أنموذج شبكة عصبية اصطناعية للعلاقة بين الهطل المطري والجريان النهري حالة دراسة حوض نهر الأبرش
Abstract
تُعتبر العلاقة بين الهطل المطري و الجريان النهري (R_R) Rainfall_Runoffمن أكثر الظواهر الهيدرولوجية تعقيداً بسبب طبيعتها غير الخطية، نظراً للتباين المكاني والزمني الكبير لخصائص الأحواض النهرية وأنماط هطول الأمطار، كما تلعب دوراً هاماً في التنبؤ بالأحداث المتطرفة (فيضانات وجفاف)، وتُسهم في تحقيق الإدارة الجيدة لمشاريع تنمية الموارد المائيّة. تهدف هذه الدراسة إلى نمذجة العلاقة بين الهطل المطري_الجريان النهري في حوض نهر الأبرش باستخدام تقانة الشبكة العصبيّة الاصطناعيّة Artificial Neural Network (ANN)، والاعتماد على البيانات اليومية للهطل المطري، التبخر، منسوب المياه في بحيرة سد الباسل، بالإضافة إلى بيانات الجريان النهري السابق للأشهر الماطرة الممتدة بين عامي (2013-2009) باستخدام برنامج Matlab. وقد أظهرت النتائج أن الشبكة العصبية الاصطناعية ذات الهيكلية (11-10-1) أعطت أفضل أداء بمعامل ارتباط 98.15%=R، وجذر متوسط مربّع الخطأ =1.3721 m3/s RMSE خلال مرحلة الاختبار, أثبتت الدراسة أنّ تقانة الشبكة العصبية الاصطناعية تقدّم نتائج جيدة في نمذجة العلاقة R_R لمنطقة البحث.
Rainfall _ Runoff relationship (R-R) is one of the most complex hydrological phenomena because of its nonlinear nature, due to the large spatial and temporal variability of the watershed characteristics and rainfall patterns, It also plays an important role in predicting the extreme events (floods and droughts), and it contributes to a good management for water resources development projects. This study aims at modeling the relationship between rainfall and runoff in Al-Abrash catchment using Artificial Neural Networks technology (ANN), and depending on the daily data of rainfall, evaporation, water level in Al-Bassil lake, as well as data of the previous runoff for the rainy months between (2009-2013) using Matlab program. The results showed that ANN (11-10-1) gave the best performance with a correlation coefficient equals 98.15%, and a root mean square error equals 1.3721 m3/s for testing data set, The study proved that artificial neural network technology offers good results in modeling the Rainfall_ Runoff relationship for research area.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 �ttps://creativecommons.org/licenses/by-nc-sa/4.0/

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series
Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series . The Readers have the right to send, print and subscribe to the initial version of the article, and the title of Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series Publisher
journal uses a CC BY-NC-SA license which mean
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- The licensor cannot revoke these freedoms as long as you follow the license terms.
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.