جستجوی مقالات مرتبط با کلیدواژه « فازی سازی » در نشریات گروه « جغرافیا »

Today, drought is one of the most important natural hazards that has direct and indirect consequences in different parts of the planet (barqi et al., 2018: 141). Nevertheless, drought is one of the environmental events and an integral part of climate fluctuations. This phenomenon is one of the main characteristics and recurrence of different climates (Hejazizadeh and Javizadeh, 2019: 251 ) The purpose of this study was to analyze the temperature and precipitation data first, then, using ANFIS and RBF model model, a model-comparative model was developed and the new S.M.S drought index was designed. Finally, in order to better visibility of the drought situation for the future, in areas affected by drought in southern regions of Iran were predicted.

In this study, after the 29-year data on temperature and precipitation data for 28 stations in the drought areas of Iran, the data were first analyzed, then normalized and the stations with abnormal data were normalized. After normalizing the temperature and precipitation data, using two new and powerful applied models for modeling and forecasting in climateology, namely ANFIS and RBF neural network models, were modeled. Then, the two models were compared for accurate prediction for the future, and after training three SPI, MCZI, and SET data, they predicted a new drought index called SMS, for the coming years, and Finally, using the TOPSIS multivariate decision making model, the areas most involved with the drought risk phenomenon were prioritized and ArcGIS software delimited the output data.

Drought is a natural hazard, which is evident gradually over the long years due to climate change in its affected areas. Which effects itself on different parts of the living environment of living organisms. One of these areas in Southwest Asia is Iran, which in recent years has shown drought in its regions, especially the southern regions of high intensity. According to the comparisons of ANFIS and RBF neural network models, the two models were able to predict the drought. The results obtained from the training of the ANFIS neural network model were, at best, RMSE values equal to 9.64 and R2 values equal to 0.0681. But the results obtained from the training of the RBF neural network model were, at best, RMSE equal to 1.15 and the R2 value was 0.9961By comparing these two models, it was finally concluded that the performance of the RBF neural network model was better. According to the modeling and the results obtained from the comparison of the models, the accuracy and reliability of the RBF neural network model was confirmed for prediction. The prediction of the RBF neural network model was used. Modeling and predicting droughts in 28 synoptic stations in southern regions of Iran were compared using SMS fuzzy new index and ANFIS, RBF models. The methods used in this study, in most studies, Monitoring, Modeling and Comparison. Among these, studies have been done in Iran: Zeinali and Safarian-zengir (2017) by studying drought monitoring in the Lake Urmia basin using Fuzzy index; Babayan et al. (2018), the monthly forecast of drought in the southwestern basin of the country Using the CFSv.2 model, they confirmed the model's acceptable accuracy. However, with all the comparisons of different models and indices in these researches, the new SMS fuzzy index and two ANFIS and RBF models used in this study, namely, modeling and predicting the natural hazards of drought In the southern regions of Iran, it has an acceptable performance.

The purpose of this study was to model and investigate the possibility of drought prediction in the southern half of Iran. To do this, the fuzzyization of the SMS index, based on the three SPI, MCZI, SET, comparisons and the results of two new simulation models in Climatology, the ANFIS and RBF neural network models, as well as the TOPSIS multivariate decision making model. The results showed that the S.M.S index reflected the three SPI, MCZI, and SET indices. Comparing two models of ANFIS and RBF neural networks, the RBF model is more accurate than the ANFIS model. As a result, for prediction of drought, RBF model was used for future years. The results showed that the S.M.S index reflected the three SPI, MCZI, and SET indices. Comparing two models of ANFIS and RBF neural networks, the RBF model is more accurate than the ANFIS model. As a result, for prediction of drought, RBF model was used for future years. The accuracy of the RBF model at best was RMSE equal to 1.15 and the R2 value was 0.99 The results of the fuzzification of the SMS index showed that the central and western parts of the study areas such as Kerman, Yasuj and Abadan, with the SMS drought percentage (0.99, 0.97 and 0.89), respectively, were higher Exposed to the drought.

Introduction :

Drought is one of the most important natural disasters affecting agriculture and water resources, and its abundance is extremely high in arid and semi-arid regions (Shamsenya et al., 2008: 165). Drought is a natural phenomenon that has a complex process due to the interactions of various meteorological factors and occurs in all climatic conditions and in all regions of the planet (Samandianfard & Asadi, 2017). According to the domestic and foreign studies, many researchers have conducted research on drought monitoring and prediction, but the research that can show the drought phenomenon more accurately with the future vision is not takenhas not been conducted if both do not cover the issue adequately. According to the researchers, this study was conducted to model, monitor and predict drought with the new method in Iran in this study.

