bluuebunny/crossref_metadata_2025 huggingface dataset:
High occurrence of new particle formation events at the Maïdo
high altitude observatory (2150 m), Reunion Island (Indian Ocean)
Authors: Foucart Brice , Sellegri Karine , Tulet Pierre , Rose Clémence , Metzger Jean-Marc , Picard David
Published: September 2017

Abstract. This study aims to report and characterize the frequent new particle formation (NPF) events observed at the Maïdo observatory, Reunion Island, a Southern Hemisphere site located at 2200 m and surrounded by the Indian Ocean. In 2014 and 2015, continuous aerosol measurements were made using both a Differential Mobility Particle Sizer (DMPS) and an Air Ion Spectrometer (AIS) to characterize the NPF events down to the lowest particle size scale. Carbon monoxide (CO) and sulfur dioxide (SO2) concentrations were monitored, as well as meteorological parameters, in order to identify the conditions that were favourable to the occurrence of nucleation in this specific environment. We point out that the annual NPF frequency average (65 %) is one of the highest reported so far. Monthly averages show a bimodal variation of the NPF frequency, with a maximum observed during off-season periods (March to May and September to December). A high yearly median particle Growth Rate (GR) of 15.16 nm.h−1 is also measured, occasionally peaking at values of the order of 100 nm.h−1 and showing a bimodal seasonal variation with maxima observed in July and November. Yearly medians of 2 and 12 nm particle formation rates (J2 and J12) are 0.858 and 0.508 cm−3.s−1 respectively, with a seasonal variation similar to that of the GR. The seasonal variations of GR and J correspond to the seasonal variation of radiation, which may be responsible for more efficient photochemistry and also for a higher influence of the boundary layer, as shown by the CO seasonal variation. Multiple sources can contribute to the NPF frequency and intensity, including marine, biogenic from vegetation, and anthropogenic sources.
Ozone comparison between Pandora #34, Dobson #061, OMI, and OMPS in Boulder, Colorado, for the period December 2013–December 2016
Authors: Herman Jay , Evans Robert , Cede Alexander , Abuhassan Nader , Petropavlovskikh Irina , McConville Glenn , Miyagawa Koji , Noirot Brandon
Published: September 2017

Abstract. A one-time-calibrated (in December 2013) Pandora spectrometer instrument (Pan #034) has been compared to a periodically calibrated Dobson spectroradiometer (Dobson #061) co-located in Boulder, Colorado, and compared with two satellite instruments over a 3-year period (December 2013–December 2016). The results show good agreement between Pan #034 and Dobson #061 within their statistical uncertainties. Both records are corrected for ozone retrieval sensitivity to stratospheric temperature variability obtained from the Global Modeling Initiative (GMI) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) model calculations. Pandora #034 and Dobson #061 differ by an average of 2.1 ± 3.2 % when both instruments use their standard ozone absorption cross sections in the retrieval algorithms. The results show a relative drift (0.2 ± 0.08 % yr−1) between Pandora observations against NOAA Dobson in Boulder, CO, over a 3-year period of continuous operation. Pandora drifts relative to the satellite Ozone Monitoring Instrument (OMI) and the Ozone Mapping Profiler Suite (OMPS) are +0.18 ± 0.2 % yr−1 and −0.18 ± 0.2 % yr−1, respectively, where the uncertainties are 2 standard deviations. The drift between Dobson #061 and OMPS for a 5.5-year period (January 2012–June 2017) is −0.07 ± 0.06 % yr−1.
Evaluation of linear regression techniques for atmospheric applications: The importance of appropriate weighting
Authors: Wu Cheng , Yu Jian Zhen
Published: September 2017

Abstract. Linear regression techniques are widely used in atmospheric science, but are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The regression techniques tested are Ordinary Least Square (OLS), Deming Regression (DR), Orthogonal Distance Regression (ODR), Weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne Twister (MT) pseudorandom number generator. The numerical simulations are also improved by: (a) refining the parameterization of non-linear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found an improper λ in DR can leads to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. With the knowledge of an appropriate weighting, DR, WODR and YR are recommended for atmospheric studies when both x and y data have measurement errors.
A PROBABILITY MODEL FOR DROUGHT PREDICTION USING FUSION OF MARKOV CHAIN AND SAX METHODS
Authors: Jouybari-Moghaddam Y. , Saradjian M. R. , Forati A. M.
Published: September 2017

