Extraction, processing, production and display of geographic data
Hossein Asakereh; Fatemeh Motevali Meydanshah; Leila Ahadi
Abstract
Extended Abstract
Introduction
Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly ...
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Extended Abstract
Introduction
Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly temperature.
The most commonly used tool for simulating the responses of the climate to greenhouse gas increases and examining future temperature changes is the use of climate variables simulated by coupled atmosphere-ocean models (AOGCMs). General circulation models (GCMs) are powerful tools aimed at generating climate scenarios. However, GCMs cannot provide effective information on climate simulation at local and regional scales. Therefore, the downscaling method is used to bridge the gap between local and global scales.
The current research aims to simulate maximum temperature using an artificial neural network model that adopts data from the atmospheric general circulation model (HadCM3) under RCP8.5, RCP4.5, and RCP2.6 scenarios for the Yazd synoptic station from 2006 to 2095. The independent variable, as the input to the artificial neural network, was selected for statistical downscaling using four statistical criteria: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. Finally, the maximum temperature of the Yazd synoptic station for the next century was simulated.
Data and Methodology
The present study aims to investigate the maximum temperature of Yazd's synoptic station in the context of climate change based on valid scenarios until 2095. To achieve this, three sets of data were used: average daily maximum temperature data from Yazd's synoptic station, observed atmospheric data for the period of 1961 to 2005 (NCEP data), and simulated data from 2006 to 2095 based on release RCP scenarios. The NCEP data from 1961 to 2005 included 26 atmospheric variables that will be used as independent or predictor variables.
Modeling, simulating, and forecasting temperature based on nonlinear and chaotic time series is a challenging task. Prior studies have shown that artificial neural networks (ANNs) are suitable for simulating and predicting basic processes that are not well known. It is crucial to select the correct input variables intelligently and according to the purpose of the artificial neural network's design for prediction and simulation. Accordingly, in this study, the most suitable atmospheric parameters as the input of the artificial neural network were selected by pre-processing and selecting the atmospheric variables for the base period (1961-2005) to simulate with four statistical criteria (Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry). The resulting mean square error (MSE) obtained from the statistical criteria was compared, and the correlation coefficient and the similarity of the monthly time series trend of the simulated values with the target values were also analyzed. The best network architecture was selected to simulate the maximum temperature of Yazd's synoptic station from 2006 to 2095 under different RCP emission scenarios.
Discussion
The selection of explanatory variables for downscaling was based on four statistical methods: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. After analyzing the mean square error (MSE), correlation coefficient, monthly average values of the maximum temperature of Yazd station, and estimated values from 1961 to 2005, the probability density function, cumulative probability function, and monthly time series trend obtained from all four methods, the explanatory variables were selected. These variables include mean sea level pressure, the divergence of 1000 hPa, zonal wind component, zonal wind intensity of 850 and 500 hPa, altitude and vorticity of 500 hPa, average temperature, and relative humidity at a 2 m height.
The structure and architecture of the neural network were designed based on these selected variables. The network consisted of a two-layer feedforward, with a sigmoid transfer function in the hidden layer, a linear function in the output layer, an input layer with eight variables, eight neurons, and the Lunberg-Marquardt training algorithm. This architecture was used to simulate the maximum temperature of Yazd's synoptic station under RCP2.6, RCP4.5, and RCP8.5 scenarios for two periods of 2050-2006 and 2095-2051.
Comparing the monthly average values of RCPs (RCP2.6, RCP4.5, and RCP8.5) in the first statistical period (2050-2006) with the base period (1961-2005), the maximum temperature of Yazd station indicates an increase in temperature in winter, spring, and summer, and a decrease in the autumn season under all three RCPs.
Comparing the monthly mean values of RCPs (RCP2.6, RCP4.5, and RCP8.5) of the second period (2051-1995) with measured mean maximum temperature (2005-1961) showed that temperature will increase the most in winter, spring, and summer, similar to the first period of the RCP8.5 scenario. In this scenario, unlike the other scenarios, the increase in temperature is evident in both subperiods for the autumn season. Finally, in the second period (2051-1995), the increase in the average maximum temperature of Yazd station in winter, spring, and summer, and the decrease in the average maximum temperature in autumn will be more significant.