In this study, drought modelling in Iran was carried out using climatic data of rainfall, temperature, sunshine, relative humidity and wind speed monthly (for 6 and 12 months scale) for the period of 29 years (1990-2018). At 30 stations using the new TIBI architecture model, a fuzzy set of four indicators (SET, SPI, SEB, and MCZI) valid in the World Meteorological Organization was used. For modelling the new TIBI index, the climatic data were first normalized, then four indices (SET, SPI, SEB, and MCZI) were calculated separately and the fuzzy modelling of the four indices was performed in the Matlab software and eventually to prioritize the drought-affected areas, the multivariate decision-making model, TOPSIS was used.

In order to investigate the effect of drought fluctuations in drought conditions of stations, it is possible to determine the changes in the indicators (SET, SPI, SEB, and MCZI) in the TIBI index analysis. Considering the large number of stations studied, For better understanding, only the drought series diagrams were presented at Bojnourd station on two 6 and 12 month scales (Figures 7 and 8),, (in the mentioned figures, the red arrow shows the drought margin at a 6-month scale with a value of 0.44 and greater, and a value of 0.76 and greater within the 12-month scale. The analysis of these forms shows that at the 6-year and 12-month scale at Bojnourd station, the amount of evapotranspiration was similar in drought conditions, which decreased from April 1994 to February 1999, and after this month an increase was observed if the impact of rainfall on a 6-month scale is weaker than the 12-month scale. It means that from May 1993 to November 1997, an increasing trend followed by the same pattern, and the indicators (SET, SPI, SEB, and MCZI) affect the TIBI index and show some trends, indicating that the new TIBI fuzzy index reflects the four indicators well. The T.I.B.I index at the 6-month scale shows a sharper shape than the scale 12.Prioritization of the stations involved in drought in Iran was analyzed using the TOPSIS model. The results of the TOPSIS model implementation using the degree of importance of the criteria derived from the entropy method indicate that, in terms of drought, more and fewer places are involved with drought by combining the two 6 and 12-month scale. According to the TOPSIS multivariate decision-making model, it was determined that the three stations most affected by drought based on the TOPSIS model were Bandar Abbas, Ahvaz and Bushehr, respectively, in the south and southwest regions of Iran with priority points of score (1, 0.78, and 0.62 respectively), and the three stations of Gorgan, Shahrekord and Orumieh in the northern and western parts of Iran with the scores of 0.026, 0.033 and 0.035 had lower priorities for drought response, respectively (Table 6) and (Figure 11).

Drought is a natural disaster that is gradually evolving under the influence of climatic anomalies over a long period of time. In recent years, various parts of the Middle East have faced drought, including those regions of Iran in Southwest Asia. In this study, drought phenomenon was assessed at 6 and 12 months using the new fuzzy index T.I.B.I. The results of the research showed that the total frequency of droughts in the 12-month scale was more than 6 months but the severity of a 6-month-old drought is more than 12 months old. On a 12-month scale, the number of drought repetitions is more than 6 months. Drought persistence was higher at 12-month scale, droughts were shorter at short-term and affected by temperature parameter. However, the intensity of drought over a long period of time had a slower response to rainfall changes. The highest percentage of drought incidence in scale of 6 months; Bandar Abbas, Bushehr, Ahvaz and Zahedan stations in the southern half of the study area respectively with the of drought (16.62, 11.24, 14.13 and 62.6 and the lowest in the 6-month scale were Urmia and Ardebil stations, with the percentages of 1.10 and 1.88, Ilam and Yasuj with the drought frequency of 1.61 and 2.01, Rasht and Gorgan, with a high percentage of drought frequency (1.26 and 0.87) in the northern and western part of Iran. The highest percentage of drought occurrence in scale 12; Bandar Abbas and Bushehr stations respectively with drought frequency of 24.30 and 14.83, Ahvaz with drought severity of 18.47, Kerman with 6.74 percent of drought frequencies in the south and southwest of Iran and the lowest in the 6-month scale; stations of Birjand (1.70), Bojnurd (66.6), Urmia (1.17), and Tabriz (66.2) in the northwest of Iran, Rasht (0.58), Sari (0.78) in the northern part of Iran.