Abstract. Drought is one of the most powerful natural disasters which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area. Monitoring and prediction the severity of the drought can be useful in the management of the natural disaster caused by drought. Many indices were used in predicting droughts such as SPI, VCI, and TVX. In this paper, based on three data sets (rainfall, NDVI, and land surface temperature) which are acquired from MODIS satellite imagery, time series of SPI, VCI, and TVX in time limited between winters 2000 to summer 2015 for the east region of Isfahan province were created. Using these indices and fusion of symbolic aggregation approximation and hidden Markov chain drought was predicted for fall 2015. For this purpose, at first, each time series was transformed into the set of quality data based on the state of drought (5 group) by using SAX algorithm then the probability matrix for the future state was created by using Markov hidden chain. The fall drought severity was predicted by fusion the probability matrix and state of drought severity in summer 2015. The prediction based on the likelihood for each state of drought includes severe drought, middle drought, normal drought, severe wet and middle wet. The analysis and experimental result from proposed algorithm show that the product of this algorithm is acceptable and the proposed algorithm is appropriate and efficient for predicting drought using remote sensor data.
MONITORING SPATIOTEMPORAL CHANGES OF HEAT ISLAND IN BABOL CITY DUE TO LAND USE CHANGES
Authors: Alavi Panah S. K. , Kiavarz Mogaddam M. , Karimi Firozjaei M.
Published: September 2017

Abstract. Urban heat island is one of the most vital environmental risks in urban areas. The advent of remote sensing technology provides better visibility due to the integrated view, low-cost, fast and effective way to study and monitor environmental and humanistic changes. The aim of this study is a spatiotemporal evaluation of land use changes and the heat island in the time period of 1985-2015 for the studied area in the city of Babol. For this purpose, multi-temporal Landsat images were used in this study. For calculating the land surface temperature (LST), single-channel and maximum likelihood algorithms were used, to classify Images. Therefore, land use changes and LST were examined, and thereby the relationship between land-use changes was analyzed with the normalized LST. By using the average and standard deviation of normalized thermal images, the area was divided into five temperature categories, inter alia, very low, low, medium, high and very high and then, the heat island changes in the studied time period were investigated. The results indicate that land use changes for built-up lands increased by 92%, and a noticeable decrease was observed for agricultural lands. The Built-up land changes trend has direct relation with the trend of normalized surface temperature changes. Low and very low-temperature categories which follow a decreasing trend, are related to lands far away from the city. Also, high and very high-temperature categories whose areas increase annually, are adjacent to the city center and exit ways of the town. The results emphasize on the importance of attention of urban planners and managers to the urban heat island as an environmental risk.
AERIAL IMAGERY AND LIDAR DATA FUSION FOR UNAMBIGUOUS EXTRACTION OF ADJACENT LEVEL-BUILDINGS’ FOOTPRINTS
Authors: Mola Ebrahimi S. , Arefi H. , Rasti Veis H.
Published: September 2017

Abstract. Our paper aims to present a new approach to identify and extract building footprints using aerial images and LiDAR data. Employing an edge detector algorithm, our method first extracts the outer boundary of buildings, and then by taking advantage of Hough transform and extracting the boundary of connected buildings in a building block, it extracts building footprints located in each block. The proposed method first recognizes the predominant leading orientation of a building block using Hough transform, and then rotates the block according to the inverted complement of the dominant line’s angle. Therefore the block poses horizontally. Afterwards, by use of another Hough transform, vertical lines, which might be the building boundaries of interest, are extracted and the final building footprints within a block are obtained. The proposed algorithm is implemented and tested on the urban area of Zeebruges, Belgium(IEEE Contest,2015). The areas of extracted footprints are compared to the corresponding areas in the reference data and mean error is equal to 7.43 m2. Besides, qualitative and quantitative evaluations suggest that the proposed algorithm leads to acceptable results in automated precise extraction of building footprints.
MODIFIED OPTIMIZATION WATER INDEX (MOWI) FOR LANDSAT-8 OLI/TIRS
Authors: Moradi M. , Sahebi M. , Shokri M.
Published: September 2017

