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

The city of Tabriz, which has the fastest urban growth in the northwest of the country, is one of the largest cities in Iran in terms of population, economic activity, industry and transportation options. Public transportation and industry combustion and lack of proper filtration of these industries, such as thermal power plants, has led to increased air pollution in the city. For this purpose, the present study tries to use input variables (distance from industrial centers, humidity, temperature, population density, distance from commercial centers, distance from bus stations, distance from educational centers, vegetation changes, distance from free Roads, building density, wind direction, carbon dioxide and carbon monoxide) to assess air pollution using artificial neural networks in the metropolis of Tabriz. In the present study, the independent variables affecting the distribution of pollution probability in two models of multilayer perceptron neural network (MLP) and linear regression were tried to be defined by defining measures in urban management and influencing and planning the mentioned variables. Improve pollution control.The results show that the major pollutants are mostly suspended particles (PM10), gas (CO2), (SO2) and (NOx).The dispersion of airborne particles is mostly due to vehicle traffic, industrial activities, fuel combustion of diesel engines and construction and the need to generate more electricity.-The activities of thermal power plants, Tabriz refinery and domestic and commercial heating systems are also among the factors producing SO2 and the highest CO2 production is related to the fuel of gasoline-burning vehicles. The intensity of the increase in the amount of this pollutant in all selected stations in the autumn and winter seasons is much higher, so that in these seasons the pollutants reach more than twice the allowable level.The share of Tabriz air pollutants can be divided into three general categories, the most important of which is the thermal power plant and transportation.

Estimation of the sediment load in rivers is one of the important issues in studies related to water quality and transport of pollutants, construction and operation of hydraulic structures, maintenance of reservoirs, water transmission networks, and water resources management. An accurate understanding of the sedimentation of a watershed can provide a correct understanding of soil erosion and its consequences. Since sediment changes in the river are often a function of flow discharge changes; therefore, methods of measuring suspended sediment load based on the suspended sediment concentration and flow discharge will be useful in estimating the amount of sediment load. The sediment rating curve is one of the methods that is based on flow discharge and sediment discharge and expresses the relationship between these two parameters in the form of power regression (Eq 1). (1) where Qs  is the suspended sediment discharge (in tons per day), Qw is the flow discharge (in cubic meters per second), and a and b are the coefficients of the equation . Rating curves can be drawn in different ways according to the way of data separation. Among these methods, we can refer to one-line, multi-line, mean of categories, seasonal, monthly, annual models, etc. The presence of bias in the sediment discharge relationship makes this relationship unable to show the exact sediment concentration in different flow discharges. This bias causes the amount of sediment to be underestimated. Various researchers have proposed some statistical correction factors to achieve the minimum error, which are applied in the sediment rating equation. In this research, in order to increase the accuracy of sediment estimation by using a sediment rating curve, at first, different types of rating curves were drawn for the station and, finally, correction factors consisting of QMLE, Smearing, MVUE, and (Beta) β were applied for the selected curve. Also, an attempt was made to separate the data into three categories of dry, normal and wet by using the percentage of normal precipitation and to draw the sediment rating curve for each. At the end, the results obtained from the statistical model (SRC) were compared with artificial intelligence models including two models of multilayer perceptron (MLP) and radial basis set (RBF) neural networks.

In this research, the flow and sediment discharge data from 1350 to 1397 for the Jelogir station in Khuzestan province located on the main Karkhe River were prepared from the Khuzestan Regional Water Organization. Sediment rating curve models, including common linear curve (USBR), mean of categories, monthly, seasonal, bilinear, trilinear, dry, normal and wet models were drawn for the station. Then, for the drawn curves, evaluation criteria including RMSE, ME and P were checked and, finally, by ranking these criteria, the curve with the least error was selected. In determining the rank of each model, the values of the evaluation indices were compared with each other. In this way, the closest P and ME index value to 1 and the closest RMSE index value to zero, which indicates the least difference between the estimated and observed sediment values, was assigned the first rank. In order to investigate the effect of skew correction coefficients on the accuracy of sediment rating curves, coefficients including MVUE, FAO, QMLE and Smearing were applied on the rating curve which was selected as the optimal model in the previous step. The data were processed using neural network models. For this purpose, different structures of neural networks with different layers, neurons and functions were investigated through trial and error.