This study aimed to identify factors leading to slope instability, Maps preparation, determine potential areas mass movements and risk zoning in the Upper Basin Komanaj Chay. That is one of the important basins in the northern city of Tabriz, by Using logistic regression models and artificial neural network done. This basin due to topography, tectonics, geology, stratigraphy, and the climate is prone to a variety of slope instability, this phenomenon always occurs. According to the study variables such as altitude, slope, aspect, type of formation, distance to fault, distance from the river, land use, distance from the road, as the independent variable And distribution of unstable slopes as the dependent variable using logistic regression models and artificial neural network was analyzed .The results showed that the most important factors in the occurrence of slope instability in the basin are as follows: Elevation, distance from the river, lithology, faults, slope and aspect More than 50 percent of instability range in height from 1850 to 1520 in the study area dip 32-17 degrees, at a distance of 200 meters from the canal and 500 meters from the fault occurred. According to the results of a very high percentage of areas the risk of neural network and logistic regression models respectively 5.6 and 8.3 percent is the mainly areas close to the drainage network which includes the lithology of these areas are located in areas with lower resistance. Statistical methods logistics showed a lot of reflects of faults and lithology in this areas is based Landslide. Evaluation ROC indicator showed that the model was assessed using logistic regression model is 0.894 and neural network models is 0.826. In fact, both models show a high value and suggest that mass movement and slope instabilities observed a strong relationship with probability values derived from logistic regression models and artificial neural network model. The results of this study can be useful risk management slope instabilities and control is deteriorating factors.

In this study, using Fuzzy Analysis Hierarchical Process (FAHP), the potential locations to build wind power plants in Khuzestan province have been identified. The main criteria used in the study include (in order of priority): Technical, Environmental and Economical each of which has a set of sub criteria. The set of sub criteria includes (in order of priority): wind power density, slope, height from sea surface, distance from the fault (sub criteria of Technical criteria), type of land use, distance from protected areas, distance from the river and soil types (sub criteria of Environmental criteria), distance from the power transmission grid, distance from the communication ways (roads), distance from urban areas and distance from rural areas (the sub criteria of Economic criteria). The Fuzzy Analysis Hierarchical Process used in this study has been performed according to the prioritization of the criteria and sub criteria. In this study, using the diurnal wind speed data, the maximum frequent wind speed and then wind power density were calculated. The average number of years used to calculate maximum frequency wind speed is about 20 years. The results showed that in this province, maximum frequency wind speed is between 3-7 (m/s), and wind power density is between 15.204 to 209.95 (W/m2). After collecting the other maps of the sub-criteria used in the study, the maps were prepared in Geographical Information System (GIS), and then using the results of Fuzzy Analysis Hierarchical Process, and through the functions available in Geographical Information System, maps were processed, standardized, and then fuzzified. In the end, after overlaying the maps created in the previous steps, the map of suitable areas for the wind power plants was obtained. Results showed that some parts of Southwestern, Southern and central parts of the province are the best options for building a power plant.

Slope instabilities are considered as one of the major dangers to human activities which occurred in natural slopes and the slopes made by humans. This study aimed to identify the factors affecting the occurrence of slope instability using logistic regression models and artificial neural network in Ahar Sattar khan dam basin. The results of statistical models to determine the potential areas of instability and ultimately create a hazard zonation map for the study area. In this regard, the most important factors in landslides such as slope, aspect, elevation, rainfall, distance from the road, fault and drainage, land use and exploration and peculiarities of each of them were identified. Standardization base of histogram model is by cutting the classes of each layer with occurred landslides. The models show that the very high-risk area in the neural network and logistic regression are 724 and 5/56 per cent respectively which cover the areas close to Sattar khan dam, mainly including the lithology of these areas located in the regions with lower resistance. Besides, Statistical methods Logistic prove that faults and lithology have an immense impact on the occurrence of landslide in this area. The ROC index value for neural network and logistic regression models are 0/85 and 0/81. So it can be said neural network model for zoning of landslides is more efficient, so any planning and construction must be compatible with the conditions of geomorphology and geology of the area leading to as least human and financial losses as possible.

Given the high importance of housing for human, predictin of locations for temporary housing of the people damaged from the events is inevitable. Meybod city with its historical background and a narrow passage network is placed in a zone with relatively high degree of risk. One of the most important duties of the planners of crisis management part is serious predictions for emergency accommodation, because the affected people with no conventional housing are on the verge of serious physical, mental and psychic serious damages.with respect to the location nature of this subject and fuzzy nature of parameters and facing with a great deal of parameters for decision making, it is possible to use a combination of GIS and Fuzzy logic together with multi criteria decision making methods for making an optimum decision which leads to specifying the promising areas for temporary housing. The required statistics and information were gathered by library and field method and satellite images of the under study area were updated in the map of Meybod city. The aim of this study is the presentation of an effective, flexible, logical and strong method for locating temporary housing by using GIS. In this study, firstly the most effective parameters were selected and by using hierarchical process and Fuzzy logic, the places for temporary housing were obtained. Then, the recommended locations were compared and prioritized and the considered sites were ranked and the best one was specified among the selected options. In AHP method, six places with relative importance of " very appropriate" and total area of 347,071 square meters and six places with relative importance of " appropriate" and total area of384,369 square meters were determined as the best locations for temporary housing. Baharan park and bare lands of the south east of the Governory stadium were selected with relative importance of " very appropriate" in Fuzzy method.