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مقایسۀ مهارت مدل¬های استوکاستیک و شبکه¬ها¬ی عصبی مصنوعی در مدلسازی و پیشبینی مقادیر و طبقات شاخص بارندگی استاندارد شده | ||
| پژوهش های جغرافیای طبیعی | ||
| مقاله 6، دوره 45، شماره 2، شهریور 1392، صفحه 91-108 اصل مقاله (1.01 M) | ||
| نوع مقاله: مقاله کامل | ||
| شناسه دیجیتال (DOI): 10.22059/jphgr.2013.35150 | ||
| نویسندگان | ||
| سمیه حجابی* 1؛ جواد بذرافشان2؛ نوذر قهرمان2 | ||
| 1دانشجوی کارشناسی ¬ارشد هواشناسی¬کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران | ||
| 2استادیار گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران | ||
| چکیده | ||
| هدف از پژوهش پیش رو، مقایسۀ کارایی مدلهای استوکاستیک و شبکههای عصبی مصنوعی در پیشبینی کمّی شاخص بارندگی استاندارد شده (SPI) در اقلیمهای خشک و مرطوب ایران است. برای این امر، محاسبۀ SPI، در مقیاسهای زمانی سهماهه، ششماهه و دوازدهماهه در چهار ایستگاه سینوپتیک کشور طی دورۀ 2007-1973 انجام شد. در گام بعد، مدلسازی سریهای زمانی SPI برای پیشبینیهای یک تا دوازده گام به جلو، به سه روش مدلسازی استوکاستیک، شبکۀ عصبی بازگشتی (RMSNN) و شبکۀ عصبی مستقیم (DMSNN) انجام گرفت. مقادیر SPI مربوط به دورۀ 1973 تا 2000، برای توسعۀ مدلها و مابقی برای صحتسنجی مدلها مورد استفاده قرار گرفت. در مرحلۀ صحتسنجی، مقایسۀ مقادیر مشاهدهشده و پیشبینیشده SPI با استفاده از آزمونهای آماری، ضریب همبستگی و شاخص خطا انجام شد. همچنین برای بررسی قابلیت مدلها در پیشبینی طبقات SPI، از آماره کاپای کوهن استفاده شد. در نهایت، اولویت دقت مدلها از دیدگاههایی چون، افق زمانی پیشبینی و مقیاس زمانی بررسی خشکسالی تعیین شد. نتایج بهدست آمده نشان داد: 1) در مقیاس زمانی سه، شش و دوازدهماهه، بهطور کلی مدلهای استوکاستیک (بهترتیب با میانگین خطای 678/0، 569/0 و 344/0 و میانگین ضریب همبستگی 682/0، 777/0 و 919/0) از نظر مهارت پیشبینی مقادیر SPI در اولویت کاربرد قرار دارند. 2) در مقیاس زمانی سه، شش و دوازدهماهه بهترتیب، مدلهای DMSNN ، RMSNN و استوکاستیک (با میانگین کاپای 397/0، 530/0 و 750/0) از نظر مهارت پیشبینی طبقات SPI در اولویت کاربرد قرار دارند. | ||
| کلیدواژه‌ها | ||
| اقلیم¬های خشک و مرطوب؛ پیشبینی؛ خشکسالی؛ شاخص بارندگی استاندارد شده؛ مدل¬های استوکاستیک؛ مدل¬های شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Comparison of Stochastic and Artificial Neural Networks Models in Modeling and Forecasting the Standardized Precipitation Index Values and Classes | ||
| نویسندگان [English] | ||
| Somayyeh Hejabi1؛ Javad Bazrafshan2؛ Nozar Ghahreman2 | ||
| 1M.Sc. Student in Agrometeorology, College of Agriculture and Natural Resources, University of Tehran | ||
| 2Assistant Prof., Dep. of Irrigation and Reclamation Engineering, College of Agriculture and Natural resources, University of Tehran | ||
| چکیده [English] | ||
| Introduction Drought is a temporary and recurring meteorological event which results from the lack of precipitation over an unusual extended period of time. Early indication of possible droughts can help set out drought mitigation strategies and measures, in advance. Therefore, the drought forecasting plays an important role in the planning and management of water resource systems. Stochastic models have been extensively used for forecasting hydrologic variables such as annual and monthly stream flow, precipitation, and etc. in the past. But they are basically linear models assuming that data are stationary, and have a limited ability to capture non-stationarities and nonlinearities in the hydrologic data. However, it is necessary to consider alternative models when nonlinearity and non-stationarity play a significant role in the forecasting. In the recent decades, artificial neural networks have shown great ability in modeling and forecasting nonlinear and non-stationary time series due to their innate nonlinear property and flexibility for modeling. The aim of this study is to compare the stochastic and artificial neural network models in forecasting the standardized precipitation index (SPI) in some stations of Iran. This is because of the multiplicity of drought occurrences in Iran and the necessity to determine the best forecasting model. Methodology The monthly total precipitation data (1973-2007) related to four synoptic stations of Iran including Bandar Anzali (with very wet climate), Hamedan Nojeh (with semi arid climate), and