نوع مقاله : پژوهشی

نویسندگان

1 دانشیار گروه مهندسی آب، دانشگاه لرستان

2 استادیار گروه مهندسی آب دانشگاه لرستان، لرستان، ایران

3 دانشجوی 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

عنوان مقاله [English]

Drought Estimate Using Artificial Network Estimation Drought Using Intelligent Networks

نویسندگان [English]

  • hassan torabipodeh 1
  • Babak Shahinejad 2
  • Reza Dehghani 3

1 Associate Professor of Water Engineering, Lorestan University

2 Assistant Professor, of Water Engineering ,University of Lorestan, Khorramabad, Iran

3 Ph.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

منابع

ـ 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.
Edwards, DC. (1997), Characteristics of 20th century drought in the United States at multiple time scales, Climatology Report Number 97-2, Colorado State University, Fort Collins, Colorado.
Gaye, O., Yildiz, O., Duvan, A., (2015), A Drought Analysis Of Sivas Using The Standardized Precipitation Index (SPI) Method And Drought Estimation With The Artificial Neural Networks, International Journal of Advances in Mechanical and Civil Engineering, Vol. 2, No. 5: PP,1-7.
Jalalkamali, A., Moradi, M., Moradi, M., (2015), Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index, International Journal of Environmental Science and Technology, Vol. 4, No. 12: PP,1201-1210.
Kisi, O., Karahan, M., Sen, Z. (2006), River suspended sediment modeling using fuzzy logic approach, Hydrol Process, Vol.20, No.2: PP,4351-4362.
Maca, P., Pech, P. (2016), Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks, Computational Intelligence and Neuroscience, Vol. 24, No. 3: PP,40-57.
McKee, TB., Doesken, NJ., Kleist, J., (1993), The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, January 17e22, Anaheim, California: PP,179-184.
Nagy, H., Watanabe, K., Hirano, M. (2002), Prediction of sediment load concentration in rivers using artificial neural network model, Journal of Hydraulics Engineering, Vol.128, No.4: PP,558-559.
-Nourani, V., Alami, MT., Aminfar, MH. (2009), A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation, Engineering Applications of Artificial Intelligence, Vol. 22, No. 2: PP,466–472.
-Nourani, V., Kisi,O., Komasi, M., (2011), Two hybrid artificial intelligence approaches for modeling rainfall–runoff process, Journal of Hydrology, Vol.402, No. 1–2: PP,41–59.
-Shin, S., Kyung, D., Lee, S., Taik & Kim, J., Hyun, J., (2005), An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, Vol. 28, No.1: PP,127-135.
-Tokar, A., Johnson,P., (1999), Rainfall-Runoff Modeling Using Artificial Neural Networks, J Hydrol., Eng, Vol.4, No.3: PP,232-239.
-Wang, D., Safavi, AA., Romagnoli, JA., (2000), Wavelet-based adaptive robust M-estimator for non-linear system identification, AIChE Journal, Vol. 46, No.8: PP,1607-1615.
-Zhu, YM., Lu, XX., Zhou,Y., (2007), Suspended sediment flux modeling withartificial neural network: An example of the Longchuanjian River in the Upper Yangtze Catchment, Geomorphology, Vol.84, No.1: PP,111-125.
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.