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, ,
Volume 16, Issue 42 (9-2016)
Abstract

  Temperature alteration plays special role as one of the most basic climate elements. So inspection of temperature alteration and anticipation has scientific- applied magnitude. In this study inspection of several cases of statistical characteristics of monthly­ average, maximum and minimum temperature and illumination of their alteration method­, temperatures predictability by ANFIS is evaluated­. Applied data is over 288 months during 24 years of statistical period since January of 1987 until December of 2010 through synoptic stations of Pars Abad, Ardebil and Khalkhal. According to equations of data lineal process­, lineal process of temperatures through all of the stations is positive and­ additive­. Lineal process gradient in minimum temperature is more than other­ maximum and average temperature. Less amplitude more variance and standard aviation and­­ data ­predictability is more. According to present article adaptive Neuro – fuzzy inference system mostly has acceptable function through anticipation of monthly minimum, maximum and average temperature in the stations of Ardebil province.


Somayeh Soltani Gerdfaramarzi, Aref Saberi, Morteza Gheisouri ,
Volume 17, Issue 44 (3-2017)
Abstract

Rainfall is one of the most important components of the water cycle and plays a very important role in the measurement of climate characteristic in any area. Limitations such as lack of sufficient information about the amount of rainfall in time and space scale and complexity of the relationship between meteorological elements related to rainfall, causes the calculation of these parameters using the conventional method not to be implemented. One method of evaluating and forecasting of rainfall in each region is time series models. In this research, to predict the average annual rainfall synoptic station at Mahabad, Uromiya and Mako in West Azarbayejan provience during 1984-2013, linear time series ARIMA was used. To investigate model static, Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) was applied and with differencing method, the non-static data transformed to static data. In next step, stochastic models to estimate the annual rainfall average were used. With regard to the evaluation criterion such as T, P-VALUE < 0.05 and Bayesian Information Creterion (BIC), ARIMA (1,0,0), ARIMA (0,1,1) and ARIMA (0,1,1) models was determined as a suitable model for predicting annual rainfall in the three selected stations at Uromiya, Makoo and Mahabad. In the following, the annual rainfall for 3 (2013-2016) years is forecasted which based on rainfall data in that time, the adjusted model was acceptable.


Mehdi Feyzolahpour, Marziye Manafi, Reza Khoshraftar, ,
Volume 21, Issue 62 (9-2021)
Abstract

Reviewing the damage caused by landslide proves the need to examine the factors influencing the occurrence of this phenomenon and the prediction of its occurrence. Therefore, the purpose of this study was to improve the prediction of landslide occurrence in the Taleghan watershed using Shannon Entropy Theory. Among the factors influencing the occurrence of landslide, ten factors of elevation, slope, slope direction, geology, vegetation, land use, water congestion, fault, road, rainfall as independent variables and sliding zones were considered as dependent variables. Then, using the entropy index, weighing was calculated for each of these factors based on their effectiveness, and the value map of each parameter was calculated according to its weight. In the next step, by mapping these maps with the map of landslides, a risk zoning map for the basin was drawn up. After calculating the Shannon entropy index, it was determined that 86% of the landslide area is in three medium-risk, high-risk and highly hazardous areas, indicating that the final map of the zoning is based on the correct method. Also, the total quality index (Qs) in this method was equal to 2.3, which indicates that this method is more reliable and more suitable for zoning of landslide hazard in Taleghan watershed. The accuracy of the method (P) for the entropy model was equal to 0.24, indicating a more appropriate resolution of the risk zones in this method.


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