جستجوی مقالات مرتبط با کلیدواژه « rbf » در نشریات گروه « جغرافیا »

The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLPchr('39')s neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.

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.

Precipitation is one of the important climatic elements and is one of the factors affecting the hydrological cycle. Temporal-spatial variations of precipitation in a watershed can have numerous effects on the engineering, management and planning of water resources. Many researchers have been studied precipitation variations. Most hydrological time series are non-stationary, trendy, or with seasonal fluctuations. Wavelet analysis is one the commonly applied approaches by researchers. It has been utilized as a common tool to break down and excavate complex, periodic, and irregular hydrological and geophysical time series, especially in recent years. On the other hand, clustering techniques can be used to identify structure in an unlabeled precipitation data set by objectively organizing data into homogeneous groups where the within-group-object dissimilarity is minimized and the between-group-object dissimilarity is maximized. Clustering analysis is similar to the homogeneity test. Considering the dynamic characteristics and non-uniform distribution of precipitation data and due to the need for identifying of homogeneous precipitation regions in water resources management, a temporal-spatial model is proposed to investigate the characteristics of precipitation. The time series of the precipitation were decomposed using MODWT mothod and the energy of sub-series was culculated. MODWT is a mathematical technique which transforms a signal into multilevel wavelet and scaling coefficients. Maximal overlap discrete wavelet transform (MODWT) is similar to the discrete wavelet transform (DWT) in that low-pass and high-pass filters are applied to the input signal at each level. However, the MODWT does not decimate the coefficients and the number of wavelet and scaling coefficients is same as the number of sample observation at every level of the transform. In other words, MODWT coefficients consider the result of a simple changing in the pyramid algorithm used in computing DWT coefficients through not down sampling the output at each scale and inserting zeros among coefficients in the scaling and wavelet filters. Clustering is the process of partitioning or grouping a given set of patterns into disjoint clusters. This is done such that patterns in the same cluster are alike and patterns belonging to two different clusters are different. k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. RBF methods have their origins in techniques for performing the exact interpolation of a set of data points in a multi-dimensional space. RBF mappings provide an interpolating function which passes exactly through every data point. In the present study, the classical and proposed methods were used to investigate the monthly rainfall characteristics of 30 stations in the southeastern of United States during 1968- 2018. In the proposed method, the time series pre-processing method including MODWT discrete wavelet transform and K-means clustering were used. At first the monthly precipitation time series of the stations were districted into several sub-series using MODWT method and db mother wavelet. Then, the energy of sub-series was calculated and used as input for K-means and RBF methods. The optimum number of clusters for the stations in both classical and proposed methods was five clusters. In order to use the data as the input of the RBF method, the correlation of the data was evaluated by variogram and covariance graphs. Then Spline with Tension method was selected in RBF model and zoning maps were drawn. In order to evaluate the temporal-spatial characteristics of monthly rainfall of 30 selected stations, two classical and proposed methods were used. At first the monthly precipitation time series of the stations were districted into several sub-series using MODWT method and db mother wavelet. Then, the energy of sub-series was calculated and used as input for K-means and RBF methods. The optimum number of clusters for the stations in both classical and proposed methods was five clusters. In order to use the data as the input of the RBF method, the correlation of the data was evaluated by variagram and covariance graphs. Based on the results of clustering and in accordance with the latitude and longitude variations of the stations, it was found that with increasing the energy of the clusters, the amount of precipitation in the stations decreased and vice versa. Silhouette coefficient of clustering in classical method was 0.3 and in proposed method was 0.8, which indicates better clustering of studied stations in the proposed method.

