According to urban environment complexity and dynamism and need to targeted land use change, incorporation GIS and PSS in the form of Spatial Planning Support Systems is inevitable. The aim of this study is to develop a spatial planning support system for urban land uses change (ULCMS), such that planners can enter expert knowledge in the form of desired criteria and weights and see their influence in results. The developed system including modules for land suitability evaluation, calculation of the area of required land and land use change. Access models, neighborhood models and Multi Criteria Decision Making methods, fuzzy operators, linear regression, maximum potential and hierarchical optimization models is used in planning and implementation these modules. System practical test performed for measuring residential, commercial, industrial, agriculture and service land use changes for the year 1390 and 1395 in Shiraz city. The result shows that ULCMS help users in better understanding, showing complexity of land use system and development and improvement land management strategies for the creation of better balance between urban expansion and environmental conservation.

The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLP's neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.