Abstract. Water is one of the most important resources that essential need for human life. Due to population growth and increasing need of human to water, proper management of water resources will be one of the serious challenges of next decades. Remote sensing data is the best way to the management of water resources due time and cost effectiveness over a greater range of temporal and spatial scales. Between many kinds of satellite data, from SAR to optic or from high resolution to low resolution, Landsat imagery is more interesting data for water detection and management of earth surface water. Landsat8 OLI/TIRS is the newest version of Landsat satellite series. In this paper, we investigated the full spectral potential of Landsat8 for water detection. It is developed many kinds of methods for this purpose that index based methods have some advantages than other methods. Pervious indices just use a limited number of spectral band. In this paper, Modified Optimization Water Index (MOWI) defined by consideration of a linear combination of bands that each coefficient of bands calculated by particle swarm algorithm. The result shows that modified optimization water index (MOWI) has a proper performance on different condition like cloud, cloud shadow and mountain shadow.
LAND COVER CHANGES DETECTION IN POLARIMETRIC SAR DATA USING ALGEBRA, SIMILARITY AND DISTANCE BASED METHODS
Authors: Najafi A. , Hasanlou M. , Akbari V.
Published: September 2017

Abstract. Monitoring and surveillance changes around the world need powerful methods, so detection, visualization, and assessment of significant changes are essential for planning and management. Incorporating polarimetric SAR images due to interactions between electromagnetic waves and target and because of the high spatial resolution almost one meter can be used to study changes in the Earth's surface. Full polarized radar images comparing to single polarized radar images use amplitude and phase information of the surface in different available polarization (HH, HV, VH, and VV). This study is based on the decomposition of full polarized airborne UAVSAR images and integration of these features with algebra method involves Image Differencing (ID) and Image Ratio (IR) algorithms with the mathematical nature and distance-based method involves Canberra (CA) and Euclidean (ED) algorithms with measuring distance between corresponding vector and similarity-based method involves Taminoto (TA) and Kulczynski (KU) algorithms with dependence corresponding vector for change detecting purposes on two real PolSAR datasets. Assessment of incorporated methods is implemented using ground truth data and different criteria for evaluating such as overall accuracy (OA), area under ROC curve (AUC) and false alarms rate (FAR). The output results show that ID, IR, and CA have superiority to detect changes comparing to other implemented algorithms. Also, numerical results show that the highest performance in two datasets has OA more than 90%. In other assessment criteria, mention algorithms have low FAR and high AUC value indices to detect changes in PolSAR images.
EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP
MAPPING USING SATELLITE IMAGE TIME-SERIES DATA
Authors: Niazmardi S. , Safari A. , Homayouni S.
Published: September 2017

Abstract. Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.
SPATIAL AND TEMPORAL ANALYSIS OF SEA SURFACE SALINITY USING SATELLITE IMAGERY IN GULF OF MEXICO
Authors: Rajabi S. , Hasanlou M. , Safari A. R.
Published: September 2017

Abstract. The recent development of satellite sea surface salinity (SSS) observations has enabled us to analyse SSS variations with high spatiotemporal resolution. In this regards, The Level3-version4 data observed by Aquarius are used to examine the variability of SSS in Gulf of Mexico for the 2012-2014 time periods. The highest SSS value occurred in April 2013 with the value of 36.72 psu while the lowest value (35.91 psu) was observed in July 2014. Based on the monthly distribution maps which will be demonstrated in the literature, it was observed that east part of the region has lower salinity values than the west part for all months mainly because of the currents which originate from low saline waters of the Caribbean Sea and furthermore the eastward currents like loop current. Also the minimum amounts of salinity occur in coastal waters where the river runoffs make fresh the high saline waters. Our next goal here is to study the patterns of sea surface temperature (SST), chlorophyll-a (CHLa) and fresh water flux (FWF) and examine the contributions of them to SSS variations. So by computing correlation coefficients, the values obtained for SST, FWF and CHLa are 0.7, 0.22 and 0.01 respectively which indicated high correlation of SST on SSS variations. Also by considering the spatial distribution based on the annual means, it found that there is a relationship between the SSS, SST, CHLa and the latitude in the study region which can be interpreted by developing a mathematical model.
GROUND FILTERING LiDAR DATA BASED ON MULTI-SCALE ANALYSIS OF HEIGHT DIFFERENCE THRESHOLD
Authors: Rashidi P. , Rastiveis H.
Published: September 2017