Bushehr (with arid climate) and Zahedan (with hyper arid climate) have been used after the homogeneity and adequacy of data have been confirmed by statistical tests. In the present study standardized precipitation index (SPI) time series (at 3-, 6- and 12-month timescales) have been calculated for the period of 1973-2007. The most suitable distribution function for precipitation at 3- , 6- and 12- month timescales has been determined by Easyfit software on the basis of kolmogorov-Smirnov statistic. This is performed separately for each month. Then, each cumulative probability density function is transformed into a cumulative standardized normal distribution. The SPI values for the period of 1973-2000 are used to calibrate the models and the rest of the data to be tested. Development of stochastic model consists of three stages of identification, estimation, and diagnostic checking (Box and Jenkins, 1976, 19). During the identification stage the candidate forms of the models are determined using the autoregressive function (ACF) and partial autoregressive function (PACF) and general forms of the models are determined on the basis of Schwarz Bayesian information criterion (Schwartz, 1978, 461–464) and Akaike information criterion (Akaike, 1974, 716–723). In the estimation stage the model parameters were calculated using Minitab14 software. Finally, diagnostic checks of the model are performed using kolmogorov-Smirnov (K-S) and Portmanteau test (Makridakis et al., 2003, 185) to reveal possible model inadequacies and to assist in selecting the best model. In the present paper two different approaches of neural networks including recursive multi-step neural network approach (RMSNN) and direct multi-step neural network approach (DMSNN) are used for forecasting several time steps ahead. The RMSNN approach based on one output node forecasts a single step ahead, and the network is applied recursively, using the previous predictions as inputs for the subsequent forecasts. DMSNN is based on the multiple outputs, when several nodes are included in the output layer, and each output node represents one time step to be forecasted. The models are evaluated with statistical tests, correlation coefficient, and error index for 1- to 12-lead time ahead forecasting over the period of 2001- 2007. Also, the capability of the models in forecasting the SPI classes is investigated using Cohen’s Kappa statistic (Cohen, 1960, 37–46). Results and Discussion The results of stochastic modeling of SPI time series showed that the null hypothesis related to the normality of residuals is accepted for 3- and 6- month time scales but rejected for 12-month time scales at 1% significant level in all stations. The results of Portmanteau test signify that the chosen stochastic models are adequate on the available data at 1% significant level. The results of artificial neural networks (RMSNN and DMSNN) modeling of each SPI time series are presented as optimal architectures of the best number of input and hidden neurons. The significance lead times of drought forecasting are determined based on correlation coefficient and Kappa statistic between the observed and forecasted values of the SPI time series in the stations of interest. Accordingly, the most appropriate models for SPI values and classes have been determined by a comparison of three models for each time series. Conclusion The results have revealed that generally, for 3-, 6- and 12-month time scales, stochastic models (with average error of 0.678, 0569 and 0.344 and average correlation coefficient of 0.682, 0.777 and 0.919, respectively) are more accurate than artificial neural network models to forecast SPI values. The comparison of models in forecasting SPI classes also showed that the most accurate model for forecasting SPI classes for 3-, 6- and 12-month time scales is DMSNN, RMSNN and stochastic model (with average Kappa of 0.397, 0530 and 0.750) in sequence. | ||
| کلیدواژه‌ها [English] | ||
| Artificial Neural Network Models, Drought, forecasting, Standardized Precipitation Index, stochastic models, Wet and Dry Climates | ||
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