modeling time series of dust phenomena in ahvaz
نویسندگان
چکیده
abstract: the dust is one of the weather phenomena that have negative environmental effects and consequences. the central city of ahvaz in khuzestan province is one of cities that in every year witnessed the dust in its environment. in this study, after determining that the dust phenomena data are abnormal, using the sen's nonparametric model been proceed to modeling of changes and survey the time series of dust phenomena in ahvaz during the statistical period (1951-2005).in this study, using three method of two half average, mann-kendall and sen's the analysis of dust phenomena in ahvaz has been performed. after determining the trend monthly, seasonal and annual scales proceed to determine the slop equation for dust phenomena that by using it you can predict occurrence of them in 2015. the results of this study first showed that most of dust phenomena occur in the warm period of year in ahvaz. the frequency of phenomena in the second half of statistical period has had salient increase (double), than the first period. totally, increasing trend of dust phenomena, except in january, has been 95 and 99 percent significant in annual months and seasonal scale level.
منابع مشابه
Statistical Analysis and Modeling (Forecasting) of the Temperature Time Series of Ahvaz Metropolis
Forecasting of temperature and precipitation can be efficiently used in decision making and optimal use of water resources. Studies in Iran have indicated a significant increase in annual temperature. This issue should be further researched in the Ahvaz region because it is the population hub in the southwest of Iran and the pole of irrigation networks and traditional agricultural land ...
متن کاملTime Series Modeling of Coronavirus (COVID-19) Spread in Iran
Various types of Coronaviruses are enveloped RNA viruses from the Corona-viridae family and part of the Coronavirinae subfamily. This family of viruses affects neurological, gastrointestinal, hepatic, and respiratory systems. Recently, a new memb-er of this family, named Covid-19, is moving around the world. The expansion of Covid-19 carries many risks, and its control requires strict planning ...
متن کاملa time-series analysis of the demand for life insurance in iran
با توجه به تجزیه و تحلیل داده ها ما دریافتیم که سطح درامد و تعداد نمایندگیها باتقاضای بیمه عمر رابطه مستقیم دارند و نرخ بهره و بار تکفل با تقاضای بیمه عمر رابطه عکس دارند
Modeling and prediction of time-series of monthly copper prices
One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...
متن کاملSugarcane transportation process modeling by time series approach
Sugarcane is one of the severely perishable crops that is used as raw material for white sugar production. Sucrose content of the sugarcane which is of high commercial value decreases in quality due to pre-harvest burning, high ambient temperature, kill-to-mill delays as well as microbial contaminations. Delays in sugarcane transportation are the most important risks which can affect the qualit...
متن کاملRainfall-runoff process modeling using time series transfer function
Extended Abstract 1- Introduction Nowadays, forecasting and modeling the rainfall-runoff process is essential for planning and managing water resources. Rainfall-Runoff hydrologic models provide simplified characterizations of the real-world system. A wide range of rainfall-runoff models is currently used by researchers and experts. These models are mainly developed and applied for simulation...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
تحقیقات جغرافیاییجلد ۲۸، شماره ۱۰۹، صفحات ۱۵۹-۱۷۰
کلمات کلیدی
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023