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Prediction of Monthly Pan Evaporation in Homs Meteostation Using Artificial Neural Networks

التنبّؤ بالتبخّر الإنائي الشّهري في محطّة حمص المناخيّة باستخدام الشبكات العصبيّة الاصطناعيّة

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 Publication date 2016
and research's language is العربية
 Created by Shamra Editor




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Evaporation is a major meteorological component of the hydrologic cycle, and it plays an influential role in the development and management of water resources. The aim of this study is to predict of the monthly pan evaporation in Homs meteostation using Artificial Neural Networks (ANNs), which based on monthly air temperature and relative humidity data only as inputs, and monthly pan evaporation as output of the network. The network was trained and verified using a back-propagation algorithm with different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. Results shown good ability of (2-10-1) ANN to predict of monthly pan evaporation with total correlation coefficient equals 96.786 % and root mean square error equals 24.52 mm/month for the total data set. This study recommends using the artificial neural networks approach to identify the most effective parameters to predict evaporation.


Artificial intelligence review:
Research summary
تتناول هذه الدراسة التنبؤ بالتبخر الإنائي الشهري في محطة حمص المناخية باستخدام الشبكات العصبية الاصطناعية. يعتبر التبخر مكونًا أساسيًا في الدورة الهيدرولوجية وله دور كبير في إدارة الموارد المائية. استخدمت الدراسة القيم الشهرية لدرجة حرارة الهواء والرطوبة النسبية كمدخلات، والتبخر الإنائي الشهري كمخرج للشبكة العصبية. تم تدريب الشبكة باستخدام خوارزمية الانتشار العكسي مع تغيير طرق التدريب وعدد الطبقات الخفية وعدد العصبونات في كل طبقة. أظهرت النتائج أن الشبكة العصبية ذات الهيكلية 1-10-2 قادرة على التنبؤ بقيم التبخر الإنائي الشهري بمعامل ارتباط كلي 96.786% وجذر متوسط مربعات الأخطاء 24.52 ملم/شهر. توصي الدراسة باستخدام الشبكات العصبية الاصطناعية لتحديد العوامل الأكثر تأثيرًا على التبخر.
Critical review
دراسة نقدية: تعتبر هذه الدراسة خطوة مهمة نحو تحسين دقة التنبؤ بالتبخر الإنائي باستخدام الشبكات العصبية الاصطناعية. ومع ذلك، يمكن توجيه بعض النقد البناء لتحسين العمل المستقبلي. أولاً، قد يكون من المفيد تضمين المزيد من العوامل المناخية مثل سرعة الرياح والإشعاع الشمسي لتحسين دقة النموذج. ثانيًا، يمكن توسيع الدراسة لتشمل محطات مناخية أخرى في سوريا أو حتى في مناطق جغرافية مختلفة للحصول على نتائج أكثر شمولية. أخيرًا، يمكن مقارنة أداء الشبكات العصبية مع نماذج أخرى مثل نماذج التعلم الآلي التقليدية أو النماذج الفيزيائية لتحسين فهم أداء الشبكات العصبية في هذا السياق.
Questions related to the research
  1. ما هي المدخلات والمخرجات المستخدمة في الشبكة العصبية الاصطناعية في هذه الدراسة؟

    استخدمت الدراسة القيم الشهرية لدرجة حرارة الهواء والرطوبة النسبية كمدخلات، والتبخر الإنائي الشهري كمخرج للشبكة العصبية.

  2. ما هي خوارزمية التدريب المستخدمة في هذه الدراسة؟

    استخدمت الدراسة خوارزمية الانتشار العكسي لتدريب الشبكة العصبية الاصطناعية.

  3. ما هو معامل الارتباط الكلي الذي حققته الشبكة العصبية الاصطناعية في التنبؤ بالتبخر الإنائي الشهري؟

    حققت الشبكة العصبية الاصطناعية معامل ارتباط كلي بلغ 96.786%.

  4. ما هي التوصيات التي قدمتها الدراسة لتحسين دقة التنبؤ بالتبخر الإنائي؟

    أوصت الدراسة باستخدام تقنية الشبكات العصبية الاصطناعية لتحديد العوامل الأكثر تأثيرًا على التبخر لتحسين دقة التنبؤ.


References used
ESLAMIAN, S. S; GOHARI, S. A; BIABANKI, M; MALEKIAN, R; Estimation of Monthly Pan Evaporation Using Artificial Neural Networks and Support Vector Machines. Journal of Applied Sciences 8 ,19, 2008, 3497-3502
BOROOMAND-NASAB, B; JOORABIAN, M. Estimating Monthly Evaporation Using Artificial Neural Networks. Journal of Environmental Science and Engineering, 5, 2011, 88-91
KUMAR, P; TIWARI, A. K. Evaporation Estimation Using Artificial Neural Network. International Journal of Computer Theory and Engineering, Vol. 4, No. 1, 2012
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