Conclusion
The increase in greenhouse gas concentration resulting from human industrial activities is expected to cause global and regional warming in the future. The current study's findings indicate that the average maximum temperature of Yazd station will rise between 0.4 to 6.9 in winter, 0.2 to 8.1 in spring, and 1.1 to 7.7 in summer from 2006 to 2095. However, a decrease in the maximum temperature between 0.6 and 1.4 is expected in autumn. These results are consistent with those of other researchers.
Geographic Data
Hossein Asakereh; Ava Gholami
Abstract
Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, ...
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Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, climate forecasting is performed using "simulation" approach. Using atmospheric general circulation models such as RCPs and climate scenarios developed as their output is an accepted method of simulating climate variables, especially temperature. In each of these scenarios, radiative forcing changes at a certain rate until 2100. Downscaling is the main technique used in RCPs. Different methods are used for downscaling among which artificial neural network is more widely accepted due to its more accurate evaluations. Materials & MethodsData collected for the purpose of the present study include: 1) Daily maximum temperature recorded in Qazvin synoptic station during 1961-2005. These records were derived from Iran Meteorological Organization and used as an output for calibration, fitting, and finally selecting the best fit model for the observations, 2) Atmospheric observations including daily records of 26 atmospheric variables. These data were recorded by the United States National Centers for Environmental Predictions (NCEP) and the United States National Center for Atmospheric Research (NCAR) during 1961-2005 reference period and used as input or explanatory (predictor or independent) variables in the present study 3) Representative Concentration Pathway (RCP) extracted from atmospheric general circulation model (including the output of HadCM3 model) which is used to simulate 2006-2100 reference period.Artificial neural network model was used to downscale atmospheric data and simulate maximum temperature recorded in Qazvin synoptic station. Using Pearson correlation coefficient, the correlation between maximum temperature recorded in Qazvin synoptic station and each of the 26 atmospheric variables was estimated. Then, forward selection and backward deletion, percentage decrease index, and stepwise methods were used to preprocess the variables, select the most appropriate predictor variables (input variable in the network) and perform statistical downscaling. Following the selection of suitable explanatory variables in each of the above mentioned methods, selected variables were separately given as input to the network to reach a proper design for the neural network architecture and perform the final simulation. In other words, the artificial neural network model was fitted four times with different input variables. Then, number of neurons and network layers were determined, a suitable weight was assigned to each variable and the neural network was trained to reach the most appropriate architecture for the neural network. Finally, emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were given as input to the selected architecture, and maximum temperature was simulated for 2006-2100 reference period. Results & DiscussionAppropriate explanatory variables were selected in the present study using four different preprocessing methods. Forward selection method with the lowest minimum mean square error (MMSE) of 6.7 and the highest correlation coefficient of 0.97 was selected as the most appropriate method. Therefore, variables obtained from this method, including average temperature near the surface, average pressure at sea level, and altitude at 500 and 850 hPa level, were selected as predictor variables entering the artificial neural network to simulate future temperature of the station. Finally, a neural network with 8 inputs, a hidden layer with 10 neurons and sigmoid transfer function, and an output layer with 1 neuron and Linear transfer function were confirmed using Levenberg-Marquardt algorithm. There were then used to simulate the future temperature of Qazvin synoptic station. The highest and the lowest temperature values were estimated for December with 9.9°C and January with 6.6°C, respectively. The lowest error rate also belonged to December (-1.5°C). Simulation results indicated that the highest increase in maximum temperature of Qazvin synoptic station in 2006-2100 reference period was observed in RCP8.5, RCP4.5 and RCP2.6 scenarios, respectively. The increasing trend in RCP8.5 scenario was estimated much higher than the base temperature. Moreover, the highest temperature increase (6.7°C) in RCP8.5 scenario belongs to June and the highest temperature decrease (3°C) in the optimistic scenario (RCP2.6) belongs to October. ConclusionSelecting appropriate explanatory variables is an important step in the process of simulating future temperature. Various methods of variables selection, statistical downscaling and artificial neural network model were used to estimate and simulate temperature parameter. Then, RCP 2.6, RCP4.5, and RCP8.5 scenarios were simulated for the 2006-2100 reference period. Maximum temperature of Qazvin synoptic station in the simulated RCP scenarios (belonging to the reference period) was compared with maximum temperature in 1961-2005 period. Results indicate that the highest temperature increase in Qazvin station occurs in the pessimistic scenario (RCP8.5). The increasing trend of temperature begins with RCP2.6 scenario and reaches its highest level in RCP8.5 scenario. In these three scenarios, summer temperature of the next 94 years may increase at a higher rate as compared to other seasons in Qazvin. This means that not only Iran is located in an arid region, but also its temperature will be increasing in the future. Since temperature and precipitation in different parts of the world are considered to be among the most important indicators of climate change and global warming, various models designed to forecast and simulate these phenomena and the future climate suggest an increase in temperature during the coming decades.
