In this study the possibility of using Fuzzy Inference system efficiency, creating a bridge between meteorological, plant parameters, and Daily Yield, and comparing the accuracy of Daily Yield using these Systems were investigated. After analyzing the different models and different combinations of daily meteorological data, seven models for estimating daily Yield were presented. For these models, the calculated Yield from AQUACROP model was considered as a base and the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination, Jacovides (t) and Sabbagh et al. (R2/t) criteria. An experiment was carried out during the 2014-2015 growing season in the Agricultural Research and Education Center of Khorasane Razavi province using a randomized complete block design with a split plot arrangement and four replications. This experiment was including of three irrigation levels treatments as the main plot and three method of planting treatments (transplanting 20-days, transplanting 30-days and direct seeded) as subplots. From the available data, 75 percent was used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the Fuzzy models with different input parameters as compared with AQUACROP model showed that Fuzzy Systems were very well able to estimate the daily Yield. Fuzzy model so that the highest correlation with the 9 input variables (r=0. 98) had in mind and evaluate other parameters, the model with 2 parameters, match very well with the AQUACROP model had stage training. In the test phase, training phase was very similar results and the model with the second phase of harvest index and canopy cover will get the best match. According to the results of this study it can be concluded that Fuzzy model approach is an appropriate method to estimate the daily yield.

To estimate actual evapotranspiration of grass, an experiment was a weather stationat, the Faculty of Agriculture, Ferdowsi University of Mashhad in 1389 year. In this experiment, actual evapotranspiration grass Deficit irrigation at different levels (5 levels) with a single branch sprinkler system, at two-day period was measure the water balance method. Also was estimated the reference crop evapotranspiration with FAO Penman, Hargreaves-Samani and pan evaporation methods. Coefficients calculated for each plant with water level and Five Fuzzy model was provided for estimating actual daily evapotranspiration. In these models was considered FAO Penman evapotranspiration as the output model. Performance models were compared using RMES, MAE, MBE, t and R2/t. The results showed that evapotranspiration values calculated in terms of standard methods of FAO Penman and Hargreaves - Samani, compared with the water balance method, respectively, 17 and 14 percent more than had been estimated. With analysis of evapotranspiration values in non-standard conditions were found to reduce grass Deficit irrigation is the actual evapotranspiration, The difference amounts to 20 percent evapotranspiration Deficit irrigation conditions was not a significant effect on evapotranspiration. Fuzzy model output results also showed that the Fuzzy models developed using the combined model (PMF56) had a high Match and the ability to estimate actual evapotranspiration are included in the daily scale.

Milk ultrafiltration is a membrane process where it is subjected to a hydrodynamic pressure difference across a porous membrane that causes the process to be highly complex innature. In this paper, Fuzzy Inference Systems have been used to model and simulate the crossflow ultrafiltration of milk in a dynamic manner. The primary aim of this work was to predict permeate flux, fouling and the milk components rejection (i.e., protein, fat, lactose, and total solids), using Mamdani and Takagi-Sugeno models. The results shows that the permeate flux and the fouling resistance vary with time for each variables. Furthermore, the protein rejection is almost constant for each parameter with time; however, the rejection of other components has been raised significantly with time. The findings of this work also revealed that by applying Fuzzy Inference Systems we can predict the permeate flux and the total hydraulic resistance and compute the system errors. Furthermore, the comparison of the two models also demonstrates that the Mamdani model exhibits a better result than that of the Takagi-Sugeno model.