Dust in hazardous areas anywhere in the world is hazardous In different parts Life Organisms had. Dust Mineral Aerosols can significantly affect Earth's climate (Zhiyuan et al., 2019: 3). The prevalence of dust storms is devastating to human health and agricultural activities in Central Asia (Tiangang et al., 2019: 16). Dust it plays an important role in socio-economic development, but on the other hand, such supply can have a negative impact on the environment and the environment of the forest (Narayan et al, 2019: 4). According to the above studies, the importance of dust and the resulting hazard, in the case study, it can be admitted that the dust parameter is important for natural hazards. According to the studies, the existing methods for studying the dust that has been done so far have been general and have not adequately addressed the subject. Dust in the areas under its control anywhere in the world has had a dangers in various parts of life. Drought storms have also been growing in recent years (Mohammad Khan, 2017: 495). Drought phenomenon due to recent droughts has caused adverse biological effects and damages in agriculture, industry and transportation in the provinces of Khuzestan and other neighboring districts (Darvishi et al., 2017: 1). Today, dust is one of the common phenomena and is one of the major environmental problems in arid and semi-arid areas (Hejazi Zadeh et al., 2018: 108). The purpose of this study was to analyze the dust data first to address this issue Then, using ANFIS and RBF models, a modeling comparison was used and finally, for a better view of the dust situation for the future, in dusty regions of Iran, they were predicted.

In this study after analyzing 29-year-old dust data for 28 stations of dusty regions of Iran, they were first analyzed and then normalized, and non-normal stations were normalized. After normalizing the dust data using two new and powerful applied models for modeling and forecasting in climateology, the ANFIS and RBF models were modeled. and Then the two models were compared to accurate prediction for the future, and after training the dust data, they were predicted for the coming years. Finally, using the TOPSIS multivariate decision making model, regions are more involved with the priority hazardous dust hazard phenomenon and by utilizing ArcGIS software output data Zoned.RBF neural network model Neural networks with radial base function are widely used for nonparametric multi-dimensional functions through a limited set of educational information. Radial neural networks with a fast and comprehensive learning are very interesting and efficient, and they pay particular attention to it, Hartman et al. (1990). Gyrosy, Pogni, as well as Hartmann and Kepler, in the 1990s proved that radial-base grid networks are very powerful approximation devices, so that by having a sufficient number of hidden neurons, they can be able to approximate each function Continuously and accurately with every degree. These networks are often compared to the neural network back propagation error. The main architecture of the RBF consists of a two-tier network (Khanjani et al., 2016).ANFIS Neural Network Model In this step, it is possible to model and predict dust in the studied area using the ANFIS comparative neuro-fuzzy network model (Ansari, 2010: 29). In this study, the phenomenon of dust in a series of time of 276 months (23 × 12 276) in two ANFIS and RBF neural networks models in each station was considered. In a time series consisting of n examples x_1, 〖x〗 _ (2), ..., x_n is the next value of relation (6) of its previous value (Asghari Oskouei, 2002: 75). (6) x_k=f (〖 x〗_(k-1 ),〖 x〗_(k-2 ),…,x_(k-p ))The fuzzy system is a system based on the "conditional-result" logical rules that, using the concept of linguistic variables and fuzzy decision making process, depicts the space of input variables on the space of the output variables. The combination of fuzzy systems based on logical rules, and artificial neural network methods that enable the extraction of knowledge from numerical data, has led to the introduction of a comparative neural system inference. In Fig. 3, a sogeven fuzzy system with three inputs, one output and two laws and an equivalent ANFIS system were presented. This system has two inputs x and y and one f output.
Proximity to Ideal Mode (TOPSIS) Huang and Yun proposed TOPSIS in 1981. In this method, m options (A1, A2, ..., Am) were evaluated with n indices (C1, C2, ..., Cn) (Momeni, 2008). Solving this problem with this method was carried out in the following steps (Makvandi et al., 1391; Law and Order, 2014).

Comparison of two ANFIS and RBF neural network models According to comparisons of ANFIS and RBF neural network models, the two models were trained to predict dust. The results obtained from the training of the ANFIS neural network model at best, the RMSE value was 11.67 and the R2 value was 0/5879. But the results obtained from the training of the RBF neural network model, at best, were RMSE equal to 2.19 and the R2 value was 0.9854. By 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, then the prediction of the RBF neural network model was used.

