرد عصبی ژنتیکی به طور موفق براورد مقادیر کافی برای موارد تشتک های کلاس A تحت شرایط سبز و تشتک کلاس A تحت  شرایط خشک ،تشتک کلرادوسانکن  تحت شرایط سبز و تشتک کلرادوسانکن تحت شرایط خشک به کار برده شد. انالیز قیاسی یا تفضیلی نشان داد که این نسبت به روش های قبلی که از معادلات Kp در براورد مقادی Kp استفاده می کند عملکرد بهتری دارد .

بخشی از مقاله انگلیسی:

INTRODUCTION Hydrological extreme events are typically defined as floods and droughts. Floods are associated with high rainfall and may cause dam break events (Tingsanchali and Chinnarasri, 2001) while drought is associated with lack of precipitation and high evaporation. For modeling purpose and decision making, it is therefore a need for a study to estimate hydrological data for areas with lack of data and different climatic conditions (Hosseini et al., 2011). Pan Evaporation (Ep) has become a widespread method for estimating reference Evapotranspiration (ETo) due to its simplicity, low cost, ease of data interpretation and application and suitability for locations with limited availability of meteorological data (Phene and Campbell, 1975; Trajkovic, 2009). To convert Ep to ETo, a pan coefficient (Kp) is necessary. Although the FAO-24 Kp Table 1 provides Kp values, several Kp equations have been developed for estimating Kp values (Cuenca, 1989; Snyder, 1992; Allen, 1998; Raghuwanshi and Wallender, 1998; Abdel-Wahed and Snyder, 2008). Those equations were developed for two types of pan, i.e., Class A and Colorado sunken pans and for two conditions, i.e., a pan placed in a short green cropped area and a pan placed in a dry fallow area. Several Kp equations have been suggested based on the FAO-24 Kp tables using linear, nonlinear and indicator regression techniques or combinations thereof. There are a few regression equations for predicting the Kp values for a FAO Class A pan placed in a short green cropped area based on the FAO-24 Kp Table (Frevert et al., 1983; Cuenca, 1989; Snyder, 1992; Raghuwanshi and Wallender, 1998). Cuenca (1989) modified Kp equation as proposed by Frevert et al. (1983) by rounding off the coefficients of the equation. Snyder (1992) used the representative values to represent the category data of wind run and relative humidity and applied a least-squares regression approach for predicting Kp values. To develop a Kp equation for Class A pan placed in short green cropped areas, Raghuwanshi and Wallender (1998) applied the indicator regression technique, which is a widely accepted approach for developing a relationship between categorical and quantitative data. Allen (1998) and Abdel-Wahed and Snyder (2008) proposed Kp equations for an FAO Class A pan placed in a dry fallow area. Allen (1998) also proposed two Kp equations for Colorado sunken pans surrounded by green and dry fetch conditions. All of the existing Kp equations are the function of the daily mean Relative Humidity (RH,%), daily mean wind speed at 2 m height (U2, km/day) and fetch distance (F, m). The main differences are the selected representative values of each range of wind run categories and relative humidity categories. The neuro-genetic approach, a hybrid of neural networks and genetic algorithms, is relatively new for predicting pan coefficient values. The main function of the neuro-genetic approach is still based on neural networks, while the genetic algorithm helps automatically search for the optimal structure of the network. Only two related research projects for the estimation of reference evapotranspiration factors were found in the literature. Trajkovic et al. (2000) applied a radial basis function network to estimate the FAO Blanney-Criddle factor. Trajkovic et al. (2001) estimated the FAO Penman factor using a radial basis function network. This sutdy presents the application of the neurogenetic approach to estimating pan evaporation coefficient values for Class A pans and Colorado Sunken pans under green and dry fetch conditions. The statistical performance comparison was undertaken between this neuro-genetic approach and the previous proposed equations. MATERIALS AND METHODS The neuro-genetic approach is a hybrid model between Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs).