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تخمین خشکسالی با استفاده از شبکه های هوشمند | ||
| هیدروژئومورفولوژی | ||
| مقاله 9، دوره 4، شماره 14، بهار 1397، صفحه 179-197 اصل مقاله (842.01 K) | ||
| نوع مقاله: پژوهشی | ||
| نویسندگان | ||
حسن ترابی پوده  1؛ بابک شاهی نژاد2؛ رضا دهقانی3
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| 1دانشیار گروه مهندسی آب، دانشگاه لرستان | ||
| 2استادیار گروه مهندسی آب دانشگاه لرستان، لرستان، ایران | ||
| 3دانشجوی دکترای سازه های آبی، دانشگاه لرستان، لرستان، ایران. | ||
| چکیده | ||
| چکیده خشکسالی یکی از پدیده‌های آب و هوایی است که در همهی شرایط اقلیمی و در همهی مناطق کرهی زمین به وقوع می‌پیوندد. پیش‌بینی خشکسالی نقش مهمی در طراحی و مدیریت منابع طبیعی، سیستم‌های منابع آب، تعیین نیاز آبی گیاه ایفا می‌نماید. در این پژوهش جهت تخمین شاخص بارش استاندارد 12 ماههی چهار ایستگاه بارانسنجی دلفان، سلسله، دورود و بروجرد واقع در استان لرستان از مدل شبکهی عصبی موجک استفاده شد و نتایج آن با سایر روشهای هوشمند از جمله شبکهی عصبی مصنوعی مقایسه گردید. برای این منظور از پارامتر بارش در مقیاس زمانی ماهانه در طی دورهی آماری (1372-1392) به عنوان ورودی و شاخص بارش استاندارد به عنوان پارامتر خروجی مدلها انتخاب گردید. معیارهای ضریب همبستگی، ریشهی میانگین مربعات خطا و میانگین قدر مطلق خطا برای ارزیابی و عملکرد مدلها مورد استفاده قرار گرفت. نتایج نشان داد هر دو مدل قابلیت خوبی در تخمین شاخص بارش استاندارد دارند، لیکن از لحاظ دقت، مدل شبکهی عصبی موجک عملکرد بهتری نسبت به شبکهی عصبی مصنوعی از خود نشان داده است. در مجموع نتایج نشان داد استفاده از مدل شبکهی عصبی موجک میتواند در زمینه تخمین خشکسالی موثر باشد. | ||
تازه های تحقیق | ||
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| کلیدواژه‌ها | ||
| کلمات کلیدی: بارش؛ خشکسالی؛ شاخص بارش استاندارد؛ شبکه ی عصبی موجک | ||
| عنوان مقاله [English] | ||
| Drought Estimate Using Artificial Network Estimation Drought Using Intelligent Networks | ||
| نویسندگان [English] | ||
| hassan torabipodeh1؛ Babak Shahinejad2؛ Reza Dehghani3 | ||
| 1Associate Professor of Water Engineering, Lorestan University | ||
| 2Assistant Professor, of Water Engineering ,University of Lorestan, Khorramabad, Iran | ||
| 3Ph.D. Student of Water Structure, Faculty of Agric., University of Lorestan, Khorramabad, Iran. | ||
| چکیده [English] | ||
| Background and Objective Drought is one of the phenomena of climate that occurs in all climatic conditions and in all parts of the planet. Drought prediction has an important role in designing and managing natural resources, water resource systems, and determining the plant's water requirement. For estimating drought, various approaches have been introduced in hydrology that artificial models are the most important ones. In this study for evaluating the accuracy of the models in estimating the 12-month standard rainfall index, monthly data from four weather stations in Boroujerd, Dorood, Selseleh and Dolphan in Lorestan province have been used. For modeling of drought in these stations utilized wavelet neural network and artificial neural network models and the results were compared to each other for the accuracy of the studied models. In a few studies, each of the models presented in the drought estimation has been studied. But the purpose of this research is simultaneous analysis of these models at four stations for estimating the standard rainfall index. Methods In this study, Boroujerd, Dorood, Selseleh and Dolphan that located in Lorestan province have been selected as the study area During the statistical period, the precipitation parameter was used at monthly time scale (1962-1372) for input and standard rainfall index as the output parameter of the models. For this purpose, at first 80% of the data (1372-1382) were selected for calibration of the models and 20% of the data (2012-2013) were used to validate the models. The wavelet neural network, which has a very good fit with the sinusoidal equations by separating the signal into high and low frequencies, can greatly increase the accuracy of the model and reduce noise. Artificial neural networks are inspired by the brain information processing system that ability to approximate patterns of a model has increased the scope of these networks. Correlation coefficient, root mean square error and mean absolute error value were used for evaluation and performance of the models. Results The results showed that both models have good performance in estimating the standard rainfall index in the four stations studied. Also, according to the evaluation criteria, the wavelet neural network model was found to have the highest accuracy and low error rate compared to the artificial neural network model. Conclusions In total, the results showed that the use of wavelet neural network model can be effective in estimating the standard rainfall index. also It can be useful in facilitating the development and implementation of management strategies to prevent drought and is a step in making managerial decisions to improve water resources. | ||
| کلیدواژه‌ها [English] | ||
| Keywords: Precipitation, Drought, Standardized Precipitation Index, Wavelet Neural Network | ||
| اصل مقاله | ||
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| مراجع | ||
منابعـ اسدزاده، فرخ؛ بایزیدی، مطلب و مهری کاکی (1395)، پایش و تخمین خشکسالی ایستگاههای شرق دریاچهی ارومیه با استفاده از مدل عصبی ـ فازی تطبیقی، اکوهیدرولوژی، شمارهی 2، صص 205-218.ـ کاوه، علی و عباس ایران‌منش (1384)، شبکهی عصبی مصنوعی در بهینه‌سازی سازه‌ها، چاپ سوم، انتشارات مرکز تحقیقات ساختمان و مسکن. Djerbouai, M., Gamane, D., (2016), Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Vol.30, No.7: PP,2445-2464.
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Zulifqar, A., Hussain, I., Faisal,M., Mamona Nazir, H., Hussain, T., Yosafshad,M., Shoukry, AM., Gani, S., (2017), Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model, Advances in Meteorology, Vol.25, No.1: PP,1-10. | ||
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آمار تعداد مشاهده مقاله: 133 تعداد دریافت فایل اصل مقاله: 155 |
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