فهرست مطالب a. m. khorshiddoust

Potential evaporation is a component of the water cycle in nature and its prediction is a complicated and nonlinear practice. In this regard, the purpose of the present study was to provide the time-series prediction model of daily evaporation potential of Tabriz station using the two approaches of neural network and neural network- wavelet through de-noising. Daily time-series data of pan evaporation in Tabriz station consisted of 4309 days for the period of 1992-2011 were considered as the data base for running the above-mentioned models. Neural network prediction model was routed based on the three time series with 4, 7 and 10 days lag time of the normalized original signal. In the second approach, the main time series signal using Meyer wavelet was decomposed to 12 levels and the highest-frequency signal was removed as noise from the time series. Then, Neural network-wavelet model was implemented based on 36 time series with 4, 7 and 10 days delays. The evaluation of the results of these models by statistical and graphical criteria, indicated following A structure of 3-10-1 with correlation coefficient of 0.80 and mean square error of 0.125, and another structure of 36-8-1 with the correlation coefficient 0.917 and mean square error of 0.0858 were known as suitable structures in neural network and neural network-wavelet approaches, respectively.

The recognition of climate and eco-physiology of agricultural plants can be regarded as one of the most imperative factor in the production of agricultural yield development، and promotion of agricultural activities in any region. This recognition may enhance the production level in one hand، and the reduction of damage resulting from incongruous climate factors in another. The current research has been carried out aiming at the determination of potentially suitable canola production zone in Kurdestan province. Climatic data from 34 existing climate stations، which had long term records were collected and analyzed. We considered climatic factors such as precipitation required for growth period، frostiness، minimum and maximum، mean annual temperatures. We also used total amount of sunshine hours، average relative humidity of phonologic stage of canola، and physical elements including the type of land uses. This was followed by the determination of different layers for each climatic demand through the application of geographical information system (GIS) function. After appropriate evaluation of canola growth requirements using weighted classification method، the layers were categorized. As a final step، all climatic and physical layers were combined in the GIS environment by overlaying of different indices and results indicated that about 6. 8 percent of Kurdestan province is suitable for canola cultivation، including Divan Darreh، Dehgolan، and Marivan plains. The reason for such suitability is better climatic، topographic، and pedologic conditions in that area. About 39. 4 percent of the whole area is unsuitable for canola cultivation due to inappropriate climatic conditions، and unsuitable physical environment. These areas include northeast and northern parts (Obatoo district)، and parts of the central and north west of the province. The remaining areas were classified as average to appropriate occupying 29. 3 and 24. 5 percent of area، respectively.