فهرست مطالب behrouz ebrahimi heravi

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.

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.