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Using Neural Networks models with Wavelet transform technology To Predict Flows Coming into 16 Tishreen Lake

استخدام الشبكات العصبية الصنعية مع تقنية التحويل المويجي للتنبؤ بالتدفق اليومي الوارد إلى بحيرة سد 16 تشرين

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 Publication date 2017
and research's language is العربية
 Created by Shamra Editor




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The evaluation of surface water resources is a necessary input to solving water management problems, which includes finding a relationship between precipitation and runoff, and this relationship is a high degree of complexity. The rain of the most important factors that greatly effect on rivers discharge, and process to prediction of these flows must take this factor into account, and much of the attention and study, artificial neural networks and is considered one of the most modern methods in terms of accuracy results in linking these multiple factors and highly complex. In order to predict the runoff contained daily to Lake Dam Tishreen 16 in Latakia, the subject of our research, the application of different models of artificial neural networks (ANN), was the previous input flows and rain. Divided the data set for the period between (2006-2012) into two sets: training and test, has been processing the data before using them as inputs to the neural network using Discrete Wavelet Transform technique, to get rid of the maximum values and the values of zero, where t the analysis of time series at three levels of accuracy before they are used sub- series resulting as inputs to the Feed Forward ANN that depend back-propagation algorithm for training. The results indicated that with the structural neural network (1-2-6) Wavelet-ANN model, are the best in the representation of the characteristics studied and best able to predict runoff daily contained to Lake Dam Tishreen 16 for a day in advance, where he reached the correlation coefficient the root of the mean of squared-errors (R2 = 0.96, RMSE = 1.97m3 / sec), respectively.


Artificial intelligence review:
Research summary
تتناول هذه الدراسة استخدام الشبكات العصبية الاصطناعية وتقنية التحويل المويجي للتنبؤ بالتدفق اليومي الوارد إلى بحيرة سد 16 تشرين في اللاذقية، سوريا. تم تقسيم البيانات للفترة من 2006 إلى 2012 إلى مجموعتين: تدريب واختبار. استخدمت تقنية التحويل المويجي المتقطع لمعالجة البيانات قبل إدخالها إلى الشبكة العصبية، وذلك للتخلص من مشاكل القيم العظمى والصغرى. تم تحليل السلاسل الزمنية إلى ثلاثة مستويات من الدقة واستخدام السلاسل الفرعية الناتجة كمدخلات للشبكة العصبية أمامية التغذية التي تعتمد على خوارزمية الانتشار العكسي لتدريبها. أظهرت النتائج أن النموذج Wavelet-ANN ذو الهيكلية (1-2-6) هو الأفضل في تمثيل الظاهرة المدروسة والتنبؤ بالجريان اليومي الوارد إلى بحيرة سد 16 تشرين ليوم واحد قادم، حيث بلغ معامل الارتباط 0.96 وجذر مربع متوسط الخطأ 1.97 م³/ثانية. تشير الدراسة إلى أن استخدام الشبكات العصبية الاصطناعية مع تقنية التحويل المويجي يمكن أن يحسن من دقة التنبؤ بالتدفقات اليومية، مما يسهم في تحسين إدارة الموارد المائية والتخطيط لمواجهة الفيضانات والجفاف.
Critical review
تعتبر هذه الدراسة خطوة مهمة في مجال استخدام التقنيات الحديثة لتحسين دقة التنبؤ بالتدفقات المائية، إلا أن هناك بعض النقاط التي يمكن تحسينها. أولاً، قد يكون من المفيد توسيع فترة البيانات المستخدمة لتشمل سنوات إضافية، مما قد يعزز من دقة النموذج. ثانياً، يمكن استكشاف تأثير عوامل إضافية مثل التبخر ورطوبة التربة على دقة التنبؤ. ثالثاً، يمكن مقارنة أداء النموذج المقترح مع نماذج أخرى مثل الشبكات العصبية العميقة أو نماذج التعلم الآلي الأخرى لمعرفة مدى تفوق النموذج الحالي. وأخيراً، قد يكون من المفيد تطبيق النموذج على مناطق أخرى ذات خصائص هيدرولوجية مختلفة للتحقق من عمومية النتائج.
Questions related to the research
  1. ما هي الفترة الزمنية التي تم استخدام بياناتها في هذه الدراسة؟

    تم استخدام بيانات الفترة من عام 2006 إلى عام 2012 في هذه الدراسة.

  2. ما هي التقنية المستخدمة لمعالجة البيانات قبل إدخالها إلى الشبكة العصبية؟

    تم استخدام تقنية التحويل المويجي المتقطع لمعالجة البيانات قبل إدخالها إلى الشبكة العصبية.

  3. ما هو الهيكل الأفضل للنموذج وفقاً للدراسة؟

    النموذج Wavelet-ANN ذو الهيكلية (1-2-6) هو الأفضل وفقاً للدراسة.

  4. ما هي القيم التي تم تحقيقها لمعامل الارتباط وجذر مربع متوسط الخطأ؟

    بلغ معامل الارتباط 0.96 وجذر مربع متوسط الخطأ 1.97 م³/ثانية.


References used
ADAMOWSKI, J, F. River flow forecasting using wavelet and cross-wavelet transform models. Hydrological Processes, 22, 2008, 4877-4891
BOX, G, E, P; JENKINS, G. M .Time Series Analysis: Forecasting and Control. Holden Day Inc; San Francisco, 1976
JAIN, A; SRINIVASULU, S. Development of Effective and Efficient Rainfall- Runoff Models Using Integration of Deterministic, Real-Coded Genetic Algorithms and Artificial Neural Network Techniques. Water Resources Research, Vol. 40, No. 4, 2004, Article ID: W04302. doi:10.1029/2003WR002355
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