Evaporation forms one of the hydrology cycle elements that it's hard to measure its actual amounts in the field conditions, so it’s estimated by calculations of experimental relations, which depend on climatic elements data. So the research goal is t
o build a mathematical model to estimate monthly evaporation amount in plain area of Syrian Coast, using Artificial Neural Network (ANN), and depending on dry air temperature, and produce comparison study between the results of network and other models. The mathematical model was built by the (NN-tool box), which is one of the v tools. A multilayer ANN architecture of error Back-propagation algorithm was built. The suitable training algorithms, number of hidden layers, number of neurons in each hidden layer, were determined.
The results showed that the ANN (1-9-1) was the best model with MSE of 0.0032 for validation group, using Transfer Function Logsigmoid and Linear in hidden and output layers, respectively.
A comparison model for the results obtained from the proposed ANN with EVANOV model by using SIMULINK technique was developed. This indicated that the ANN using temperature only gives results more accurate than EVANOV equation in determining evaporation.
Continuous calculations of evapotranspiration (LE) using eddy covariance
method and energy budget were performed over more than one year above the
heterogeneous canopy of an arid oasis ecosystem in the central Syrian desert.
Irrigation practice wa
s traditional flooding with a 28 days turn of water
delivery. The work focuses on seasonal variations of energy budget over a 2
years period with emphasis on effects of rainfall, wind speed and radiative
budget on evapotranspiration. Maximum evapotranspiration was only
5mm/day. Even under irrigation practices, winter rainfall seems to have an
important impact on LE: comparisons of two situations in June 2002 and June
2003 showed an increase of 13% in values of LE/Rn-G. Maximum averaged
hourly values of evapotranspiration were found for wind speed values closed to
3m/s. This suggests that when the evaporative demand from the air (or vapour
pressure deficit (vpd) ) is increasing above a certain limit, the vegetation closes
its stomata and reduces transpiration. Results from the energy balance closure
showed significant differences in the slope of H+LE against Rn-G relationships
between cold and hot month which was explained by specific radiative budget
of desert areas.
The evaporation is one of the basic components of the hydrologic cycle and it is
essential for studies such as water balance, irrigation system design and water resource
management, and it requires knowledge of many climatic variables. Although, th
ere are
many empirical formulas available for evaporation estimate, but their performances are not
all satisfactory due to the complicated nature of the evaporation process. Accordingly, this
paper is an attempt to assess the potential and usefulness of ANN based modeling for
evaporation prediction from HAMA by using temperature, relative humidity and wind
velocity. The mathematical model was built by the (nntool-box), which is one of the
MATLAB tools. The feed forward back propagation network with one hidden layer has
been utilised to construct the model. Different networks with different number of neurons
were evaluated. Root Mean Squared Error (RMSE) was employed to evaluate the accuracy
of the proposed model. The study shows that ANN (3-14-1) was the best model with
RMSE (21.5mm/month) and R2 (0.97).
This study suggests using other types of neural networks for estimation of
evaporation
Accurate estimating and predicting of hydrological phenomena plays an influential role in the development and management of water resources, preparing of future plans according to different scenarios of climate changes. Evapotranspiration is one of t
he major meteorological components of the hydrologic cycle and from the most complex of them, and the accurate prediction of this parameter is very important for many water resources applications.
So, this research goals to prediction of monthly reference evapotranspiration (ET0) at Homs meteostation, in the middle of Syrian Arab Republic, using Artificial Neural Networks (ANNs), and Fuzzy Inference System (FIS), depending on available climatic data, and comparision between the results of these models.
The used data contained 347 monthly values of Air Temperature (T), Relative Humidity (RH), Wind Speed (WS) and Sunshine Hours (SS) (from October 1974 to December 2004). The monthly reference evapotranspiration data were estimated by the Penman Monteith method, which is the proposed method by Food and Agriculture Organization of the United Nations (FAO) as the standard method for the estimation of ET0, and used as outputs of the models.
The results of this study showed that feed forward back propagation Artificial Neural Networks (FFBP-ANNs) pridected successfully the monthly ET0 using climatic data, with low values of root mean square errors (RMSE), and high values of correlation coefficients (R), and showed that the using of the monthly index as an additional input, improves the accurate of prediction of the artificial neural networks models.
Also, the results showed good ability of Fuzzy Inference Models (FIS) in predicting of monthly reference evapotranspiration. Sunshine hours are the most influential single parameter for ET0 prediction (R= 97.71%, RMSE = 18.08 mm/month) during the test period, sunshine hours and wind speed are the most influential optimal combination of two parameters
(R= 98.55%, RMSE = 12.49 mm/month) during the test period.
The results showed high reliability for each of the artificial neural networks and fuzzy inference system with a little preference for artificial neural networks which can add the monthly index in the input layer, and there for improve the presicion of predictions.
This study recommends the using of artificial intelligence techniques in modeling of complex and nonlinear phenomena which related of water resources.
