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ارزیابی مدل های تلفیقی شبکه‌ی عصبی‌مصنوعی-موجک و برنامه ریزی بیان ژن-موجک در پیش‌بینی‌کردن خشک‌سالی کوتاه‌مدت | ||
| پژوهش های آبخیزداری | ||
| مقاله 5، دوره 33، شماره 1 - شماره پیاپی 126، فروردین 1399، صفحه 39-55 اصل مقاله (1.04 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2019.125825.1204 | ||
| نویسندگان | ||
| محبوبه یونسی1؛ حامد نوذری* 2 | ||
| 1دانشجوی دکترای مهندسی منابع آب، گروه مهندسی علوم آب، دانشکده‌ی کشاورزی، دانشگاه بوعلی سینا، همدان | ||
| 2دانشیار مهندسی منابع آب، گروه مهندسی علوم آب، دانشکده‌ی کشاورزی، دانشگاه بوعلی سینا، همدان | ||
| چکیده | ||
| پیش‌بینی‌کردن خشک‌سالی نقش مهمی در طراحی و مدیریت کردن منابع طبیعی، سامانههای منابع آب و تعیین‌کردن نیاز آبی گیاه دارد. از سوی دیگر، تبدیل موجک یکی از روشهای نوین و بسیار موثر در تجزیه کردن پیامها و مجموعه‌های زمانی است. در این تحقیق پیام شاخص بارش معیار (SPI) با موجک مادر تجزیه کرده، و نتیجه‌ی آن ورودی مدلهای شبکه‌ی عصبی‌مصنوعی و برنامه‌ریزی بیان ژن گرفته شد. برای پیش‌بینی‌کردن خشک‌سالی شبکههای عصبی‌مصنوعی شناسنده‌ی چندلایه، تابع پایه‌یی شعاعی، برنامه‌ریزی بیان ژن، شبکههای عصبی مصنوعی-موجک شناسنده‌ی چندلایه، تابع پایه‌یی شعاعی، و برنامه‌ریزی بیان ژن-موجک به‌کاربرده شد. داده‌های بارندگی از ایستگاه هواشناسی بیدستان با دوره‌ی داده‌برداری 44 ساله در آبخیز شور استان قزوین گرفته شد. وضعیت رطوبتی با شاخص بارندگی به‌معیارشده در دوره‌ی سه‌ماهه محاسبه کرده شد. برای تخمین مقدار شاخص بارندگی به‌معیارشده در هر بازه‌ی زمانی، اندازه‌های زمان‌های پیش‌تر به‌کاربرده شد. نتیجه‌ها نشان داد که از میان 6 مدل بررسی‌شده، برنامه‌ریزی بیان ژن-موجک با دقت بیش‌تری شاخص بارش معیار و وضعیت خشک‌سالی کوتاه‌مدت را پیش‌بینی می‌کند. در بهترین حالت نیز اندازه‌ی شاخص‌های R2، RMSE، MAE و NS در مرحله‌ی صحت‌سنجی برای مدل WA-GEP به‌ترتیب 0/911، 0/037، 0/022 و 0/845 بود. | ||
| کلیدواژه‌ها | ||
| برنامه‌ریزی بیان ژن- موجک؛ شاخص بارش استاندارد؛ شبکه‌ی عصبی مصنوعی- موجک؛ پیش‌بینی خشک‌سالی | ||
| عنوان مقاله [English] | ||
| Evaluation of Integrated Models of Wavelet-Artificial Neural Network and Wavelet-Gene Expression Programming in the Short-Term Drought Prediction | ||
| نویسندگان [English] | ||
| Mahbobeh Younesi1؛ Hamed Nozari2 | ||
| 1Ph.D. Student in Water Resources Engineering, Department of Water Sciences Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran | ||
| 2Associate Professor. Department of Water Sciences Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran | ||
| چکیده [English] | ||
| Drought prediction plays an important role in the planning and management of natural resources, water resources and plant water requirements. Wavelet transform is one of the new and highly effective methods for analysing signals and time series. Using the mother wavelet, the standard precipitation index (SPI) signal was analyzed and the results were considered as inputs of the artificial neural network models and the gene expression programming (GEP). Multi-layer perceptron (MLP), radial basis function (RBF), (GEP), as well as the wavelet-artificial neural networks integrated model and multi-layer perceptron (WA-MLP), radial basis function (WA- RBF) and wavelet- gene expression programming (WA- GEP) were used for drought forecasting. The rainfall data collected at the Bidestan Station for a period of 44 years were used on the Shoor Watershed in the Province of Qazvin. Moisture condition was calculated using the SPI in the short-term period of 3 months. To estimate the SPI in each period, the respective amounts were considered from the previous cycles. The results showed that among the six applied models, the WA-GEP predicted the SPI values and the short-term drought condition with a higher accuracy. The WA-GEP model proved to be the best scenario in the validation stage of R2, RMSE, MAE and NS of 0.911, 0.037, 0.022 and 0.845, respectively. | ||
| کلیدواژه‌ها [English] | ||
| Drought prediction, standard precipitation index, wavelet-artificial neural networks, wavelet-gene expression programming | ||
| مراجع | ||
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