Showing 9 results for Fuzzy Logic
H Pourghasemi, H Moradi, M Mohammadi, M Mahdavifar,
Volume 12, Issue 46 (1-2009)
Abstract
One of our first activities in natural resources management and development programs is to acquire knowledge on Landslide Susceptible areas. The aim of this research is landslide hazard zonation in some part of Haraz watershed between Vana village and Emam zadeh Ali, using fuzzy membership functions and fuzzy operators. At first landslide points were recognized using arial photography and field studies. Afterwards, the inventory map of landslide was prepared. Then, each effective element in landslide such as: slope, aspect, elevation, lithology, landuse, distance of road, distance of drainage, distance of fault and precipitation map was prepared in GIS environment.These data were saved in raster and vector format in ILWIS software and used for analysis with theory of fuzzy sets. Fuzzy analysis was made by IDRISI software, after assigning value and fuzzy membership functions. In this research we used different fuzzy operators such as (And, Or, Sum, Product and Gamma). Results showed Gamma fuzzy operator had the best accuracy ( ) in making landslide susceptibility map in study area.
A. Mahdavi , M. R. Nouri Emamzadei, R. Mahdavi Najafabadi, S. H. Tabatabaei,
Volume 15, Issue 56 (7-2011)
Abstract
In recent years, surface water resources in Chaharmahal and Bakhtiari province have decreased and groundwater level has fallen down. Thus, groundwater must be strengthened by surface water resources. The objective of this search was identification of artificial recharge sites thorough Fuzzy Logic in Shahrekord Basin. Effective factors in ground water recharge such as slope, infiltration rate, thickness of unsaturated zone, surface water EC, land use and stream network were determined. They were classified, weighted in software packages Arc View 3.2a and Arc GIS 9.3 and they were integrated using multiplying operator in fuzzy model. The obtained results showed 4.79 % of all areas are suitable and 17.94 % are somewhat suitable in this method. To include the effect of land use parameter, it was overlaid on the final maps, showing a decrease in suitable areas up to 1/3. Generally about 30 points were introduced with priorities A, B, AB as having potential for artificial recharge.
M. Bagheri Bodaghabadi, M. H. Saleh, I. Esfandiarpoor Borujeni, J. Mohammadi, A. Karimi Karouyeh, N. Toomanian,
Volume 16, Issue 61 (10-2012)
Abstract
Discrete Models of Spatial Variability (DMSV) have limitations for soil identification in traditional soil maps. New approaches, generally called digital soil mapping (DSM), using continuous methods (CMSV), try to predict soil classes or soil properties based on easily-available environmental variables. The objective of this study was to map the soil classes of the Borujen area, Chaharmahal-va-Bakhtiari province, using digital elevation model (DEM) and its attributes and Soil-Land Inference Model (SoLIM). To do this, eighteen terrain attributes were derived from the DEM of the area. The primary analysis showed seven attributes are the most important derivatives. These derivatives as well as three dominant soil subgroups and seven soil families of the region (41 profiles from 125 profiles) were used to construct the input data matrix of the model. Then, output fuzzy soil maps of SoLIM were converted to polygonal soil map, using ArcGIS. Results showed that different combinations of DEM attributes have different accuracy rates for soil prediction. The accuracy of the interpolation was twice that of the extrapolation. Although SoLIM had an acceptable accuracy for soil nomination, and identification of soil map units’ types, it did not have enough accuracy for the location of soil classes. It seems that using other data like parent material and geomorphic surface maps will increase the accuracy of the model prediction.
M. Jabarifar, B. Khalili Moghadam, M. Bodaghabadi Bagheri,
Volume 20, Issue 75 (5-2016)
Abstract
Splash erosion is one of the most important water erosion types, causing initiation of other types of water erosion. The objective of this study is to model the splash erosion using fuzzy logic approach in part of northern Karoon basin. The major land usage in the area are irrigated farming, dry land farming, pasture and degraded pasture. For the purposes of this study, soil properties including organic matter; CaCO3; surface shear strength (SSS); particle size distribution; mean weight diameter (MWD) and soil splash erosion were measured under four different slope conditions (S:%) and rainfall intensity (RI:mm.h-1): 5-50, 5-80, 15-50, 15-80, respectively, using multiple splash sets (MSS) at 80 different locations. Splash erosion was modeled based on combinational rule of inference under five conditions for selection of different operators. The efficiency of the models was evaluated using mean square error (MSE) between observed and estimated values. Results revealed that all models are capable of predicting splash erosion. Also slope, rainfall intensity, MWD, SSS, fine sand and coarse silt attributes were found to be appropriately and precisely using splash erosion.
M. Ayoubi, R. Sokouti, M. J. Malakouti,
Volume 20, Issue 76 (8-2016)
Abstract
This study is aimed to investigate the spatial variation of soil macronutrients such as phosphorus, potassium and organic matter using different methods of Geostatistics and Geostatistical method combined with Fuzzy logic to estimate the values of this element to provide a spatial distribution map for the proper distribution of fertilizer in the plain of Uremia. Spatial variations in soil nutrients are natural but knowing these changes for careful planning and management particularly in the agricultural lands is simply inevitable. This information is necessary to increase the profitability and sustainable agricultural management. Therefore, to estimate the changes in the elements of places not sampled, the Kriging, Fuzzy Kriging, Cokriging and Inverse Distance Weighting methods have been used in GS +. In this study, Matlab 9.1 software was used to fuzzification of the data and GIS was used for the final mapping. The parameters MAE, MBE and RMSE were used to compare these methods. The results showed that the combined method of Fuzzy Geostatistic with the mean absolute error values for the elements phosphorus, potassium and organic matter i.e. 0.17, 0.18 and 0.18, respectively, is recognized as the preferred method based on which zoning maps have been prepared for P, K and OC in GIS.
