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Showing 4 results for Anfis

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%.
Mr Milad Khayat, Ms Atefeh Bosak, Dr Zahra Hejazizadeh,
Volume 0, Issue 0 (3-1921)
Abstract

Using urban growth and development modeling, it is possible to draw a development trend appropriate to the city's position according to environmental and natural factors and population attraction. The purpose of this study is to represent a model of urban development in Shushtar that can be used as a felicitous tool to analyze the complex processes of urban development. To achieve this goal, two databases consist of urban land use maps for educational, medical, habitation, etc and Landsat satellite images for major land uses such as rivers, barren areas, forests, etc were used by GIS and MATLAB software environment in three time periods 1991, 2004 and 2014. Existing urban land use maps were updated by using Landsat satellite imagery after digitization. Then the effective parameters in urban development were entered as inputs with the adaptive neuro-fuzzy inference algorithm (ANFIS). in order to evaluate the performance of the proposed method, training for 1991 and 2004 was performed. the result of urban development forecasting using the algorithm was compared with the current situation in 2014. The results are very close to reality and with an accuracy of 93.7%. The land use change map, which is the result of the change detection process, can be prepared based on multi-time remote sensing images and combined with urban user maps, and the relevant consequences examined. The use of intelligent algorithms in this research has allowed us to execute modeling with high accuracy. The results are satisfactory and this development was predicted for the coming years.
 
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Volume 16, Issue 42 (12-2016)
Abstract

  Temperature alteration plays special role as one of the most basic climate elements. So inspection of temperature alteration and anticipation has scientific- applied magnitude. In this study inspection of several cases of statistical characteristics of monthly­ average, maximum and minimum temperature and illumination of their alteration method­, temperatures predictability by ANFIS is evaluated­. Applied data is over 288 months during 24 years of statistical period since January of 1987 until December of 2010 through synoptic stations of Pars Abad, Ardebil and Khalkhal. According to equations of data lineal process­, lineal process of temperatures through all of the stations is positive and­ additive­. Lineal process gradient in minimum temperature is more than other­ maximum and average temperature. Less amplitude more variance and standard aviation and­­ data ­predictability is more. According to present article adaptive Neuro – fuzzy inference system mostly has acceptable function through anticipation of monthly minimum, maximum and average temperature in the stations of Ardebil province.


Dr Maryam Bayatvarkeshi, Ms Rojin Fasihi,
Volume 18, Issue 48 (4-2018)
Abstract

Modeling provides the studying of groundwater managers as an efficient method with the lowest cost. The purpose of this study was comparison of the numerical model, neural intelligent and geostatistical in groundwater table changes modeling. The information of Hamedan – Bahar aquifer was studied as one of the most important water sources in Hamedan province. In this study, MODFLOW numerical code in GMS software, artificial neural network (ANN) and neural – fuzzy (CANFIS) method in NeuroSolution software, wavelet-neural method in MATLAB software and geostatistical method in ArcGIS software were used. The results showed that the accuracy of methods in estimation of the groundwater table with the lowest Normal Root Mean Square Error (NRMSE) include Wavelet-ANN, CANFIS, geostatistical, ANN and numerical model, respectively. The NRMSE value in Wavelet-ANN method as optimization method was 0.11 % and in numerical model was 2.2 %. Also the correlation coefficients were 0.998 and 0.904, respectively. So application of neural combination models, specially, wavelet theory in estimated the groundwater table is most suitable than geostatistical and numerical model. Moreover, in the neural intelligent models were applied latitude, longitude and altitude as available variables in input models. The zoning results of groundwater table indicated that the decreased trend of groundwater table was from the west to the east of aquifer which was in line with the hydraulic gradient.
 


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