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عملکرد شبکۀ عصبی مصنوعی در پیش بینی و تحلیل فرایندهای هیدرولوژیک (مطالعۀ موردی: کمبود آب حوضۀ آبخیز نازلوچای در استان آذربایجان غربی) | ||
| اکوهیدرولوژی | ||
| مقاله 12، دوره 3، شماره 4، دی 1395، صفحه 631-644 اصل مقاله (1.34 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2016.60365 | ||
| نویسندگان | ||
| سعید فرزین* 1؛ حجت کرمی2؛ مهسا دوست محمدی3؛ آنسه قنبری3؛ الهام ضمیری3 | ||
| 1استادیار، گروه مهندسی آب و سازه های هیدرولیکی، دانشکدۀ مهندسی عمران، دانشگاه سمنان | ||
| 2استادیار، گروه مهندسی آب و سازههای هیدرولیکی، دانشکدۀ مهندسی عمران، دانشگاه سمنان | ||
| 3دانشجوی کارشناسی ارشد مهندسی و مدیریت منابع آب، دانشکدۀ مهندسی عمران، دانشگاه سمنان | ||
| چکیده | ||
| بارش یکی از فرایندهای هیدرولوژیک است که تأثیر زیادی در کنترل مدیریت منابع آب دارد. کمبود بارش سبب به‌وجود‌آمدن مشکلات فراوانی از جمله کمبود آب شرب میشود. به‌علت اهمیت مسئلۀ کمبود آب، استفاده از روشهای نوین بهمنظور پیشبینی فرایندهای هیدرولوژیک تأثیر زیادی در برنامهریزی و مدیریت منابع آب خواهد داشت. از این‌رو، در تحقیق حاضر کمبود آب ماهانه در حوضۀ نازلوچای طی یک دورۀ آماری 39ساله (1352ـ 1391) با استفاده از مدل شبکۀ عصبی مصنوعی و مدل بهبودیافتۀ موجک‌ـ شبکۀ عصبی، شبیهسازی، پیشبینی و تحلیل شده است. عملکرد این دو مدل توسط معیارهای خطای ضریب همبستگی، ضریب تبیین و جذر میانگین مربعات خطا ارزیابی شد. بنا بر نتایج به‌دست‌آمده، مدل موجک‌ـ شبکۀ عصبی با ضریب همبستگی 96/0 و 945/0 که به‌ترتیب مختص به حالت آموزش و آزمون است، نسبت به شبکۀ عصبی مصنوعی توانایی بیشتری برای پیشبینی کمبود آب داشت. در ادامه، مقادیر کمبود آب ماهانه در این حوضه طی سالهای 1392 تا 1399 پیشبینی شده است. نتایج نشان میدهند روند کمبود آب همچنان مانند گذشته باقی است. البته، متوسط میزان کمبود در 8 سال آینده تقریباً 95/2 میلیون متر‌مکعب تخمین زده شد. در حالی که همین پارامتر برای 39 سال گذشته 04/4 میلیون متر‌مکعب بوده است. از این‌رو، نیاز است که برای سالهای آینده اقدامات لازم انجام شود و با برنامهریزی مدیریتی دقیق برای بهرهبرداری از منابع آب (کشاورزی، صنعت، شرب و...)، کاهش میزان کمبود آب در سالهای آتی ممکن شود. | ||
| کلیدواژه‌ها | ||
| الگوریتم موجک؛ شبکۀ بهینه؛ کمبود آب؛ نویز‌زدایی؛ هوش مصنوعی | ||
| عنوان مقاله [English] | ||
| The performance of Artificial Neural Network in prediction and analysis of hydrological processes (Case study: Water shortage in Nazloo-chai watershed, West Azerbaijan province) | ||
| نویسندگان [English] | ||
| Saeed Farzin1؛ Hojat Karami2؛ Mahsa Doostmohammadi3؛ Anese Ghanbari3؛ Elham Zamiri3 | ||
| 1Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran | ||
| 2Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan, Iran | ||
| 3M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran. | ||
| چکیده [English] | ||
| Precipitation is one of the hydrological processes that play an important role in controlling water resources management. Shortage of rain causes some problems such as lack of drinking water. Due to the importance of the issue of water shortage, using modern methods to predict hydrological processes will play an important role in planning and management of water resources. Therefore, in this study, monthly shortage of water in Nazloo-chai watershed was predicted using Artificial Neural Network (ANN) and improved wavelet-neural network (IWNN) models, for the past 39 years (1973-2012). Performance of these two models was evaluated using statistical indicators including correlation coefficient (R), determination coefficient (R2) and root mean square error (RMSE). According to the results of IWNN model, the obtained correlation coefficient was 0.960 and 0.945 for testing and training modes, respectively, and this model has greater ability for predicting the shortage of water in comparison with ANN. Accordingly, the amount of monthly water shortage in this watershed was predicted for 2013 to 2020. Results indicated that shortage of water still remains as in the past years. The average water shortage was estimated nearly as 2.95 million cubic meters (MCM) in the next 7 years, while, this parameter for the past 39 years was 4.04 MCM. Therefore, it is required to take necessary measures for future years, and with careful management plans for exploitation of water resources (agriculture, industry, urban, etc.), it is possible to reduce water shortage in the coming years. | ||
| کلیدواژه‌ها [English] | ||
| Water shortage, Artificial Intelligence, Wavelet algorithm, De-noising, Optimized network | ||
| مراجع | ||
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