This paper aims to increase the accuracy of runoff Predictions using WAPABA model and with comparing its efficiency with SALAS model outputs, involving hydrometric runoff data of North Markazi Province-Iran, for year 2010-2011. The above mentioned models were applied and calibrated using the mentioned historical data. Then the performance of each model were evaluated using different criteria including; CE, RMSE, R2 and MAE. Also, comparison of the models Predictions with the measured data were made. Results show that the predicted runoff data using SALAS model are values more than the measured data, which can be due to the weighting values of this model. In other words in SALAS model the weights which relate to precipitation is more than other parameters. While WAPABA runoff model with the same weights, which are considered for all parameters, have a better and accurate Predictions. However, in this study for the Ghet-e-Char station of the case study WAPABA model had not suitable Predictions.

Plant development can be defined as a programmed qualitative change in plant form, which leads plant to maturity, and researchers call it as phasic development or phenology. Recognizing the timing of occurring each development stage is necessary for managing system in order to yield increment. The timing of occurring development stages depend on climate, genotype specifications and sowing date then determination of these times in different regions is difficult and it is only possible through the using of crop simulation models which can predict the timing of occurrence each development stage by integrating effective factors. The model was constructed based on linear equation of plant temperature response. In order to model evaluation two experiments were carried out in agricultural and natural resources research center of Khuzistan in 2003-2004 and 2004-2005 cropping years. Wheat development stages were determined based on Kirby and Appleyard’s scale by stereoscopic microscope and required GDD for each development stage as well. The constructed model was calibrated and run for simulation. Comparison of simulated and observed data showed that the model can strongly predict wheat development stages.

Taking into account the ambiguities and limitations of prevailing statistical models, such as losing data related to complicated and nonlinear interactions between psychological constructs and some of the assumptions like homogeneity of variances and normal distribution, the present research investigated the capability of Artificial Neural Networks model for con ducting predictive studies. A sample of 456 male senior high school students responded to the California Personality Inventory (CPI; Gaff, 1975) and Adjustment Inventory for School Students (AISS; Sinha & Singh, 1993), and was categorized into five levels of adjustment (from maladjusted to completely adjusted). Factor analysis of various combinations of personality traits suggested that some of the networks could not predict adjustment due to non conformity between the number of variables and network architectures. However, a re- vision of the architectures and repetition of new networks significantly increased the proportion of correct Predictions (the proportion of participants categorized into the indicated levels of adjustment based on AISS). The most appropriate network for predicting adjustment included a combination of the cognitive variables of flexibility, femininity, communality and tolerance.

Bankruptcy is the last part of the companies' life cycle and it affects a wide range of financial participants. Bankruptcy Prediction is important not only for financial participants but also for politicians, lawyers and researchers.As legal process of declearing bankruptcy is a time consuming and beurocratic issue, in this paper, we use the concept of bankruptcy triggering asset value to detect bankrupt companies in Tehran Stock Exchange. We use neural network as a powerfull tool for predicting bankruptcy. For training purposes, firstly back propagation method was used and then the aim became to improve the Prediction results by using genetic algorithm and particle swarm optimization. Our findings illustrated that genetic algorithm acts better than back propagation method but we do not have enough evidence to prove that generaly particle swarm optimization acts better than genetic algorithm.We also comared financial ratios' power versus market data for predicting bankruptcy. To do this, we used three groups of data, financial ratios, market data and simultaneously using both financial ratios and market data. It was discovered that market data is a better mean for predicting bankruptcy. Our findings show that using particle swarm optimization for training method and market data as an input for predicting bankruptcy could lead to 92.6% correct Prediction of bankruptcy in test sample.

There are a lot of techniques and methods for Prediction of bankruptcy; among them “Statistical methods” or econometrics techniques are more popular. As dependent variable in our study is qualitative; it is convenient to use qualitative discrete models. Mixed Logit model is one of the powerful and flexible techniques of discrete choices that allow the coefficients to be random with distribution function. Explanatory variables are financial ratios which derived from Zmijewski’s model. The sample data are from Tehran Stock Exchange’s Brokerage Companies during 2001-2008. We selected two random samples, one for estimation and another for Prediction power test. Results show that the degree of successfulness of the model is over 90 percent.