Morteza Najafi; Mojtaba Rafieian; Rama Ghalambor Dezfuli
Abstract
Introduction Nowadays, spatial models and techniques are widely used to analyze challenges at urban and regional levels. These models and techniques can identify the relations between different variables, evaluate their impact on spatial spheres, and thus aid urban planners and managers. Recently, solid ...
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Introduction Nowadays, spatial models and techniques are widely used to analyze challenges at urban and regional levels. These models and techniques can identify the relations between different variables, evaluate their impact on spatial spheres, and thus aid urban planners and managers. Recently, solid waste and the amount of waste generated in urban areas have gained attention as a major global challenge and the World Bank has highlighted the importance of an acceptable global approach to the issue of urban waste in 2016 (World Bank, 2016). Urban waste impacts the city and its urban management system in different ways such as urban environment degradation, economic impacts and the challenges of urban landscape. Different factors impact urban solid waste generation and investigating the relation between these variables can help urban planners and managers formulate general plans and policies to reduce urban waste. But a mere examination of the relationship between factors affecting urban waste generation and the variables proposed by the World Bank cannot provide a good estimate of the future status, since spatial factors always impact the quantity of urban waste generated. Therefore, spatial models and artificial neural networks were proposed and discussed. Geographically Weighted Regression is one of these methods used to investigate the relationship between different factors affecting urban waste generation. Geographically Weighted Regression can investigate the relationship between different variables, examine their impact on the city and predict the relationship between different variable of urban waste generation and their impact on the city in the future. The artificial neural network was also used to assess the nature of data and predict the future status of urban waste. Materials & Methods The study area consists of 22 districts, 123 zones (116 zone due to the availability of supplementary information of 2011-2012 regarding the districts of Tehran), 40323 statistical areas and 895247 land uses of Tehran. Data were classified in three stages. The first phase includes the information collected from Tehran waste management organization regarding urban waste in 1996 to 2016. In the second phase, information was collected from statistical center of Iran regarding demographic segments and social components. Finally, data were collected from Tehran municipality in the third phase providing useful information about urban performance (Land use). Results & Discussion Physical-environmental components and especially land use directly impact urban waste generation. However, results indicate that some land uses such as institutional and publicbuildings gradually stop the increasing process of urban waste generation due to a decrease in their population as compared to residential land use. Population density and income ratio are investigated as the first and second rank variables. These two variables have directly impacted the amount of urban waste generation in most districts of Tehran. From central areas of the 6th district to the southern areas of the 20th district, southeastern areas of the 18th district and eastern areas of the 4th district of Tehran were influenced by population variables. In other words, the amount of urban waste generation is increased with increased population density in these district. However, the amount of urban waste generation in the 22nd and 21st districts do not change with the above mentioned variables. Results indicate that different urban development plans and policies increase population and area dedicated to different land uses and thus, play an important role in urban waste generation. The 22nd and 21st districts are in a desirable status regarding variables such as area, population, and urban waste generation, but predictions indicate that they will reach a similar status and face challenges related to urban waste generation in 10 years. Spatial distribution pattern of urban waste generation in Tehran indicates that the eastern and southern districts produce the highest amount of urban wastes. This pattern is gradually moving from central to western and central districts, and without a plan to control the situation, the pattern will move from east to west and south to north of Tehran in the next 10 years. Based on the results of spatial autocorrelation and a comparison with the results of the least squares method, Geographically Weighted Regression was considered as a suitable method of predicting urban waste variables in Tehran. This indicates that spatial variables affect urban waste generation in Tehran. Moreover, artificial neural network is capable of predicting non-spatial nature of relations among different variables of urban waste generation and thus can predict the amount of urban waste generation in Tehran. Conclusion Results not only identify (physical-environmental, economic and social) variables affecting urban waste generation, but also indicate superiority of Geographically Weighted Regression technique at spatial and non-spatial levels as compared to the least-squares regression and artificial neural network.
