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Register a new userArtificial Neural Network Model to Predict Water Levels in Qattinah Lake
أنموذج شبكة عصبية صنعية للتنبؤ بمنسوب المياه في بحيرة قطينة
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تتضمن هذه الدراسة إمكانية استخدام الشبكات العصبية الاصطناعية مع خوارزمية الانتشار العكسي في التنبؤ قصير المدى بمناسيب بحيرة قطينة على نهر العاصي, مع الإشارة على أن البيانات المستخدمة هي بيانات مناسيب المياه في البحيرة و بيانات الأمطار للفترة الممتدة بين ( 1\5\2007 - 28\2\2009 ).
This study includes the possibility of using Artificial neural networks (ANNs) with back-propagation algorithm in a short-term prediction of water level in Qattinah Lake. The data used are the water level data in the lake and rainfall data for the period from 1/5/2007 to 28/2/2005. 2009).
Artificial intelligence review:
Research summary
تتناول هذه الدراسة إمكانية استخدام الشبكات العصبية الاصطناعية مع خوارزمية الانتشار العكسي للتنبؤ قصير المدى بمناسيب المياه في بحيرة قطينة على نهر العاصي. البيانات المستخدمة تشمل مناسيب المياه في البحيرة وبيانات الأمطار للفترة من 1 مايو 2007 حتى 28 فبراير 2009. أظهرت النتائج أن الشبكة العصبية الاصطناعية ذات الهيكلية (1–10–10–4) والمعايرة بخوارزمية الانتشار العكسي تعتبر تقنية فعالة للتنبؤ بتغيرات مناسيب المياه ليوم مقدماً، بمعامل ارتباط (0.997) وجذر متوسط مربعات الأخطاء (3.12 سم). توصي الدراسة باستخدام الشبكات العصبية الاصطناعية للتنبؤ بالتدفقات الواردة إلى البحيرة وحجوم المياه المخزنة فيها من أجل التنبؤ بالفيضانات القادمة على المدى القصير.
Critical review
دراسة نقدية: تعتبر هذه الدراسة خطوة مهمة نحو استخدام تقنيات الذكاء الاصطناعي في إدارة الموارد المائية، إلا أن هناك بعض النقاط التي يمكن تحسينها. أولاً، الدراسة تعتمد على بيانات محدودة زمنياً (من 2007 إلى 2009)، مما قد يؤثر على دقة التنبؤات في فترات زمنية مختلفة. ثانياً، لم يتم تناول تأثير التغيرات المناخية المستقبلية على دقة النموذج، وهو أمر بالغ الأهمية في ظل التغيرات المناخية الحالية. ثالثاً، كان من الممكن تحسين الدراسة بإجراء مقارنة مع نماذج تقليدية أخرى للتنبؤ بالمناسيب لتوضيح مدى تفوق الشبكات العصبية الاصطناعية بشكل أكثر وضوحاً.
Questions related to the research
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ما هي الفترة الزمنية التي تم استخدام بياناتها في هذه الدراسة؟
تم استخدام بيانات مناسيب المياه والأمطار للفترة من 1 مايو 2007 حتى 28 فبراير 2009.
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ما هي الهيكلية التي استخدمتها الشبكة العصبية الاصطناعية في الدراسة؟
استخدمت الشبكة العصبية الاصطناعية هيكلية (1–10–10–4) مع خوارزمية الانتشار العكسي.
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ما هو معامل الارتباط الذي حققته الشبكة العصبية الاصطناعية في التنبؤ بمناسيب المياه؟
حققت الشبكة العصبية الاصطناعية معامل ارتباط بلغ 0.997.
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ما هي التوصيات التي قدمتها الدراسة لاستخدام الشبكات العصبية الاصطناعية؟
توصي الدراسة باستخدام الشبكات العصبية الاصطناعية للتنبؤ بالتدفقات الواردة إلى البحيرة وحجوم المياه المخزنة فيها من أجل التنبؤ بالفيضانات القادمة على المدى القصير.
References used
Asce Task Committee on Application of Artificial Neural Networks in Hydrology, 2000 - Artificial Neural Networks in Hydrology. I: Preliminary concepts. J. Hydrol. Eng, 115-123
Asce Task Committee on Application of Artificial Neural Networks in Hydrology, 2000 - Artificial Neural Networks in Hydrology. II: Hydrologic applications. J. Hydrol. Eng, 124- 137
THIRUMALAIAH, K; DEO, M.C, 1998 - River Stage Forecasting Using Artificial Neural Networks. Journal of Hydrologic Engineering 3, PP: 26–31
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