بهروز ابراهیمی هروی

In this study, using Fuzzy Analysis Hierarchical Process (FAHP), the potential locations to build wind power plants in Khuzestan province have been identified. The main criteria used in the study include (in order of priority): Technical, Environmental and Economical each of which has a set of sub criteria. The set of sub criteria includes (in order of priority): wind power density, slope, height from sea surface, distance from the fault (sub criteria of Technical criteria), type of land use, distance from protected areas, distance from the river and soil types (sub criteria of Environmental criteria), distance from the power transmission grid, distance from the communication ways (roads), distance from urban areas and distance from rural areas (the sub criteria of Economic criteria). The Fuzzy Analysis Hierarchical Process used in this study has been performed according to the prioritization of the criteria and sub criteria. In this study, using the diurnal wind speed data, the maximum frequent wind speed and then wind power density were calculated. The average number of years used to calculate maximum frequency wind speed is about 20 years. The results showed that in this province, maximum frequency wind speed is between 3-7 (m/s), and wind power density is between 15.204 to 209.95 (W/m2). After collecting the other maps of the sub-criteria used in the study, the maps were prepared in Geographical Information System (GIS), and then using the results of Fuzzy Analysis Hierarchical Process, and through the functions available in Geographical Information System, maps were processed, standardized, and then fuzzified. In the end, after overlaying the maps created in the previous steps, the map of suitable areas for the wind power plants was obtained. Results showed that some parts of Southwestern, Southern and central parts of the province are the best options for building a power plant.

Abstract
One method of flood forecasting and flood control in rivers is ‘flood routing'. The relationship between precipitation and runoff and creating flooding in the region is not linear mathematical relationship which we can predict flooding in one region and such phenomena should be regarded as a model. Artificial intelligence methods such as artificial neural network and fuzzy inference system can be used as a good method in this field. In this study, using artificial neural network and fuzzy inference system, which are two types of the most widely used computational intelligence, we attempt to predict flood in Zard River. For the implementation both methods, first, the necessary data were collected and then wrong data were excluded from the data set and the data have been normalized. Modeling using artificial neural networks using MATLAB software coding was performed on data. To implement, the fuzzy inference system were used from prepared data. In this study, types of artificial neural networks structures with different number of neurons and hidden layers, number of educational courses and different functions have been performed on the data until obtaining the best structure for study area. Fuzzy inference models were implemented until the best model is chosen. Results showed that in general, fuzzy inference system have a better simulate data in the studied area and better and more accurate results than the artificial neural network model is showed. Also, values of MSE and r in fuzzy inference system and artificial neural network is equal to 0.2196, 0.0297, 0.7667 and 0.96 respectively which shows higher accuracy of fuzzy inference system for predicting floods in the our area of the study.

Temperature is one of the most significant climatic parameters and is among the main factors affecting urban planning. It is a significant determinant of the dedicated facility in the cities and can even determine the urban structure, shape and texture. Land Surface Temperature is a key indicator for energy balance at the Earth and can be used as an input for models of climate change, Urban Heat Islands, etc. The Land Surface Temperature in Metropolitan is higher than the surrounding area and this phenomenon called the Urban Heat Island. Karaj metropolitan is the third largest city and the second host city of Iran. Given this position, it is necessary to require to check the temperature in this metropolitan more than ever. In this study, the Land Surface Temperature of the Karaj Metropolitan was extracted by using Landsat8 images (4 images) in years of 2013 and 2014. Methods used to extract the Land Surface Temperature include: Mono Window, SEBAL (Surface Energy Balance Algorithm for Land), Stephan-Boltzmann, Single-Channel (developed by Munoz and Sobrino, 2003), Single-Channel (developed by Munoz et al, 2014), Split Window and Landsat Project Science Office. All used methods were compared by using Mean Absolute Error (MAE) indices and finally, the best method was chosen by its proximity to ground data. Results showed that the best method is SEBAL (Band 11) with the MAE equal to 7.98. In general, band 11 of Landsat 8 compared to band 10 has produced more reliable results for extracting Land Surface Temperature. Further analysis of the different dates indicated that the images of the second half of the year produced more accurate and reliable results compared to the first half of the year.

Land surface temperature is a key indicator of energy balance. Besides, it serves as input data for models of climate change, agriculture, meteorology, urban heat islands, choosing the best time to agricultural activities, study of volcanic and geothermal activity, and fire detection. In this study land surface temperature has been extracted by available methods using 4 images of TM and ETM+ sensors of Landsat in span years of 1985 to 2003. The methods of land lurface temperature extraction included landsat project science office, mono window, SEBAL, Stefan-Boltzmann and single channel. Because of the multiplicity of methods and the number of images used in this study using a statistical method is required. It is required to determine the most efficient extraction method of land surface temperature, which is close to the existing field data. The statistical indicator used in this study was a mean absolute error (MAE). The results indicated that Stefan-Boltzmann method was the best method for both TM and ETM+ sensors. The MAE values for TM and ETM+ were 4.3 and 6.8 respectively, which showed a minimum value among other results.