Mojtaba Rahiminasab; Yazdan Amerian
Abstract
Extended Abstract Introduction Rain is one of the most important atmospheric phenomena affecting human life. Rainfall prediction is important for various purposes such as planning for agricultural activities, forecasting floods, monitoring drought and providing resources for consumable water. The rapid ...
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Extended Abstract Introduction Rain is one of the most important atmospheric phenomena affecting human life. Rainfall prediction is important for various purposes such as planning for agricultural activities, forecasting floods, monitoring drought and providing resources for consumable water. The rapid expansion of using artificial neural networks (ANNs) as an experimental and efficient model in various sciences including meteorology and climatology implies the high value of studying these types of models. Materials and methods The purpose of this paper is to predict the monthly rainfall in Iran, using the combination of artificial neural networks and extendedKalman filter. For this purpose, the monthly average rainfall data of about 180 synoptic stations spreading across the country were used during the years 1951 to 2016, then, the monthly rainfall was predicted for the year 2017 using the article’s method. Artificial neural networks are a method for the approximation of the functions and prediction of the future state of various systems. Artificial neural networks discover the law latent in them and transfer it into the network by processing the experimental data. The smallest processing unit of information in the artificial neural network is neuron that builds the bases for the application of neural networks. Each neural network consists of a number of nodes which are the neurons, and the communication weights that connect the nodes together. Input data is multiplied by their corresponding weights, and their sum is entered into the neurons. Each neuron has a transfer function. This input data passes through the transfer function and specifies the output value of the neuron. The back propagation algorithm is one of the most commonly used algorithms for teaching these networks, but the back propagation algorithm depends on the selection of the number of hidden neurons. Also, the convergence speed of the back propagation algorithm is very slow comparing with the proposed method in this paper, and is very sensitive to the noises present in the input and output data set, which is used for teaching the neural network. In addition, it has a poor performance in modeling the complex processes. One of the most famous methods to eliminate the aforementioned defects is the use of the Kalman filter. The Kalman filter contains a set of mathematical equations that performs a repeated process, prediction and updates, and is also an optimal tool in minimizing the covarianceof the estimated error. The leading neural network can be considered as a nonlinear dynamic system with synaptic weights and equate the teaching of the neural network with the problem of estimating the state of the nonlinear system. Therefore, the extended version of the Kalman filter can be used to estimate the adjustable parameters of the artificial neural network like the neural network weights. Results and discussion The climatic zonation of Iran was used for the data separation by the method of Coupon-Geiger which has been conducted by other researchers, and Iran was divided into eight climatic zones. This zonation divides Iran into dry and hot desert, dry and cold desert, dry and hot semi-desert, dry and cold semi-desert, moderate with dry and hot summers, rainy moderate with warm summers, snowy with dry and hotarm summers, snowy with dry and warm summers climates. This artificial neural network which has been taught by the extended Kalmanfilter, was used for the prediction in each of the climatic zones. A multi-layered artificial neural network with two hidden layers has been used. There are 10 neurons in each of the hidden layers, and 7 neurons in the input layer, which are the same numbers as the network inputs. The methodology is that the year and number of months, the average monthly temperature, the average monthly wind speed and the geographic location of the synoptic stations are considered as the input parameters, and the average monthly precipitation as the output parameter. The difference between the observed and the predicted values of the monthly precipitation in 2017 was calculated for all stations and was considered as an error. The Root Mean Square Error (RMSE) of these differences was calculated for the 8 climatic zones. The RMSE is lower for dry and hot desert climate than for dry and cold desert climate. This RMSE is lower for dry and cold semi-desert climate than for dry and hot semi-desert climate. The RMSE is lower for moderate climate with dry and hot summers than for moderate rainy climate with warm summers. The RMSE is lower for snowy climate with dry and hotsummers than for snowy climates with dry and warm summers. Conclusion In most cases, the RMSE for hot climates is less than others that represents the efficiency of the proposed method for the prediction of monthly precipitation in hot climates.
Kaveh Jafarzadeh; GholamReza Sabzghabaei; Shahram Yousefi Khangah; satar soltanian
Abstract
Extended abstract
Introduction
City has long been regarded as one of the human achievements by civilizations. Urban structure is part of the basic and mainconcepts of urban engineering knowledge and, in fact, is the foundation of its formation, and it is of great importance that some urban planners ...
