مقايسه برخي از روش هاي هوش مصنوعي در پيش بيني سري‌هاي زماني روزانه دماي حداقل، حداكثر و بارش ايستگاه سد تنگاب واقع در استان فارس

Comparison of Artificial Intelligence Methods in the Prediction of Daily Times Series of Minimum Temperature, Maximum Temperature, and Rainfall at Tangab Dam Station (Fars Province)

توسعه يك مدل پيش‌بيني هيدرولوژيكي بر اساس اطلاعات ثبت شده‌ي گذشته، به منظور مديريت و برنامه‌ريزي موثر مخازن آبي، وابسته به پيش‌بيني و درك صحيح از سري‌هاي زماني تاثير گذار در منابع آب است. در سال‌هاي اخير يكي از موضوعات رو به رشد در اين زمينه، استفاده از روش‌هاي هوش مصنوعي در مدل‌سازي، پيش‌بيني و بازيابي اطلاعات هيدرولوژيكي است. در اين مقاله به مقايسه روش‌هاي هوش مصنوعي در پيش‌بيني و بازيابي سري‌هاي زماني روزانه‌ي دماي حداقل و حداكثر و بارش در ايستگاه سد تنگاب پرداخته شده است. در اين مطالعه هم از خود سري‌ها (استفاده از تاخير در سري‌ها) و هم از ايستگاه‌هاي مجاور، به منظور بازيابي و پيش‌بيني اطلاعات، استفاده شده است. روش‌هاي MLP (پرسپترون چند لايه)، RBF (توابع شعاعي پايه)، SVM (ماشين‌هاي بردار پشتيبان)، روش منطق فازي (FIS) و روش ANFIS (سيستم استنتاج عصبي- فازي)، مورد بررسي قرار گرفته‌اند. به منظور ارزيابي و سنجش عملكرد اين مدل‌ها از ميانگين توان دوم خطا (MSE)، ضريب همبستگي (R)، واريانس و انحراف معيار داده‌هاي حاصل، و همچنين نمودارهاي گرافيكي استفاده شده است. نتايج نشان دهنده عدم كارايي مدل‌ها در پيش بيني بارش است ولي به منظور بازيابي بارش و پيش‌بيني دما مي‌توان از اين روش‌ها استفاده كرد.

Introduction The field of prediction using linear statistical methods was considered for a long time. Nevertheless, in the late 1970s and early 1980s, it was proved that the linear models are not of appropriate consistency in many of actual applications. The models known as block-box or data-driven models have been placed as the serious competitors of classic statistical models in the field of prediction and recovery. The results, especially in the long-term predictions, are useful in many of water resource applications such as environmental protection, drought management, operation of water supply facilities, optimal reservoir operation including multiple irrigation objectives, power generation, and sustainable development of water resources. Thus, the prediction of hydrological meteorological time series has been always a topic of interest in the operational hydrology. This has attracted much attention in the last few decades and many models have been proposed for predicting time series in order to improve the hydrological prediction. Most of the uses of artificial intelligence in the hydrology have been so far on the monthly and annual data and it has been less used from daily data. In this study, it is dealt with the abilities of 5 artificial intelligence methods including MLP, SVM, RBF, FIS, and ANFIS, in the prediction of hydrological time series of precipitation and the maximum and minimum daily temperatures, through both the station data by making delay in the main series, and the neighboring station data. It is attempted to identify the best method to predict and recovery the missing data of the series. Material and Methods -Adaptive Neuro-fuzzy Inference System (ANFIS) In 1993, Jang has introduced a learning method for fuzzy inference system (FIS). In this method, the neural network learning algorithm is used to create a set of if-then fuzzy rules with a number of appropriate membership functions (MFs) of inputoutput pairs. The method, used to create FIS from neural networks framework, is called ANFIS. -Support vector regression (SVR) SV algorithm was developed in Russia in 1960s (Vapnik, 1964; Vapnik and Lerner, 1963). This was a generalization on Generalized Portrait algorithm. The basic idea of SVM is to use the linear model to implement nonlinear classification boundaries through a number of non-linear mapping of input vector to a high-dimensional feature space (Wang et al., 2009). - Radial basis function (RBF) RBF is a three-layer neural network, including input layer, hidden layer and output layer (Guo et al., 2012). High convergence speed, lower reps during training, not positioning in the local minimum and stronger robustness are the advantages of RBF over BP (Liu et al., 2006}. RBF first layer neurons release only the inputs features to the next layer (hidden layer). In the second layer, each neuron associates with a kernel function with a center and a width. In the last layer, neurons calculate the weighted simple sum based on the answer of the hidden layer for input pattern (Wen et al., 2012). -Multi-layer perceptron (MLP) Multi-Layer Perceptron (MLP) can be a generalization on perceptron networks with at least one hidden layer. MLP is a feed forward neural network that has one or more layers between the input and output layers (Talebizadeh and Moridnejad, 2011). In MLP, each neuron calculates total weighted inputs based on an activation function and expresses the response accordingly (there are many activation functions that the most common of them are sigmoid, hyperbolic, linear, Elliott) (Moghaddamnia et al., 2009}. - Fuzzy Inference System (FIS) The FIS main structure is consisted of three conceptual components: a) Rules Base: that is consisted of a set of fuzzy rules, b) A database that define the membership functions (MFs) used in the fuzzy rules, c) An argument mechanism that performs the output inference approach based on derivation rules. Conclusions Since the precipitation (rainfall) is of an alternative nature but its time and place distributions are very inharmonic and the meteorological series are considered as chaos series (Watts, 2007; Ott, 2002; Lorenz, 1963; Ivancevic and Tijana, 2008L the use of an input data of the adjacent station or making delay in its series, cannot provide an accurate prediction, even thought the used method is successful in other fields. Although the artificial intelligence methods could not be of the necessary and all-round efficiency in the case of predicting daily precipitation, the use of an influencing input in the precipitation can be effective for the precipitation prediction. With this possibility, it can be yet said that it is better to give the prediction of rainfall in the future days to the general circulation atmospheric models, because these models have proven their ability in predicting rainfall and other meteorological series in the next days (Cox, 2010; Holton, 2004; Brown, 2008). In the case of predicting meteorological series in the future decades, it can be argued that the use of down scale models and the outputs of general circulation atmospheric models could operate much better than the artificial intelligence approaches (Khan et al., 2006). The artificial intelligence methods operate very well in the prediction and recovery of temperature data and can be used as a tool for recovery the missing data of meteorological stations. Also, the use of other inputs affecting the temperature can lead to increase the acceptable performance of these methods. In the case of limit cases, it could be stated that the artificial intelligence methods are very weak in providing the limit values, which this weakness was obvious from the error histogram.