According to the obtained results, the mean categories method has the highest correlation coefficient (0.85). The RMSE in rainy and flooding months (April and March) and also in high flow discharge rates (in bilinear, and trilinear models, at flow discharge greater than 201 and 114 cubic meters per second, respectively), has allocated the largest amount. The lowest value of RMSE is related to the months of August and September, which is reasonable due to the lack of rainfall and flooding in these months and as a result of low erosion of sediments. According to the ranking values, the periods of low rainfall, including summer and July, August and September are in the first ranks, and as a result, the sediment rating curve has more accuracy in estimating sediments. Finally, the rating curve of August, which has the lowest total ranking value, was chosen as the optimal curve. According to the ranking of the correction coefficients, it can be seen that the sediment rating curve without applying the correction coefficients (the highest rank) has the highest amount of error and by applying the coefficients, the error of sediment flow estimation can be reduced. Finally, MVUE with the lowest total ranking was chosen as the optimal correction coefficient, and by applying it, the accuracy of the model in estimating the sediment discharge increases. In the neural network model, Lunberg-Marquardt optimization algorithm was used and the number of hidden layer neurons in the best MLP and RBF structure was obtained as 5 and 6, respectively. Also, the activator function in the hidden layer in MLP was selected as sigmoid tangent and Gaussian function in RBF. The results show that by using neural networks of multilayer perceptron, it is possible to predict the amount of suspended sediment with higher accuracy, and the accuracy of the results obtained from the artificial neural network method is far higher than the accuracy of the rating curve method with and without data classification. According to the results, the MLP model has shown a lower error value than the RBF radial base model.

In this article, in order to estimate the suspended sediment in the Jelogir station, the data were separated into different forms and the sediment rating curves were drawn into linear curve (USBR), mean of categories, monthly, seasonal, dry, normal, wet, bilinear, and trilinear types. The obtained results showed that the accuracy of the relationship obtained for the classification of data based on August (R2= 0.785) and the total rating of 9 (the lowest value) was more than the other models. And at high flow discharge, the accuracy of the models decreases. It was found that the correction coefficients are effective in increasing the accuracy of the models, and the lowest amount of error for the optimal model is obtained by using MVUE. Comparing the results of statistical methods and neural networks showed that neural network models are more accurate in estimating daily sediment. The better performance of artificial neural networks compared to statistical methods can be expressed in the nonlinear approximation capability of neural networks.

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.

The growth of the urban population has been led to increasing of the urban spaces and growth of the city size. as a result of further construction and alteration of the land available to the benefit of its built-up spaces. Special location the city of Urmia at proximity of the Urmia lake and unfavorable condition of this lake reveals the necessity of the proper landuse planning at this city. One of the required tools for proper planning in this field is the use of remote sensing techniques. The present study aims to evaluate these changes (period 1989-2015) and predict its future trend. SVM and neural network methods are used to evaluate changes in 5 classes Due to the high accuracy of the classification of the neural network, the results of this classification have been used to predict changes for the 2045 horizon. Land constructed in 1989 is 7469.1 hectares, reaching 9217.3 and 94366.9 hectares in 2002 and 2015 respectively, and by2045, according to the prediction model, the neural network is equal to 22449.6 hectares, which is built on lands 13012.7 Shows hectares of increase. The determination coefficient (0.73) and rock curve (82.55%) also indicate the high accuracy of the neural network model to predict urban development changes. The Heldern method results shows that all of these constructions are not based on the real needs of the city And the sparse phenomenon has happened.

Transportation has always been one of the most important factors, which affects the structure of cities. However, the development of a variety of motor vehicles and ever-increasing population changes, especially in the last century, have become one of the main urbanization problems. Considering the volume of inland traffic in Kermanshah City, the standard site selection of bus stations is one of the most important parameters that improves the proper operation of public transportation in the stations. This includes the reducing of Get-of and the Get-in time of the users as well as making less traffic jam of other vehicles. In this study, the multi-layer perceptron with the error-back propagation algorithm as one of the most important structures of the neural networks is used. Afterward, 15 map layers were used as the effective data to select bus station locations. In addition, 500 layers were prepared as network teaching points and 10 intermediate layers were determined. Then, the optimized site zones were obtained by implementing a network of susceptible zones. Eventually, the stations were able to be constructed by identifying the passages in the susceptible zones. It was also observed that susceptible passages are located near demographic, downtown, cultural and commercial centers.

  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

The rapid growth of urbanization and urban development, especially in developing countries, needs to be understood scientifically and efficiently by the complex patterns and processes of urban growth. In this study, the Multilayer Perceptron Neural Network (MLP) with the Levenberg-Marquardt Learning Algorithm for Kermanshah Urban Development Potential was used. Effective data in urban development were identified as inputs to the network. These layers are subdivided into three groups: socio-economic, land use and biophysical, including 16 layers: distance from urban areas, distance from city streets, distance from hospital, distance from the clinic, distance from the park and the green area, distance from main roads, distance between the commercial centers, distance from educational centers, distance from the fire, distance from the fault, distance from the gas station, agricultural use, the use of the mountain, the use of the plain, slope and elevation. The 500 points were provided as network teaching points and the number of layers was 12 layers. Finally, according to the results, with the removal of facilities and urban areas, the potential has declined sharply, and most of the regions have urban development potential at the closest distance between these facilities and urban areas. The most potential areas of urban development are located in the southwest of Kermanshah and around the main roads of Kermanshah-Islamabad and Kermanshah-Kengavar. The northern areas of the city have a low development potential due to their height and slope.