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Extended abstract
Introduction
City has long been regarded as one of the human achievements by civilizations. Urban structure is part of the basic and mainconcepts of urban engineering knowledge and, in fact, is the foundation of its formation, and it is of great importance that some urban planners in developed countries regard it as equal to the spatial planning of the city. Today, lots of driving forces exert pressure on the environment. Change in land use and land cover is one of the pressures caused by driving factors such as population and its increase. The destruction of urban landscape, and change in land use and land cover are cases that constantly pressure the environmentof the country. Land use change is a complex and dynamic process thatinterconnect natural and human systems, therefore it directly relatesto many environmental issues that are globally significant. So, it can be stated that changes in urban structure has always been one of the most important factors, by whichmanhas influenced his environment. Given the role of environment in human life, precise information about the environmental change and the process of their changes should be achieved,which, can determine the extent of the expansion and destruction of resources, and guide these changes in appropriate courses by predicting urban structure changes.
Materials and Methods
In this research, an eight-year period ofthe Google Earth images from Digital Globe, Astrium satellites for the years 2007-2015 was used to model the changes of the urban structure in the study area. These images were then digitized to identify the desired uses. The required preprocessing was carried out by implementing the rules of topology, and the map of user changes for the two periods of 2007 and 2015 was prepared by inserting the images into the ENVE software, and land use was located in 12 educational, religious, park and green spaces, Residential, agricultural, gardens, industrial, sports, tree cover, wasteland and industrial land classes.Then, the transfer force modeling was carried out using the Perceptron of Multi-layer Artificial Neural Networks and 11 variables that include slope, direction, elevation, distance from residential areas, distance from agricultural lands, distance from the gardens, distance from the water zone, distance from the tree cover, distance from barren lands and distance from the road. Then, theassignment of changes to each use was calculated using the Markov Chain, and the modeling for the year 1402 was carried out using the hard prediction and calibration periodmodelofthe years1386to1394.At the end, the urban structureof 1402 was predicted using the Calibration period of 1386 to 1394.
Results and discussion
The results of monitoring the changes showed that agricultural uses (437) and tree cover (9) have decreased, while other uses have increased during two study periods. The reasons for these changes can be largely due to the increase in population and the increase in the needs of the population along with the agricultural not being cost effective, and the roads, wastelands, gardens, educational, religious, water zones, parks and green spaces, industrial, sports and residential uses have had an increasing trend. The results of modeling the transition forceusing artificial neural network showed high accuracy in most of the sub-models. The total error in modeling was obtained for the year 1394, which illustrates the high adaptability of the projected image of the model with the image of the ground reality and the acceptability of the model.The results of modeling for the year 1402 indicatea very high increase in the use of residential (195 hectares) and garden (104 hectares), and a significant reduction of 33 hectares in agricultural use.
Conclusion
In general, it can be stated that the trend of Changes in the urban structure ofGhaemshahr, especially in agricultural and residential sectors is enormous, which leads to the degradation and destruction of the natural environment and the fragmentation of communication corridors that guarantee the balance and sustainability of wild life and the environment. All of these factors are due tothe poor urban and environmental management, including control, supervision and monitoring and the lack of proper planning. The findings of this research call for the necessity of more attention to the sustainable exploitation of the land and preventing its destruction in this city. The results obtained from the prediction of the future also indicate the reliability and validity of the model that is fully consistent with the reality and can be used as an executive model in the future vision planningfor the city ofQaemshahr, and it is possible to prevent damages to the city and its nature through proper urban planning and decision-making of managers.
Farideh Sabzehee; Mohammad Ali Sharifi; Mehdi Akhoondzadeh hanzaee
Abstract
Extended Abstract
Electrondensity is one of the significant parameters for monitoring and describing the ionosphere.The ionosphere is a consequential source of errors for the GPS signals that traverse through the ionosphere on their ways to the ground-based receivers, because there is a high concentration ...
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Extended Abstract
Electrondensity is one of the significant parameters for monitoring and describing the ionosphere.The ionosphere is a consequential source of errors for the GPS signals that traverse through the ionosphere on their ways to the ground-based receivers, because there is a high concentration of free electrons and ionsreleased by the ionizingaction of solar X-ray and ultraviolet radiation on atmospheric formers. Radio Occultation(RO) is one of the most modern satellite techniques to study on vertical profiles of neutral density, temperature, pressure and water vapor in the stratosphere and troposphere and ionospheric electron density profiles with high vertical resolutions.Since the RO technique using the GPS signals was employed for the first time by the Global Positioning System Meteorology (GPS/MET), the low-earth-orbit-based GPS RO technique has been proven as a successful method in exploring the earth’s lower atmosphere and ionosphere.