E. Mehrabi Gohari, H. R. Matinfar, R. Taghizadeh,
Volume 21, Issue 3 (11-2017)
Abstract
Typical routine surveys of soils are relatively expensive in terms of time and cost and due to the fact that maps have been traditionally developed and considering their dependence on experts' opinions, updating maps is time consuming and sometimes not economical as well. While soil digital mapping, using soil various models - the Landscape, leads to simplification of the complexity found in natural soil systems and provides users with quick and inexpensive updates. In fact, the model represents a simplified form of the complex relationships between the soil and the land. This study aims to consider inferential model Soil-Land (SOLIM) in mapping and estimating soil classes in Aran area, Isfahan province. For this purpose, the SOLIM model inputs are digital geological and environmental layers of digital elevation model (DEM) including elevation, slope in percent, slop direction, curvature of the earth's surface, wetness indicator, flow direction, flow accumulation, and satellite images of Landsat 8. The seven subcategory of soil in the study area are input data of SOLIM model. Then fuzzy maps were prepared for seven types of soil and final maps of soil prediction were created by non-fuzzy action. Results showed that the SOLIM using environment variables has very high ability to separate soil types in greater detail and soils with different parent materials, geology, climate and vegetation can be separated from each other by this model with a high degree of accuracy. Comparing error matrix shows that the overall accuracy of the map derived from the model SOLIM is 92.36%.
A. Fariabi, H. Matinfar,
Volume 22, Issue 3 (11-2018)
Abstract
One of the problems with the traditional mapping of soils is the expert’s opinion, it time-consuming and timely preparation, and the updating of the maps. While digital soil mapping, using different soil-earth models leads to the simplification of the complexity of the soil system. The purpose of this study was to investigate Soil-Environment Inference (SIE) in soil mapping with an emphasis on using the expert knowledge and fuzzy logic. For this purpose, the digital layer of geology and peripheral layers were derived from a digital elevation model including elevation, slope, and curvature of the ground surface, and auxiliary index, which comprised the input data of the SIE model. Then, the fuzzy maps prepared for the five soil types and the final map of soil prediction were created by hardening. The results showed that the SIE model, which used environmental variables, had a high ability to isolate soil types with more detailed compositions of soils with different maternal materials. The comparison of the error matrix showed that the overall accuracy of the derived map of the SIE model was equal to 75%, and the matching of the digital mapping results with conventional mapping accounted for 74.71% of the results. The difference in the compliance rate could be attributed to the difference in the nature of the two methods.
S. Ebrahimiyan, M. Nohtani, H. Sadeghi Mazidi, E. Soheili,
Volume 26, Issue 1 (5-2022)
Abstract
The basis of land management is the geomorphological zoning of the land surface, which is determined based on the same geomorphological characteristics of the zoning. Ground zoning detect land features by basic surface features such as height, slope, and slope direction. In this study, quantitative zoning of the land surface with small coefficients to the surface has been used to identify suitable areas for artificial feeding in the mountainous region of Gohar and Dasht-e Gorbayegan in Fars province. Quantitative zoning of the land surface has been performed by Evans-Shri coefficients due to the accurate determination and separation of types, faces, and surface features of the land has an important role in determining the exact land use. In this research basic models included linear, circular, and divergent models. These basic models with the dimensions of the final windows are ranked second in the MATLAB software to the level the ground is fitted to determine the fit of these models, the parameter of total squared difference has been used. In addition, the suitability of the study area for flood distribution in five different classes was determined using fuzzy logic. The most suitable areas for feeding downstream of the cones had five parameters with a maximum score of 20. The inappropriate class related to the lower plains of alluvial fans have a minimum score of five input classes in fuzzy logic, which is equal to zero.
A. Shahbaee Kotenaee, H. Asakereh,
Volume 26, Issue 4 (3-2023)
Abstract
Precipitation is one of the most significant climatic parameters; its distribution and values in different areas is the result of complex linear and nonlinear relationships between atmospheric elements-climatic processes and the spatial structure of the earth's surface environment. Classification of data and placing them in small and homogeneous zones can be effective in improving the understanding of these complex relationships and their results. In the present study, zoning and analyzing the distribution of rainfall in Iran concerning environmental factors was performed using the annual precipitation data of 3423 synoptic, climatological, and gauge stations in the country during the period from 1961 to 2015 and the altitude, slope, aspect, and station density data. After standardization and preparation of the data matrix, the optimal number of clusters was determined and the data set was entered into the neural-fuzzy network model (ANFIS-FCM). The results showed that the values of R2 and MAE indices were 0.76 and 0.23, respectively which indicate the appropriate accuracy of the model. It was also found that in the four output zones of the model, environmental factors have a high impact on the spatial distribution of precipitation. In the first and third zones, the combination of high altitude and slope factors along with geographical proximity to precipitation systems has caused the average annual rainfall in these zones to be 318 and 181 mm, respectively. The mean annual rainfall has decreased to about 100 mm by the weakening of the role of environmental factors in the second and fourth clusters.
