sara azizi ghalaty

Considering non-normative extension and too much development of cities, the lack of accurate model of consumption, increasing waste production, the most logical and most economical method for disposal of municipal waste is sanitary landfill. Given that, to identify areas prone to waste landfilling requires application of comprehensive techniques. The main objective for the present research is to select optimum place for waste sanitary landfill using Multi-Criteria techniques of FAHP(fuzzy analytic hierarchy process) and fuzzy TOPSIS(Fuzzy Technique for Order Preference by Similarity to Ideal Solution). First, determinant factors affecting landfill place from various institution were identified, collected and were standardized using geographical information system. Then by using fuzzy analytic hierarchy process, weights are calculated. After applying the weights on the corresponding criteria, using Fuzzy overlay functions combination of layers were done. Then, FTOPSIS method was used to prioritize options. Number of thirty one suitable options were calculated through running above mentioned methods. Then, one options were considered as the best as per prioritization located at south western of Shahriar city. Results showed that suitable elite options are located at better landfill compared to current one. So, this integrated approach, showed better performance than others due to taking inexact nature of phenomena.

Owing to the vital effects of future land use changes, it is necessary to predict land use growth pattern before any decision making by the authorities and decision makers. Purpose of this research is to model land use change of Kohmare Scorch plain of Shiraz province using Ordinary Least Squares regression (OLS) for pre-processing variables and Modeling using neural networks. To perform this model, the land use maps using Landsat images of the years 1987, 2000 and 2012 were prepared. Next, the validation of classified images and change detection analysis performed. Results of change detection between 1987 and 2000 with accuracy of 83% kappa, shows the greatest increase in rangeland area (4224.24 ha) and the greatest decrease was on forest area (3953.75). Considering these changes, selection of the best combination of explanatory variables, potential land use changes for year 2012 was performed using multi-layer perceptron algorithm of artificial neural network. Next, using Markov chain method the land use map for 2012 was predicted. The error matrix for modeled land use map and that of Landsat image of year 2000 is 75%. Next, the revealed changes for the second period (2000-2012) with Kappa of 88% show greatest increase for rangeland area (1807.02ha). In contrast the greatest decrease was for forest (2132.82). Considering change detection at second period, land use for year 2024 was predicted and result shows that irrigated agriculture would have the greatest change.