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Neural 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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 Publication date 2015
and research's language is العربية
 Created by Shamra Editor




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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 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.


Artificial intelligence review:
Research summary
تتناول هذه الدراسة بناء نموذج رياضي باستخدام الشبكات العصبية الصنعية لتقدير التبخر الشهري في المنطقة السهلية من الساحل السوري، اعتماداً على درجة الحرارة الشهرية فقط. تم استخدام NN-tool box من MATLAB لبناء شبكة عصبية صنعية متعددة الطبقات بخوارزمية الانتشار العكسي للخطأ. أظهرت النتائج أن الشبكة العصبية ذات الهيكلية (1-9-1) تعطي أقل قيمة لمربع متوسط الخطأ لمجموعة التحقق، مما يشير إلى دقة النموذج في تقدير التبخر مقارنةً بنماذج أخرى مثل معادلة إيغانوف. تم استخدام تقانة Simulink لمقارنة نتائج النموذج مع نماذج أخرى، وأظهرت النتائج أن النموذج المقترح يعطي نتائج أكثر دقة في تقدير التبخر الشهري.
Critical review
دراسة نقدية: على الرغم من أن الدراسة تقدم نموذجاً مبتكراً ودقيقاً لتقدير التبخر الشهري باستخدام الشبكات العصبية الصنعية، إلا أن هناك بعض النقاط التي يمكن تحسينها. أولاً، الاعتماد على درجة الحرارة فقط كمدخل للنموذج قد يكون محدوداً، حيث أن التبخر يتأثر بعوامل مناخية أخرى مثل الرطوبة وسرعة الرياح. ثانياً، لم يتم التطرق إلى كيفية التعامل مع البيانات المفقودة أو غير المكتملة، وهي مشكلة شائعة في البيانات المناخية. ثالثاً، يمكن تحسين الدراسة بإجراء تجارب إضافية لاختبار النموذج في مناطق مناخية مختلفة لضمان عموميته وفعاليته. وأخيراً، كان من الممكن تقديم تحليل أعمق للنتائج ومقارنتها مع نماذج أخرى بشكل أكثر تفصيلاً.
Questions related to the research
  1. ما هو الهدف الرئيسي من الدراسة؟

    الهدف الرئيسي هو بناء نموذج رياضي لتقدير التبخر الشهري في المنطقة السهلية من الساحل السوري باستخدام الشبكات العصبية الصنعية اعتماداً على درجة الحرارة فقط.

  2. ما هي الأدوات البرمجية المستخدمة في بناء النموذج؟

    تم استخدام NN-tool box وSimulink من MATLAB لبناء النموذج وإجراء المحاكاة.

  3. ما هي الهيكلية الأفضل للشبكة العصبية الصنعية وفقاً للدراسة؟

    الهيكلية الأفضل هي (1-9-1) حيث تعطي أقل قيمة لمربع متوسط الخطأ لمجموعة التحقق.

  4. كيف تقارن نتائج النموذج المقترح مع معادلة إيغانوف؟

    أظهرت النتائج أن النموذج المقترح يعطي نتائج أكثر دقة في تقدير التبخر الشهري مقارنةً بمعادلة إيغانوف.


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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