Evapotranspiration is one of the major components of hydrologic cycle and estimation of irrigation needs. In recent years the use of intelligent Systems for estimating hydrological phenomena has increased significantly.In this study the possibility of using Fuzzy Inference system efficiency, creating a bridge between meteorological parameters and evapotranspiration, and comparing the accuracy of reference evapotranspiration using these Systems were investigated. After analyzing the different models and different combinations of daily meteorological data, five models for estimating daily reference evapotranspiration were presented. For these models, the calculated evapotranspirationfrom Penman- Monteith-FAO equation was considered as a baseand the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination, Jacovides (t) and Sabbaghet al. (R2/t) criteria. The used data were collected from Mashhad’s meteorological synoptic station for a period of 50-years (from 1339 to 1389).From the available data, 75 percentwas used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the Fuzzy models with different input parameters as compared with Penman-Monteith-FAO and Hargreaves-Samani methods showed that Fuzzy Systems were very well able to estimate the daily reference evapotranspiration.Fuzzy model so that the highest correlation with the four input variables (r=0.99) had in mind and evaluate other parameters, the model with two parameters, temperature and relative humidity (RMSE=0.96, MBE=0.18, R2=0.95, t=22, = and R2 / t=0.04) match very well with the model Penman - Monteith - FAO had stage training. In the test phase, training phase was very similar results and the model with the second phase of temperature and relative humidity will get the best match. According to the results of this study it can be concluded that Fuzzy model approach is an appropriate method to estimatethe daily reference evapotranspiration. In addition, the Fuzzy models do not require complex calculations which are required forcombination methods.

Forecasting and monitoring droughts are important elements of optimum water resources management specifically in the metropolitan areas. Tehran as the biggest city of Iran and its five dams (Amirkabir, Lar, Latyan, Mamloo and Taleghan) are also exposed to drought hazards. In the current article, monthly meteorological data in the geographic area covering [0o, 60o] Northern latitudes and [0o, 90o] Eastern longitudes with 10×10 degree resolution including air temperature and geopotential height at 1000, 850, 700, 500 and 300 mbar levels are used as the model predictors. These data recorded in the period of 1948 to 2008 have been used to develop a model for forecasting SPI (Standardized Precipitation Index) values in Winter and Winter-Spring seasons with 2.5 and 4.5 months leadtime. This model has been calibrated using 31 years of data. Mutual Information (MI) index has been used to select the inputs (predictors) for each basin in each season. Fuzzy Inference System (FIS) has been used to formulate the model. The Fuzzy membership functions have been selected based on sensitivity analysis and engineering judgment. The results of the study have shown that geopotential height in 850 and 300 mbar levels are the best predictors for forecasting SPI values in the selected seasons. The model results have had enough accuracy to be used for forecasting SPI values in Winter and Spring seasons in Karaj and Taleghan basins and SPI values in the Winter season in Mamloo, Latyan, and Lar basins.

Trip production modeling is the process of representing the effect of various socioeconomic parameters on human trip-making behavior. Whereas making a trip appears to be related to the socioeconomic characteristics through some functional form. This paper describes the use of an advanced type of NFSs, Adaptive Network-Based Fuzzy Inference Systems (ANFIS), for modeling trip production pattern. The proposed trip production model used a four-step hybrid learning strategy. At first, by using a linear regression model, a suitable initial situation was developed for the NFS and then this initial system was trained until reaching the final model. The data used in this research were collected from 55 traffic zones (47 zones in inner regions and 8 zones in outer regions) throughout Shiraz comprehensive study in 1990. In 1990s Shiraz comprehensive study, linear regression analysis was used to model the trip production. In that study the models were developed for four major trips: work trips, school trips, shopping trips, and recreational trips. In order to be comparable with the previous practice, we also used this classification and made the new models for those four trip purposes. We also use the same variables that were used in conventional models for building the new models. The predictions of the conventional models were compared with those from the new proposed models. The results indicate that the new models have capability to represent the relationship between the trip demands and the independent variables more accurately than the conventional models.

Using Information Technology in the Insurance Industry has openned new opportunities. IT applications increased the expectation for precision and speed in insurance procedures. Risk evaluation is a key process in issuing life insurance. Insurers try to estimate risk based on stated customer information, expert opinion, medical knowledge, and existing data from previous contracts. Risk assessment is then used to determine a premium that satisfies the customer and allows for viable risk management. This paper uses the views of experts and insurance certified doctors to determine factors influencing risk of smokers. To calculate the risk rate we use a Fuzzy Inference System (FIS). The performance of FIS is tested using expert opinions on existing profiles.