Abstract. Separating point clouds into ground and non-ground points is a necessary step to generate digital terrain model (DTM) from LiDAR dataset. In this research, a new method based on multi-scale analysis of height difference threshold is proposed for ground filtering of LiDAR data. The proposed method utilizes three windows with different sizes in small, average and large to cover the entire LiDAR point clouds, then with a height difference threshold, point clouds can be separated to ground and non-ground in each local window. Meanwhile, the best threshold values for size of windows are considered based on physical characteristics of the ground surface and size of objects. Also, the minimum of height of object in each window selected as height difference threshold. In order to evaluate the performance of the proposed algorithm, two datasets in rural and urban area were applied. The overall accuracy in rural and urban area was 96.06% and 94.88% respectively. These results of the filtering showed that the proposed method can successfully filters non-ground points from LiDAR point clouds despite of the data area.
STATISTICAL METHOD TO OVERCOME OVERFITTING ISSUE IN RATIONAL FUNCTION MODELS
Authors: Alizadeh Moghaddam S. H. , Mokhtarzade M. , Alizadeh Naeini A. , Alizadeh Moghaddam S. A.
Published: September 2017

Abstract. Rational function models (RFMs) are known as one of the most appealing models which are extensively applied in geometric correction of satellite images and map production. Overfitting is a common issue, in the case of terrain dependent RFMs, that degrades the accuracy of RFMs-derived geospatial products. This issue, resulting from the high number of RFMs’ parameters, leads to ill-posedness of the RFMs. To tackle this problem, in this study, a fast and robust statistical approach is proposed and compared to Tikhonov regularization (TR) method, as a frequently-used solution to RFMs’ overfitting. In the proposed method, a statistical test, namely, significance test is applied to search for the RFMs’ parameters that are resistant against overfitting issue. The performance of the proposed method was evaluated for two real data sets of Cartosat-1 satellite images. The obtained results demonstrate the efficiency of the proposed method in term of the achievable level of accuracy. This technique, indeed, shows an improvement of 50–80% over the TR.
INVESTIGATION ON THE URMIA LAKE DROUGHT USING SATELLITE ALTIMETRY AND WEATHER REANALYSIS PRODUCTS
Authors: Sabzehee F. , Nafisi V. , Iran Pour S.
Published: September 2017

Abstract. The Urmia Lake is located in northwestern Iran. It is the sixth largest salt lake in the world. In the past 14 years, this lake strongly faced to the water level decrease and surface water reduction. Several space-borne techniques have been employed for detecting the hydrological cycle and its inter-annual changes. The goal of this paper is to investigate weather reanalysis products such as specific humidity of NCEP model and a total column water vapor extracted from ECMWF model and other techniques such as satellite altimetry that have been powerful for the identification of some drought factors of the Urmia Lake. In this study, we used satellite altimetry data from ENVISAT and CryoSat-2 to monitor the Lake water level and optical satellite imagery to determine the surface water extent of the lake during 2002 to 2014. Our altimetry and satellite imagery results reveal decrease in height and area of Urmia Lake over the last 14 years. The lake surface area and the water level decreased continuously at approximately 2666 km2 and 3.5 m respectively. Moreover, we employed the monthly 0.125°×0.125° datasets of the ECMWF data and 2.5°×2.5° datasets of the NCEP data to demonstrate amount of water vapor in the atmosphere over the lake. The total column water vapor extracted from ECMWF data and specific humidity of NCEP data show similar behavior. Results indicate that no meaningful correlation between the altimetry data and weather reanalysis models exists. The weather reanalysis models do not indicate descending variations during 2000 until 2014. For interpreting the situation and studying the climate change and man-made impacts on the drought in this region, we need to use ground-based data such as wind speed, precipitation, barometric pressure and temperature.
SPATIAL INTERPOLATION OF AEROSOL OPTICAL DEPTH POLLUTION: COMPARISON OF METHODS FOR THE DEVELOPMENT OF AEROSOL DISTRIBUTION
Authors: Safarpour S. , Abdullah K. , Lim H. S. , Dadras M.
Published: September 2017

Abstract. Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters are important due to health impact. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. Seasonal aerosol optical depth (AOD) values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA’s Terra satellites, for the 10 years period of 2000 - 2010 were used to test 7 different spatial interpolation methods in the present study. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as root mean square error (RMSE) and correlation coefficient. Based on the RMSE and R values of predictions made using measured values from 2000 to 2010, Radial Basis Functions (RBFs) yielded the best results for spring, summer and winter and ordinary kriging yielded the best results for fall.
TREND ANALYSIS OF SOIL SALINITY IN DIFFERENT LAND COVER TYPES USING LANDSAT TIME SERIES DATA (CASE STUDY BAKHTEGAN SALT LAKE)
Authors: Taghadosi M. M. , Hasanlou M.
Published: September 2017

