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Register a new userThe Effect Of Adding Time Factor On The Artificial Neural Network Performance In Estimating Daily Evaporation In Mountainous Region From Syrian Coast
تأثير إضافة معامل الزمن على أداء الشبكة العصبية الاصطناعية في تقدير التّبخر اليومي في المنطقة الجبلية من الساحل السوري
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تهدف هذه الدراسة إلى بناء أنموذج رياضي لتقدير التبخر من المنطقة الجبلية من الساحل السوري، باستخدام الشبكة العصبية الصنعيَّة و ذلك اعتماداً على أربعة بارمترات جوية، و هي درجة الحرارة، الرطوبة النسبية، سرعة الرياح و السطوع الشمسي، و من ثم دراسة تأثير إضافة معامل الزمن على تقدير التبخر. بني الأنموذج الرياضي باستخدام Neural Fitting Tool إحدى أدوات الماتلاب، و قد اعتمد على البيانات اليومية للبارامترات المذكورة في منطقة الدراسة بالإضافة إلى معامل الزمن، كما استُخدِمت بيانات التبخر اليومي المقيسة بوساطة حوض التبخر الأميركي صنف A كمخرجات مأمولة لغرض التحقق من صحة أداء الشبكة. و تظهر النتائج تفوق الشبكة المضاف لها معامل الزمن حيث بلغ معامل الارتباط فيها لمجموعة التحقق 0.8919 و متوسط مربع الخطأ 0.02166 بينما كانت قيمة معامل الارتباط للشبكة المستخدمة للتنبؤ بقيمة التبخر اعتماداً على المعطيات المناخية بدون إدخال معامل الزمن 0.8324 و متوسط مربع الخطأ 0.0327.
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.
References used
Jadeja, V, Artificial neural network estimation of Reference Evapotranspiration from pan evaporation in a semi-arid environment. National Conference on Recent Trends in Engineering & Technology, 13-14 May 2011
Kumar,P. et al, Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques , Pakistan Journal of Meteorology, Vol. 8, Issue 16: Jan 2012, 81-88
SAMMEN, S. Forecasting of evaporation from Hemeren reservoir by using artificial neural networks. College of Engineering, Diyala University, Iraq. 2012
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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
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