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Register a new userNeural Network Model for Evaporation Prediction in Plain Area of Syrian Coastal Region Depending on Monthly Temperature
أنموذج شبكة عصبية صنعية للتنبؤ بالتبخر الشهري في المنطقة السهلية من الساحل السوري اعتماداً على درجة الحرارة الشهرية
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يشكّل التبخر أحد عناصر الدورة الهيدرولوجية، الذي يصعب قياس كمياته الفعلية في الشروط الحقلية، لذلك يجري تقديره اعتماداً على الحسابات بعلاقات تجريبية تعتمد على بيانات عناصر المناخ. يهدف البحث إلى بناء أنموذج رياضي لتقدير التبخر الشهري في المنطقة السهلية من الساحل السوري، و ذلك باستخدام الشبكات العصبية الصنعيَّة اعتماداً على درجة الحرارة فقط. و إجراء دراسة مقارنة بين نتائج أنموذج الشبكة و نتائج نماذج أخرى معروفة. بُني الأنموذج الرياضي باستخدام NN-tool box إحدى أدوات MATLAB حيث شكلت شبكة عصبية صنعيَّة متعددة الطبقات لخوارزمية الانتشار العكسي للخطأ، و حُددت خوارزمية التعلم الملائمة، و عدد الطبقات الخفية المستخدمة، بالإضافةً إلى عدد العصبونات و نوع دوال التفعيل المستخدمة في كل طبقة. و قد أظهرت النتائج أن الشبكة العصبية الصنعيَّة ذات الهيكلية (1-9-1) تعطي أقل قيمة لمربع متوسط الخطأ لمجموعة التحقق و يساوي 0.0032، مع استخدام دالتي التفعيل Logsigmoid و Linear على الترتيب في الطبقة الخفية و طبقة الإخراج. كما طُوِّر أنموذج المحاكاة للنتائج المستحصلة من الشبكة العصبية الصنعيَّة المقترحة مع نماذج أخرى مثل معادلة إيفانوف و ذلك باستخدام تقانة (Simulink). تبين أن الشبكة العصبية الصنعيَّة المعتمدة على درجة الحرارة فقط تعطي نتائج أكثر دقة من معادلة إيفانوف في تقدير التبخر.
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 to 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.
References used
SUDHEER, M.E. et, al. Estimating actual evapotranspiration from limited climatic data using neural computing technique. J. Irri. Drain. Engg. ASCE. 129(3), 2003, 214-218
KESKIN, K.P. TERZI, O. Artificial Neural Network Models of Daily Pan Evaporation. J. Hydrologic Engrg. 11(1), 2006, 65-70
MOGHADDAMNIA, A. et, al. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Science Direct U. S. A.Vol.32, 2009, 88-97
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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
This study includes the possibility of using Artificial neural
networks (ANNs) with back-propagation algorithm in a short-term
prediction of water level in Qattinah Lake. The data used are the
water level data in the lake and rainfall data for the period from
1/5/2007 to 28/2/2005. 2009).
This study is aiming at building a mathematical model to estimate evaporation from Mountainous region in Syrian Coast, using an artificial neural network, based on four metrological parameters (i.e. temperature, relative humidity, wind speed and sun
hours), then studying the effect of adding time variable on evaporation estimation. The mathematical model was built by the (NN-tool box), which is one of the MATLAB tools, using the daily value of the above mentioned parameters in addition to time, as the network inputs and the evaporation measured from the American pan class A as the network output . The results show that ANN4+T model which have 5 inputs (temperature, relative humidity, wind speed, sun hours, time) is the best in estimation evaporation with correlation factor of 0.8919 and Mean square error of 0.02166 for the validation set where the correlation factor in ANN4 (without time) was 0.8324 and MSE of 0.0327for the validation set.
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.
The study and design of water-intakes on springs is based on the analysis of time series of
historical measurements to achieve prediction of incoming water volumes or future
expected.
The research aims to model the monthly water flows of AL-SIN Sp
ring in Syrian Coast
and future expectations of these flows, by adopting the Box-Jenkins models to analyze the
time series data, due to its reliable accuracy. Monthly water flows, thus, monthly volumes,
for 101 month (from June 2008 to October 2016) were processed. Performing the stability
of the time series on variance and median and non-seasonality and making the wanted tests
on model residuals, we found that the best model to represent the data is SARIMA(2,0,1)
(2,1,0)12 , and after dividing the data into 81 month to build the model and 20 month to test
it. Depending on the smallest of weighted mean of criteria RMSE, MAP, MAE,. The best
predicted model was SARIMA (3,1,0) (1,1,0)12 and the model gave the nearest predicted
values to actually measured data in spring.
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