Abstract. Soil salinity is one of the main causes of desertification and land degradation which has negative impacts on soil fertility and crop productivity. Monitoring salt affected areas and assessing land cover changes, which caused by salinization, can be an effective approach to rehabilitate saline soils and prevent further salinization of agricultural fields. Using potential of satellite imagery taken over time along with remote sensing techniques, makes it possible to determine salinity changes at regional scales. This study deals with monitoring salinity changes and trend of the expansion in different land cover types of Bakhtegan Salt Lake district during the last two decades using multi-temporal Landsat images. For this purpose, per-pixel trend analysis of soil salinity during years 2000 to 2016 was performed and slope index maps of the best salinity indicators were generated for each pixel in the scene. The results of this study revealed that vegetation indices (GDVI and EVI) and also salinity indices (SI-1 and SI-3) have great potential to assess soil salinity trends in vegetation and bare soil lands respectively due to more sensitivity to salt features over years of study. In addition, images of May had the best performance to highlight changes in pixels among different months of the year. A comparative analysis of different slope index maps shows that more than 76% of vegetated areas have experienced negative trends during 17 years, of which about 34% are moderately and highly saline. This percent is increased to 92% for bare soil lands and 29% of salt affected soils had severe salinization. It can be concluded that the areas, which are close to the lake, are more affected by salinity and salts from the lake were brought into the soil which will lead to loss of soil productivity ultimately.
ASSESSMENT OF NORMALIZATION TECHNIQUES ON THE ACCURACY OF HYPERSPECTRAL DATA CLUSTERING
Authors: Alizadeh Naeini A. , Babadi M. , Homayouni S.
Published: September 2017

Abstract. Partitioning clustering algorithms, such as k-means, is the most widely used clustering algorithms in the remote sensing community. They are the process of identifying clusters within multidimensional data based on some similarity measures (SM). SMs assign more weights to features with large ranges than those with small ranges. In this way, small-range features are suppressed by large-range features so that they cannot have any effect during clustering procedure. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed images. To address this problem, the feature normalization (FN) can be used. However, since different FN methods have different performances, in this study, the effects of ten FN methods on hyperspectral data clustering were studied. The proposed method was implemented on both real and synthetic hyperspectral datasets. The evaluations demonstrated that FN could lead to better results than the case that FN is not performed. More importantly, obtained results showed that the rank-based FN with 15.7% and 12.8% improvement, respectively, in the synthetic and real datasets can be considered as the best FN method for hyperspectral data clustering.
APPLICATION OF EXTENDED KALMAN FILTER IN PERSISTANT SCATTERER INTERFEROMETRY TO ENHACE THE ACCURACY OF UNWRAPPING PROCESS
Authors: Tavakkoli Estahbanat A. , Dehghani M.
Published: September 2017

Abstract. In interferometry technique, phases have been modulated between 0-2π. Finding the number of integer phases missed when they were wrapped is the main goal of unwrapping algorithms. Although the density of points in conventional interferometry is high, this is not effective in some cases such as large temporal baselines or noisy interferograms. Due to existing noisy pixels, not only it does not improve results, but also it leads to some unwrapping errors during interferogram unwrapping. In PS technique, because of the sparse PS pixels, scientists are confronted with a problem to unwrap phases. Due to the irregular data separation, conventional methods are sterile. Unwrapping techniques are divided in to path-independent and path-dependent in the case of unwrapping paths. A region-growing method which is a path-dependent technique has been used to unwrap PS data. In this paper an idea of EKF has been generalized on PS data. This algorithm is applied to consider the nonlinearity of PS unwrapping problem as well as conventional unwrapping problem. A pulse-pair method enhanced with singular value decomposition (SVD) has been used to estimate spectral shift from interferometric power spectral density in 7*7 local windows. Furthermore, a hybrid cost-map is used to manage the unwrapping path. This algorithm has been implemented on simulated PS data. To form a sparse dataset, A few points from regular grid are randomly selected and the RMSE of results and true unambiguous phases in presented to validate presented approach. The results of this algorithm and true unwrapped phases were completely identical.
QUANTITATIVE RISK MAPPING OF URBAN GAS PIPELINE NETWORKS USING GIS
Authors: Azari P. , Karimi M.
Published: September 2017

