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Estimation of Monthly Reference Evapotranspiration in Safita Area by using Artificial Neural Network

تقدير التّبخر- نتح المرجعي الشَّهري في منطقة صافيتا باستخدام الشَّبكة العصبيَّة الصنعيَّة

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 Publication date 2013
and research's language is العربية
 Created by Shamra Editor




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Evapotranspiration 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 that depend on climatic elements data. These estimations include different errors because of approximation processes. The research goals to accurate estimation of the monthly reference evapotranspiration amount in Safita area (on the east coast of the Mediterranean Sea), and the research depends on the technique of Artificial Neural Network (ANN), and the mathematical model was built by the (nftool), which is one of the Matlab tools, depending on monthly air temperature and relative humidity data which were taken from Safita meteorological station, and the data of monthly pan evaporation (Class A pan) has been used, after modifying its results, for the purpose of checking the performance accuracy of the network, by using Simulink technique, which is existing in Matlab Programs Package. The results of the research verify that a multi-layer ANN of error Back-propagation algorithm gives a good result in estimating monthly reference Evapo-transpiration for the used data group.


Artificial intelligence review:
Research summary
تتناول هذه الدراسة تقدير التبخر - نتح المرجعي الشهري في منطقة صافيتا باستخدام الشبكة العصبية الصنعية. يعتبر التبخر - نتح أحد عناصر الدورة الهيدرولوجية الذي يصعب قياسه بدقة في الظروف الحقلية، لذا يتم تقديره باستخدام علاقات تجريبية تعتمد على بيانات المناخ. تم بناء النموذج الرياضي باستخدام أداة Neural Fitting Tool في برنامج الماتلاب، معتمدين على بيانات درجة حرارة الهواء والرطوبة النسبية من محطة صافيتا المناخية. للتحقق من صحة أداء الشبكة، تم استخدام بيانات التبخر الشهري من حوض التبخر الأمريكي صنف A. أظهرت النتائج أن الشبكة العصبية الصنعية متعددة الطبقات وذات الانتشار العكسي للخطأ تعطي نتائج جيدة في تقدير التبخر الشهري. تم استخدام تقانة Simulink لتحويل النموذج إلى قالب جاهز. توصلت الدراسة إلى أن استخدام الشبكات العصبية الصنعية يمكن أن يكون بديلاً فعالاً ودقيقاً لتقدير التبخر - نتح المرجعي مقارنة بالطرق التقليدية مثل معادلة إيفانوف. كما أوصت الدراسة بإجراء قياسات حقلية إضافية واستخدام الشبكات العصبية لدراسة مسائل أخرى تتعلق بالموارد المائية في سوريا.
Critical review
دراسة نقدية: تعتبر هذه الدراسة خطوة مهمة نحو استخدام التقنيات الحديثة في تقدير التبخر - نتح المرجعي، إلا أن هناك بعض النقاط التي يمكن تحسينها. أولاً، الاعتماد على بيانات من محطة واحدة قد لا يكون كافياً لتعميم النتائج على مناطق أخرى، لذا يفضل توسيع نطاق الدراسة لتشمل محطات مناخية متعددة. ثانياً، رغم أن الشبكات العصبية الصنعية أثبتت دقتها في هذه الدراسة، إلا أن هناك حاجة لمقارنة نتائجها مع طرق أخرى حديثة مثل الخوارزميات الجينية أو نظام الاستدلال العصبي الضبابي التكيفي. ثالثاً، لم تذكر الدراسة تأثير العوامل البيئية الأخرى مثل سرعة الرياح والإشعاع الشمسي بشكل مفصل، والتي قد تكون لها تأثيرات كبيرة على التبخر - نتح. وأخيراً، يفضل إجراء دراسات طويلة الأمد للتحقق من استدامة ودقة النموذج عبر فصول ومواسم مختلفة.
Questions related to the research
  1. ما هو الهدف الرئيسي من هذه الدراسة؟

    الهدف الرئيسي هو تقدير التبخر - نتح المرجعي الشهري في منطقة صافيتا باستخدام الشبكة العصبية الصنعية.

  2. ما هي البيانات المستخدمة في بناء النموذج الرياضي؟

    تم استخدام البيانات الشهرية لدرجة حرارة الهواء والرطوبة النسبية من محطة صافيتا المناخية، بالإضافة إلى بيانات التبخر الشهري من حوض التبخر الأمريكي صنف A.

  3. ما هي النتائج الرئيسية التي توصلت إليها الدراسة؟

    أثبتت الدراسة أن الشبكة العصبية الصنعية متعددة الطبقات وذات الانتشار العكسي للخطأ تعطي نتائج جيدة في تقدير التبخر الشهري، وأنها أكثر دقة من معادلة إيفانوف.

  4. ما هي التوصيات التي قدمتها الدراسة؟

    أوصت الدراسة بإجراء قياسات حقلية إضافية للتبخر - نتح الحقيقي، وتوسيع استخدام الشبكات العصبية لدراسة مسائل متنوعة تتعلق بالموارد المائية في سوريا.


References used
DOORENBOS, J.; PRUITT, W.O. GuideLines for Predicting Crop Water Requirement. Food and Agriculture Organization of the United Nations (FAO).  N .24,1977,156
RAGHUWANSHI, N.S.; WALLENDER, W.W. Converting from pan Evaporation to Evapotranspiration. Journal of Irrigation and Drainage Engineering. Vol. 124, 1998, 275-277
FAO Corporate Document Repository. Crop Evapotranspiration. Natural Resources Management and environment Department, 2008
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