The zoning of dust phenomena in dusty regions of Iran using TOPSIS The results of the implementation of the Topsis model, using the degree of importance of the criteria derived from the entropy method, indicate that, in terms of dust intensity, places more and less dusty for the next 14 years in dusty areas Iran, three stations of Abadan, Masjed Soleyman and Ahvaz were exposed to dust (1, 95%, and 81%), respectively, for simulated years. The northern stations of the study area including Khoy, Boroujen and Ahar showed a lower dust intensity with percentages (0.1, 0.4 and 0.6), respectively. According to TOPSIS model, south west and west of Iran were exposed to dust for simulated years.

  River sediments are transmitted in two ways: either these substances are immersed in the flow of water, and they move with water, which is called suspended sediment, and the amount of suspended sediment that passes through a section of the river at a time They call a suspended load, or they move a slip, slide, jump, to which they say the bed load. Artificial neural network is a method that is based on the simulation of human brain function for solving various problems and the input, output, and median of the neuron layers and the weights associated with the input values ​​and the bias and the stimulation function. The study area in this study is the catchment area of ​​Golrood River.
Artificial neural network is a method whichwasprovided based on the simulation of human brain function for solving various problems and formed from the input, output, and median neuron layers and the weights associated with the input values and the bias and the stimulation function. One of the features of the artificial neural network can be referred to as the calculation of a definite function, the approximation of an unknown mapping, pattern recognition, signal processing, and learning (American Society of Civil Engineers, 2000). The disadvantages of neural network methods are that it does not provide a function which can be used explicitly. Many studies have not been conducted on sedimentation using a neural network (Govindaraju&Ramachandra, 2000; Sarangi & Bhattacharya, 2005).Feedforward error back propagation neural networks with nonlinear functions (sigmoid) has high flexibility and can be very effective in approximating a function, finding the relation between input and output, and so on. In hydrology, the use of these networks is highly recommended considering the turbulence dominating runoff-sediment data, (Flood &Kartam, 1994).
Javadi and others (2015) in an article compared river sediment estimation method using two methods of artificial neural network and SVM in Iran. Then, the output of these models was compared with the experimental models and eventually the RMSE and R indices to compare these models were used. The results indicated that SVM model has better estimation than artificial neural network model. The RMSE was 75 for this model.
Semkol et al. (2016) estimated the amount of sediment in the Shiwan River in Taiwan. In this study, artificial neural network model and sediment rating curve method were used. The results showed that the MLP neural network model was able to provide an appropriate estimation of the amount of sediment with R value of 0.97 (Tfwala& Wang, 2016).Afan et al. (2016) also estimated the amount of river sediment in the Johouw River. In this study, two models of neural network RBF and FFNN were used. Finally, it was found that the FFNN model showed a much better performance than the RBF model. The R index of this study for the FFNN model was 0.92 and its RMSE was 26, while the RBF model had R value of 0.86 and a RMSE of 32 (Afan et al., 2015).
Summarizing the research history showed that the static regression methods did not have high accuracy in estimating the suspended sediment load discharge.In recent years, the focus of predictive models has also changed from linear regression to neural network models. Most researchers have been providing comparisons between different models of the neural network during these years and also, in their final modeling, tried to use the domain morphology factors in the final model to improve the accuracy of the final model. Therefore, the use of artificial neural network method and considering the dynamic behavior of sediment suspension load and considering the flow of previous days as an effective variable has been evaluated in this research.