Abel transformation is the basic hypothesis made in the retrieval of radio-occulted ionospheric parameters.The Abel inversion is a powerful tool to retrieve high-resolution vertical profiles of electron density from GPS radio occultation collected by satellites into Low Earth Orbit(LEO).
COSMIC satellite records measurements during the whole day and is not limited to the specific times and special atmospheric conditions.It should be noted that the GPS radio occultation techniques provide continuous and useful ionospheric layers information and are not obtained from the point wise measurements by other satellites.
COSMIC satellite also records the altitude for the measurements of the electron density profile. COSMIC satellite provides more than1000 electron density profiles per day with approximately global coverage and also parts of them cover IRAN .In this approach, the LEO-GPS line of sight is occulted by the Earth’s limb with the setting(or rising) motion of the LEO satellite. The GPS-LEO radio connection successively records the atmospheric layers at different altitudes. The ionosphere is highly variable in space and time. Thus, for modeling the electrondensity profile the time changes(diurnaland seasonal) and location changes(geographical position of station), must be considered. In this research, the input space includes the day number (seasonal variation), hour (diurnal variation), latitude, longitude, height and F10.7 index (measure of the solar activity). The output of the model is the ionospheric electron density profile(Ne).The COSMIC observations and IRI-2007-based data of electron density profiles were also analyzed during the solar minimum period. In this research, we used a feedforward Artificial Neural Network (ANN) with 55 neurons in hidden layer for modeling profiles of electron density of COSMIC satellite performance of the ANN models was evaluated using correlation coefficient (R=92%),R-Squared(0.83). It was found that the ANN model could be applied successfully in estimating the electron density profiles retrieved from the FORMOSAT-3/COSMIC.The comparison of the IRI model electron density profile with the COSMIC RO measurements during each month of the year 2007 over IRAN is performed.The electron density profile from all three International Reference Ionosphere (IRI) models, namely IRI-NEQ,IRI-2001, and IRI-01-Corr are used.
The results showed that the results of the IRI2007 model electron density is not satisfactory over IRAN and ANN model electron density profile is in very good agreement with COSMIC RO measurements. It was concluded that IRI-NEQ model is more appropriate thanthe other two models.
The results showed that the differences between the modeled profile electron density and theobserved profile electron density are very lower than the differences between the IRI-2007 models.Maximum changes occurred in January and December at analtitude of about 450 km and minimum changes were recorded in November at the height of 250 Km and in April at the height of 450 Km. The differences also decreased in the summer at higher altitudes and in winter at lower altitudes.
Monir Darestani Farahani; Mahdi Akhondzadeh Hanzaei; Farhang Ahmadi Qivi
Abstract
Abstract
Water salinity is one of the important environmental factors of the sea and plays a significant role in the study and prediction of the oceanic surface currents, location analysis of the fish aggregation, density determination and studying its changes, and also in ecological properties. This ...
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Abstract
Water salinity is one of the important environmental factors of the sea and plays a significant role in the study and prediction of the oceanic surface currents, location analysis of the fish aggregation, density determination and studying its changes, and also in ecological properties. This parameter changes greatly with time and location, and proper recognition of it requires measurements at short time intervals (monthly) of multiple points in the study area.
In traditional ways, the assessment and evaluation of one or several specific factors of water quality is often costly and time-consuming, and cannot be a good indication for the entire area of a vast region. But in recent years, satellite and remote sensing technology have been considered as an appropriate tool for evaluating some water quality parameters because, given the digitality of these data, their wide availability, regular measurements, their repetition in short periods of time, Less cost and time, a wide range of projects can be achieved. The purpose of this study is mapping sea surface salinity of the Persian Gulf in Iran and the Gulf of St. Lawrence in Canada using MODIS satellite imagery. In this regard, a software has been produced in Iran for the first time that can prepare salinity, temperature and density maps of the sea surface in three different models with proper accuracy by entering the MODIS satellite imagery and CTD field data. High capability and flexibility of the Artificial Neural Network in approximation of nonlinear and linear continuous functions in hybrid space, led this study to provide a new method based on using this network in which salinity map is determined by a multilayer perceptron network.
