كليدواژه :
مخاطره دماي فرين , محصولات باغي و كشاورزي , شمال ايران , anfis
چكيده فارسي :
با توجه به اهميت فرين دما در بخش توليدات كشاورزي و باغي كه هر ساله خسارات قابل توجه ي را در اين بخش به بار مي آورد. براي جلوگيري از خسارات وارده در اين بخش بايد پژوهش هاي دقيق انجام گرفته و سپس با مديريت و برنامه ريزي بر اين مساله فايل آمد. هدف پژوهش حاضر بررسي و پيش بيني اثرات مخاطره اي دماي فرين ماهانه بر روي محصولات باغي و كشاورزي در نوار شمالي ايران مي باشد. براي اين منظور ابتدا داده هاي فرين دماي تمامي ايستگاه در بازه زماني 30 ساله اخذ شد و سپس با استفاده از مدل شبكه عصبي تطبيقي anfis داده ها براي خطاسنجي و پيش بيني براي 6 سال آينده انجام شد سپس براي سنجش تناسب اراضي نوار شمالي ايران براي كشت براساس داده هاي پيش بيني شده با به كارگيري دو مدل vikor و topsis اقدام شد. براساس يافته هاي پژوهش با توجه به مدل سازي خطايابي فرين دماي حداكثر كم ترين خطايابي را نسبت به حداقل دما نشان داد. در استان گلستان فرين هاي دماي حداكثر و حداقل هر دو در حالت افزايشي ضعيف مي باشد ولي استان گيلان فرين هاي دماي حداكثر و حداقل هر دو در حالت افزايشي و حداكثر دما شدت بيش تري را دارا مي باشد. استان مازندران فرين هاي دماي حداكثر و حداقل هر دو در حالت افزايشي و حداقل دما شدت بيش تري را نشان داد. هر دو مدل تصميم گيري چند متغيره تاپسيس و ويكور تلفيق داده ها، حداقل دما ايستگاه ها را به خوبي منعكس كردند ولي در دماي حداكثر در ايستگاه هاي با اولويت بدتر را به خوبي منعكس نكردند.
چكيده لاتين :
Changes, although low in temperature, change the occurrence of extreme phenomena such as droughts, heavy rainfall and storms (Varshavian et al., 2011: 169). Reducing the daily temperature variation has led to a reduction in the frequency of occurrence of temperature minima, especially in winter (Schiffinger et al., 2003, p. 51-41).
Material and method
The purpose of the present study was to investigate and predict the risk of monthly rainfed temperatures on horticultural and agricultural products in northern Iran. For this purpose, first, the data of the temperature of the whole station were obtained at a time interval of 30 years. Then, using Anfis's adaptive neural network model, data were collected for prediction and prediction for the next 6 years. Then, to measure the land suitability of the northern strip Iran was used for cultivating according to the predicted data using two models of Vikor and Topsis.
Conclusion:
In recent years, damage to agricultural and horticultural products has been increased due to temperature fluctuations. Accordingly, in this research, the prediction of the risk of monthly rainfed temperatures on horticultural and agricultural products in northern Iran has been investigated. Based on the predicted data, the minimum temperature of the Gorgan station was the lowest educational error with a value of 0.10 and at the maximum temperature, the lowest error was 0.015. Finally, in Golestan province, the maximum temperature and at least both are weak in the incremental state. Minimum and maximum temperature of Bandar Anzali station was the lowest educational error with the value (0.013, 0.10). In Gilan province, the maximum temperature peaks and at least both are in incremental conditions and the maximum temperature has a higher intensity. Be The minimum temperature of the Babolsar station was the lowest educational error with the value of 0.019 and at Ramsar maximum temperature, the lowest error was 0.016, and Mazandaran province exhibited maximum and minimum temperatures at both incremental and minimum levels Temperature showed greater intensity.
Results:
According to the findings of the study, with respect to the friction frain modeling, the maximum temperature showed the lowest defect compared to the minimum temperature. In Golestan province, the maximum temperature peaks and at least both are in weak increment, but in Gilan province, the maximum temperature peaks and at least both the maximum and maximum temperatures are higher. Mazandaran province showed maximum temperature and minimum temperature in both incremental and minimum temperature conditions.