The study area
The study area in this research is the Gelerood river basin. This area is located in Borujerd, Lorestan province, west of Iran. The basin is between longitudes 48.30 to 48.55 degrees and latitudes 33.45 to 34.00 degrees. GeleroodRiver drains waters of an area of 70 square kilometers. The average height of this basin is 2350 meters. The river originates from a number of headwaters in the village of Vanai in the west of this city and receives other branch in the western part of the Boroujerd city in the vicinity of the Chogha hill from the north.
There are 8 stations named such as Doroud-Tireh, Doroud-Marbareh, DarehTakht-Marbareh, Vanai-Gelerood, Biatoun, Rahim-Abad, Water Organization and Chogha hill in the area of Silakhor plain in Dorood-Boroujerd area. In Gelerood river basin, two stations of Vanai and Water Organization have been used to estimate the amount of river sediment. The position of these two stations in relation to the Gelerood River and its sub-basins is shown in Fig 1.
Data used
In this study, the instantaneous flow rate- instantaneous sediment statistics recorded deposition related to the period 1971 to 2002 were used. These figures include the instantaneous daily flow rate per cubic meter per second and the instantaneous daily sediment per day that were measured simultaneously. Morphological characteristics of the basin including the area, length of the river and its environment using ArcGIS software and geomorphologic parameters of the basin using natural features of the basin have been calculated based on the guidelines of Singh et al. (2009) using the ArcHydro plugin installed on the above software.

So far, different prediction models have been used to estimate the sediment volume of rivers. Some of these models estimated the amount of sediment by combining various physical parameters of the domain, climate, and even satellite image outputs. Artificial neural network models are widely used today to predict geographic models. In this study, three models of artificial neural network RBF, artificial neural network MLP and multivariate linear regression model have been used to estimate river sediment.
After calculating the RMSE and MAE indices, given the lower the rate of these indicators, the predicted value is closer to the actual values, so MLP artificial neural network models have a better accuracy than the other two other models in estimating the region's sediment. On the other hand, according to the value of the index calculated for the three models, the accuracy of the model estimation is calculated 90.44 for the MLP model, the value for this model is 0.88. After the MLP artificial neural network model, the RBF artificial network model provides better results. In this model, the value of is 0.4, which indicates the estimate accuracy of the half of the MLP model. In the third place, the multivariate linear regression model with value is 0.3.
Two neural network models of MLP and RBF were also studied in this research. The MLP model was able to estimate sediment data with a better accuracy than other models. Thus, the feasibility of using feedforward neural network models in the estimation of sediment load can be confirmed. Based on the available time series, more accurate estimates require long periods of time, as well as considering climate changes in this research can help improve the results and accurately predict the amount of sediment. On the other hand, taking into account the soil type-specific parameters of the area and the potential for water penetration in the soil for each sub-basin can be effective in improving the results. The results of this study indicated that there is a significant relationship between the amount of suspended sediment production with the number and severity of runoff events. Among the physical characteristics, the area of the basin and the length of the main river are other factors that affect the estimation of the river downstream sedimentation rate.
As well as, recurrent neural network models can be used in the following studies, given that the stations are located along the other stations. Moreover, the combination of satellite imagery data can lead to more accurate models, given the fact that this data is also available to users from past periods

Development of a prediction hydrological model based on past records depends on the proper prediction and understating of time series effective on water resources to manage and plan water reservoirs effectively. In recent years, a growing issue in this context is the application of artificial intelligence techniques in modeling, forecasting and recovery of hydrological data. This paper compares the artificial intelligence methods in predicting and recovery of time series of daily minimum and maximum temperatures and precipitation in Tangab dam station. Both series (using delay in the series) and nearby stations are used in this study to recover and predict data. Multi-layer perceptron (MLP), radial basis functions(RBF), support vector machine (SVM), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) methods have been studied. In order to evaluate the performance of these models, the mean squared error (MSE), correlation coefficient (R), variance and standard deviation of obtained data, as well as graphical diagrams have been used. The results showed the inefficiency of the models in predicting precipitation, but these can be used in recovering the precipitation and predicting temperature.

Graphical display of human phenomena, as a model a model to explore areas for spatial analysis, can portray the spatial distribution of geographical phenomena. In this regard, geostatistics and modeling can help estimate the distribution of graphic representation.The research is analytical and comparative approach. Twenty villages were used with tourist arrival statistics from April 1389 to April 1390 for estimating arrival zoning area in terms of tourism. Also, the technique of radial basis function (RBF) with five models was used for zoning.Mutual assessment models was used to assess the interaction of the semivariogram.Theresults indicated that Spline model with the coefficient of determination 0/77 and RMS = 19 is chosen as the best model for zoning. The results show 73 villages of Ramsar per year between 1000 and 1500, 46 villages between 1500 and 2000 and 42 villages between 500 and 1, 000 can absorb tourists located in the north of the city. The highest estimate tourist attraction is allocated to 2 the villages (Javaherdeh and Janet roudbar) with 4,000 to 5,000 tourists, respectively. The results indicated that the west city of Ramsar has a high potential for attracting tourists.