Mohammadzaman Ahmadi; Saeed Behzadi
Abstract
Abstract
Wells are one of the main sources of drinking water, agriculture and industry. Water quality in terms of drinking is the most important parameter among qualitative parameters. Therefore, the investigation and anticipation of pollution are the goals of managers and planners. In this research, ...
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Abstract
Wells are one of the main sources of drinking water, agriculture and industry. Water quality in terms of drinking is the most important parameter among qualitative parameters. Therefore, the investigation and anticipation of pollution are the goals of managers and planners. In this research, artificial neural network and geospatial information system have been used to determine the contamination of magnesium parameter in the water of Gonbad villages in Golestan province during the 4 consecutive of 2008, 2009, 2010 and 2011. In this model, the artificial neural network has been evaluated in Perceptron structure with a number of hidden layers and various neurons. At present, pollution of underground is increasing due to the chemical and industrial activities. Therefore, it is necessary to identify vulnerable areas to prevent the pollution of groundwater. Also, in this research, to determine the groundwater contamination, maps such as topography, geology, location of wells, slopes and …, were used in spatial environment. After determining the amount of contamination using the neural network models and the output of the model in spatial environment, the pollution maps were obtained. Also, by observing contamination maps and data available in the aforementioned years, it can be concluded that the level of pollution was low and this pollution cannot be dangerous.
Nahid Sajadian
Abstract
To date, a number of plans have been implemented to reduce air pollution in the city of Tehran.But the problem is that, along with other shortcomings,these planshave often been a passive and temporaryreaction to the increase of air pollution with adherence to crisis management rather than risk management, ...
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To date, a number of plans have been implemented to reduce air pollution in the city of Tehran.But the problem is that, along with other shortcomings,these planshave often been a passive and temporaryreaction to the increase of air pollution with adherence to crisis management rather than risk management, and no decision-making support system has been used in management decisions based on these plans.Therefore, due to the importance of the subject, this research was carried out by an analytical-applied method using hourly data, carbon monoxide density of 12 stations from a collection of air pollution measurement stations belonging to the air quality company, as well as meteorological dataof wind speed, wind direction and the temperature at the Mehrabad station, all related to the year 1389, and the number of the cars on the highways and streets of city of Tehran with the aim of predicting the temporal-spatial air pollution caused by the urban transport of Tehran Metropolis in line with the application of the spatial decision- making of the air quality management and with the ultimate goal of optimal management of urban transport of Tehran Metropolis. In this regard, since the ultimate goal of the present study is to use its results in controlling the optimal urban transportation as an important source of air pollutants, the LUR method was used to measure carbon monoxide index in the transportationof Tehran metropolis along with other pollutants. An artificial neural network was then used to predict the time of the possible occurrence of air pollution with emphasis on using risk management, and then, based on time predictions resulted from the artificial neural network, the regions with high possibility of air pollution occurrence were identified using the Kriging index.According to the findings of this research,the results were appropriate, so that this model could be used in the air quality management support system to reach the ultimate goal of optimal urban transport management in Tehran Metropolis.
Monireh Shamshiri; Mahdi Akhondzadeh Hanzaei
Abstract
Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which ...
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Discussion about earthquake to reduce its casualties and damages is very important, especially in a seismic area like Iran where the occurrence of this natural phenomenon is seen annually. Anomaly detection prior to earthquake plays an important role in earthquake prediction. Ionosphere changes which are recognizable by remote measurements (such as using Global Positioning System) are known as earthquake ionospheric precursors. In this study, two data sets from the ionospheric Total Electron Content (TEC) derived from the GPS data processing by Bernese software were used for two studies, Ahar earthquake, East Azerbaijan (2012/08/11) and Kaki earthquake,Bushehr (2013/4/9), and the results were compared with data obtained from the global stations. Because of the nonlinear behavior of TEC changes, in order to predict and detect its changes, integration of neural network (using multilayer Perceptron (MLP)) with particle swarm optimization algorithm (PSO) was used. Particle Swarm Optimization algorithm with a performance based on the population can be effective in improving estimatedweight by artificial neural network. By analyzing the causes of ionospheric anomalies including the geomagnetic fields and solar activities and their removal from the processes, the results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms were able to have appropriate efficiency for the prediction of nonlinear time series. The output resulted from the integration of artificial neural network and PSO shows that both positive and negative anomalies occur. The anomalies before earthquakes often occur close to the epicenter of the earthquake and are visible 3 days before the Ahar earthquake and 2 to 6 days before the Kaki earthquake are.
