Showing 5 results for پیش بینی
Toba Alizadeheh, Majid Rezaie Banafsh, Gholamreza Goodarzi, Hashem Rostamzadeh,
Volume 0, Issue 0 (3-1921)
Abstract
Dust is a phenomenon that has many environmental effects in various parts of human life, including: agriculture, economy, health and so on. The purpose of this study is to investigate and predict the dust phenomenon in Kermanshah. Meteorological data with a resolution of 3 hours in the statistical period (2020-2000) of Kermanshah station was obtained from the Meteorological Organization. First, the dust data were normalized and then using ANN neural network models to predict dust concentration and ANFIS adaptive neural network to debug and predict the time series of dust occurrence in MATLAB software were debugged and predicted. Findings showed that the maximum predicted dust concentration related to the minimum fenugreek point with the highest Pearson correlation with dust was estimated to be 3451.23 μg / m3. Also, the results of time series prediction using ANFIS model showed that the linear bell membership function with grade 3, in the training and testing stages, has the most desirable input function among other membership functions. According to the forecasting models, the highest probability of maximum dust occurrence in the next 20 years in Kermanshah was 94%.
Arash Malekian, Mahro Dehbozorgi, Amir Hoshang Ehsani,
Volume 15, Issue 36 (6-2015)
Abstract
Drought is one of the most destructive natural disasters in human societies that cause irreparable impacts on agriculture, environment, society and economics. So, awareness of occurrence of droughts can be effective in reducing losses. In this study, in order to modeling and forecasting drought severity in a 37 year time period (1971-2007) in 21 meteorological stations, located in the cold semi-arid region of north-west Iran, artificial neural networks was used. The input data was annual rainfall data and annual drought precipitation index for all stations that 80% of the data (1971-2000) used for training the network and other 20% (2001-2007) used for testing it and in the next step drought severity predicted for the years 2008 to 2012 by the trained algorithm without using actual and existed data in this period. The appropriate structure for the network, based on Multi Layer Perceptron with three hidden layer, Back Propagation algorithm, Sigmoid transfer function and 10 neurons in middle layer. The results show that the artificial neural networks are well able to predict the non-linear relationship between rainfall and drought as it can simulate drought precipitation index values largely consistent with the real values with more than 97% regression and less than 5% error. So, drought can be predicted by this method in future and also it is useful in water resources management, drought management and climate change.
Rahman Zandi, Najmeh Shafiei, Ebrahim Akbari, Ali Hajizadeh Shikhanlo,
Volume 23, Issue 71 (12-2023)
Abstract
Natural parameters are one of the main determinants of the physical development of cities and settlements. In a mountainous area, the effects of these factors have become a barrier to development and can have natural hazards. In this research, it is tried to identify the optimal directions of physical development of the city of Nurabad as a relatively high region by identifying its effective factors and evaluating it. To achieve this, seven effective indicators (elevation, gradient, gradient direction, lithology, distance from the fault, distance from the waterway) were used and to assess, model, and predict areas suitable for physical development of the city from Landsat satellite imagery and Models of FUZZY-AHP and Makov and Markov's predictions have been used. So that each of the layers is fuzzy according to the fuzzy membership functions in GIS Arc 10.3 software. An analytical comparison on the appropriate areas of the city based on the critical points with the appropriate zones. Finally, the final map with the two models was classified into five classes. The results of the research showed that up to 1404 horizons of the city were developed eastwards in Although this pathway is not a suitable route, due to the existence of the main Kazeroun fault and the main waterway, the most important risk factors in the city are considered to be the best place for the development of the city of the western and southwestern regions of the region, which is 13% of the area of the basin Includes.
Saeed Jahanbakhshasl, Ali Mohammadkhorshiddoust, Fatemeh Abbsighasrik, Zahra Abbasighasrik,
Volume 24, Issue 75 (12-2024)
Abstract
Assessing and predicting future climate change is of particular importance due to its adverse effects on water resources and the natural environment, as well as its environmental, economic and social effects. Meanwhile, rainfall is also an important climatic element that causes a lot of damage in excess conditions. West Azerbaijan Province is no exception. The aim of this study is to model and predict 30 years of rainfall in West Azerbaijan province. The statistical period studied is 32 years (2019-1987). Selected stations in the province include Urmia, Piranshahr, Takab, Khoy, Sardasht, Mahabad and Mako stations. Average slider time series models, Sarima (seasonal Arima), Health Winters were used for analysis and prediction and also linear regression and Mann-Kendall test were used to determine the data trend. The results show an increasing trend of precipitation in Urmia, Piranshahr, Khoy, Sardasht and Mako stations and a decreasing trend in Takab and Mahabad stations. According to the results of comparing the models used, the Health Winters model with the least error in the absolute mean of deviations, mean squared deviations and the percentage of absolute mean errors was introduced as the best precipitation forecasting model for West Azerbaijan province.
province. [A1]
Zeinab Mokhayeri, Ebrahim Fatahi, Reza Borna,
Volume 25, Issue 76 (3-2025)
Abstract
To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). To assess future changes in rainfall, historical data from the period (1976-2005) and simulated climate data from the period (2021-2050) using two models (CM3 and CSIRO-Mk3.6) from the CMIP5 series were used. These simulations were based on four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) with a spatial resolution of 0.5 x 0.5 using the BCSD method. A mean-based (MB) strategy was employed to correct any bias in the model outputs. The results of the AOGCM models indicated that the CSIRO-Mk3.6 model had a lower error coefficient than the GFDL-CM3 model when simulating precipitation in the Large Karoun case. The average future rainfall (2021-2050) across the entire basin, compared to the average observed rainfall during the statistical period of 1976-2005, exhibited a significant decrease in both the amount and extent of precipitation in both basins for all models and scenarios. In the Great Karoun Basin, heavy rains were consistently concentrated east of the basin across all scenarios and models, with the central foothills experiencing the highest rainfall and the southwest and southeast regions receiving the lowest amounts. The findings of this study estimate rainfall to range between 83-116 mm, with the highest rainfall expected in the Greater Karoun Basin under the rcp4.5 and rcp2.6 scenarios for both models.