Load sediment transport in rivers is important according to their role in pollution, Reservoir filling, hydroelectric equipment life, Fish and other hydrological issues. Direct measurement of suspended sediment load in rivers is expensive and construction of measurement stations along the river is not possible. The equations used to estimate the sediment load are not applicable for all areas and also require long-term monitoring. In this study, to estimate daily sediment load, the Neural Fuzzy Inference System (ANFIS) is used. For this, daily discharge and suspended sediment load data of 365 days of years 2007 and 2009 of Zarine rood located in the south east of Urmia Lake is used for training and testing the Artificial Neutral Fuzzy Inference System. Southeast basin of Urmia Lake due to its hydrological and litologhical conditions have high rates of sediment production. ANFIS model is a nonlinear model and this is a great advantage. Note that the suspended sediment load also follows a linear relationship, so this model can achieve more accurate and more realistic results. This model of the multilayer Perceptron model (MLP), Neural networks, radial basis function (RBF), and sediment measures curve (SRC) has been used in these estimates. The results of ANFIS model is compared with the above models. To determine the model efficiency, the mean square error factor (RMSE) and explanation error (R2) was used and it can be seen that the ANFIS model achieves better results than the other models

The present study was done through Iran to investigate the best method of spatial interpolation to estimate the annual average temperature. Therefore, the data of 180 synoptic stations in Iran were used. Using different interpolation methods (IDW, GPI, LPI and RBF) with different powers of 1, 2, 3, 4 and 5 and models (Completely Regularized Spline, Inverse Multi-quadric, Multi-quadric, Spline with Tension, and Thin Plate Spline), spatial distribution of the average annual precipitation was investigated to recognize the best method of interpolation. The geo-statistical interpolation methods were kriging and co-kriging. Using some evaluation methods and some indices (RMSE, MBE, MAPE and R), the best method for interpolation was selected. Finally, annual temperature map in GIS was provided by the best method. The results showed that the RBF method has the least RMSE and the most correlation relationship than the others for the interpolating the annual average temperature in Iran (Multi-quadric)

Being informed of spatial distribution of heavy metals concentrations in soil for environmental pollution monitoring and maintaining the quality of the environment is necessary. This study aimed to map the spatial distribution of heavy metal contamination concentration of Manganese، Copper، Zinc and Iron in Harris agricultural soils located in the province of East Azerbaijan done. For this purpose، using systematic random sampling of 370 surface soil samples were collected at a depth of zero to 30 cm، and the concentrations of all these metals were measured. In order to modeling the spatial variation of heavy metal concentrations in the soil، the ordinary kriging and radial basis functions were applied in ArcGIS. Among the various models of ordinary kriging and radial basis functions، the lowest RMSE and MAE values and very close to zero MBE and the highest correlation coefficient R as a criterion for evaluating the best way to model the spatial distribution of heavy metals were considered. Both methods have accuracy، but based on proper the evaluation criteria for metals، Magnesium and Copper، kriging method with exponential model، for Zinc، kriging method with spherical model and for iron، RBF method with Multi-quadric function was chosen. The map after choosing the best model for each of the elements، drawing and taking into account the standard of the Institute of Soil and Water Conservation، re-classifying the elements in four classes deficiency، sufficiency، and infection. Analysis of the spatial distribution maps of heavy metal contamination showed that most of the land was sufficient range for Magnesium; for Copper was found that about 92 percent had a high class and about 8 percent of the study area was contaminated; contamination for zinc and iron wasn’t found and respectively، about 96 and 80 percent were deficient.