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.

The aim of studying of sedimentary in a reservoir dam is finding of a comprehensive attitude about the measurement of the reservoir lost volume. In this middle, studying and anticipating of the volume of input sediment into the reservoir is significant. In this paper by using of sub-scaling, we have anticipated long term precipitation and temperature in Sattarkhan reservoir by the area of 950 km2 located in East Azerbaijan province. According to anticipated precipitation and temperature and Water Assessment Tools (SWAT) input sediments into the dam has simulated. The results show that although SWAT model can simulate with upper than 80% the procedure of input flow into the reservoir, but it couldn’t assessment of the simulation of flow maximum value. To resolve of this problem, it must be add erosion parameter. Therefore we have used of SDSM model and Multi-Layer Perceptron (MLP) and then land use map has made in GIS environment to recognition of application effect and erosion rate. Composing and integrating of user parameter in GIS environment and anticipated precipitation can optimize SWAT outcome and make it close to real value. Results indicated that Correlation between flows are anticipated and value of error has decreased in 95% accuracy by integrated using of above models.

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 main perspective in seasonal prediction of precipitation، is presenting a qualitative prediction for upcoming seasons. Information gained from such predictions can be used for decision making in various deciplines such as agriculture، water management and hydropower production. Besides، it can help for reducing the adverse effects of climatic changes like drought and flood. But General Ciculation Models (GCMs) outputs have coarse resolution (>100 km). Dynamical downscaling is a method for obtaining high-resolution climate data from relatively coarse resolution global climate models which do not capture the effects of local and regional forcing in areas of complex surface physiography. GCMs outputs at spatial resolution of 150-300 km are unable to resolve important sub-grid scale features such as clouds and topography. Many impacts models require information at scales of 50 km or less. As suchm، several statistical and dynamical methods are developed to estimate the smaller-scale information. Dynamical downscaling uses a limited area، high resolution model (a regional climate model: RCM) driven by boundary conditions from a GCM to derive smaller scale information. The aim of this study was application of RegCM4 dynamic model (Reginal climate model) in forecasting rainfall and improving the outputs using post processing techniques in northwest Iran during period 1982-2011. The recorded data of precipitation were collected from Urmia، Tabriz، Ardebil and Khuy Stations. The data required for running the regional climate model RegCM4 were adopted from center ICTP (International Centre for Theoretical Physics)، in the format of NetCDF including three sets of weather data، NNRP1 with a 6-hour-scale and a horizontal resolution of 2. 5°×2. 5° on the reanalysis databases of National Center of environmental prediction of United States، sea surface temperature، (SST) with a horizontal resolution of in 1°×1° from the type of SST belonged to America and National Oceanic and Atmospheric surface SURFACE، which were consisted of three topographic data GTOPO، the vegetation or land use، GLCC، and the soil type data GLZB، with a horizontal resolution of 30×30 seconds from United States Geological Survey، for the period 1982 to 2011. In order to implement the dynamic model، a test was conducted to determine the Convective Precipitation scheme and the amount of horizontal resolution for the year 2009 (as a normal year)، Accordingly، Kuo scheme with minimum mean bias error (MBE)، in comparison with observed precipitation in 36 synoptic stations of the region، was implemented as the main scheme، horizontal resolution of 30 × 30 square kilometers، the number of grid points including 152 in longitude (iy) and 168 in latitude (ix) was conducted during the statistical period of 1982 to 2011. Geographical area center implemented in the intended period was located in 30. 5 degrees north latitude and 50 degrees east longitude respectively. The output of the model included atmospheric data (ATM)، surface cover (SRF) and radiation cover with the format of NetCDF، each containing a large number of meteorological variables among which except precipitation from the Model (tpr)، 9 variables that were associated more with precipitation including q2m و t2m، ps، v1000، v500، u1000، u500،omega1000، omega500 were extracted. For post-processing the output of the model، the Multi-Layer Perceptron (MLP) and in Moving Average (MA) methods were used. For MLP Entering variables were 10 aforementioned variables and the target variable was observatory precipitation in the stationary point. At any one time، 80% of the data at the beginning of the series were for train and final 20 percent of data was used for test. The results of the study demonstrated that، in the study area، the mean bias error of raw annual precipitation outputs of the RegCM4 model was 124. 3 mm in the validation period، which by conducting Post Processing، reduced to 8. 9 mm. In the seasonal and monthly time scales، also، mean bias error of the were 31. 1 and 10. 4 mm، respectively، which were reduced to -0. 3 and zero mm، respectively، after post processing. The MA model was the prefered post processing method، in all time scales. In conclusion، it can be stated that the RegCM4 regional climate model with the said implementing conditions and in the study area، contained، mainly، overestimate in precipitation forecasting. However، the application of post-processing will optimally reduce bias. The appropriate method is also the simple moving average (MA) method.