Abstract. Natural gas is considered an important source of energy in the world. By increasing growth of urbanization, urban gas pipelines which transmit natural gas from transmission pipelines to consumers, will become a dense network. The increase in the density of urban pipelines will influence probability of occurring bad accidents in urban areas. These accidents have a catastrophic effect on people and their property. Within the next few years, risk mapping will become an important component in urban planning and management of large cities in order to decrease the probability of accident and to control them. Therefore, it is important to assess risk values and determine their location on urban map using an appropriate method. In the history of risk analysis of urban natural gas pipeline networks, the pipelines has always been considered one by one and their density in urban area has not been considered. The aim of this study is to determine the effect of several pipelines on the risk value of a specific grid point. This paper outlines a quantitative risk assessment method for analysing the risk of urban natural gas pipeline networks. It consists of two main parts: failure rate calculation where the EGIG historical data are used and fatal length calculation that involves calculation of gas release and fatality rate of consequences. We consider jet fire, fireball and explosion for investigating the consequences of gas pipeline failure. The outcome of this method is an individual risk and is shown as a risk map.
MULTI-AGENT SIMULATION OF ALLOCATING AND ROUTING AMBULANCES UNDER CONDITION OF STREET BLOCKAGE AFTER NATURAL DISASTER
Authors: Azimi S. , Delavar M. R. , Rajabifard A.
Published: September 2017

Abstract. In response to natural disasters, efficient planning for optimum allocation of the medical assistance to wounded as fast as possible and wayfinding of first responders immediately to minimize the risk of natural disasters are of prime importance. This paper aims to propose a multi-agent based modeling for optimum allocation of space to emergency centers according to the population, street network and number of ambulances in emergency centers by constraint network Voronoi diagrams, wayfinding of ambulances from emergency centers to the wounded locations and return based on the minimum ambulances travel time and path length implemented by NSGA𝜫 and the use of smart city facilities to accelerate the rescue operation. Simulated annealing algorithm has been used for minimizing the difference between demands and supplies of the constrained network Voronoi diagrams. In the proposed multi-agent system, after delivering the location of the wounded and their symptoms, the constraint network Voronoi diagram for each emergency center is determined. This process was performed simultaneously for the multi-injuries in different Voronoi diagrams. In the proposed multi-agent system, the priority of the injuries for receiving medical assistance and facilities of the smart city for reporting the blocked streets was considered. Tehran Municipality District 5 was considered as the study area and during 3 minutes intervals, the volunteers reported the blocked street. The difference between the supply and the demand divided to the supply in each Voronoi diagram decreased to 0.1601. In the proposed multi-agent system, the response time of the ambulances is decreased about 36.7%.
COLLABORATIVE MULTI-SCALE 3D CITY AND INFRASTRUCTURE MODELING AND SIMULATION
Authors: Breunig M. , Borrmann A. , Rank E. , Hinz S. , Kolbe T. , Schilcher M. , Mundani R.-P. , Jubierre J. R. , Flurl M. , Thomsen A. , Donaubauer A. , Ji Y. , Urban S. , Laun S. , Vilgertshofer S. , Willenborg B. , Menninghaus M. , Steuer H. , Wursthorn S. , Leitloff J. , Al-Doori M. , Mazroobsemnani N
Published: September 2017

Abstract. Computer-aided collaborative and multi-scale 3D planning are challenges for complex railway and subway track infrastructure projects in the built environment. Many legal, economic, environmental, and structural requirements have to be taken into account. The stringent use of 3D models in the different phases of the planning process facilitates communication and collaboration between the stake holders such as civil engineers, geological engineers, and decision makers. This paper presents concepts, developments, and experiences gained by an interdisciplinary research group coming from civil engineering informatics and geo-informatics banding together skills of both, the Building Information Modeling and the 3D GIS world. New approaches including the development of a collaborative platform and 3D multi-scale modelling are proposed for collaborative planning and simulation to improve the digital 3D planning of subway tracks and other infrastructures. Experiences during this research and lessons learned are presented as well as an outlook on future research focusing on Building Information Modeling and 3D GIS applications for cities of the future.
OPTIMIZING ENERGY CONSUMPTION IN VEHICULAR SENSOR NETWORKS BY CLUSTERING USING FUZZY C-MEANS AND FUZZY SUBTRACTIVE ALGORITHMS
Authors: Ebrahimi A. , Pahlavani P. , Masoumi Z.
Published: September 2017

Abstract. Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.
A NOVEL APPROACH FOR 3D NEIGHBOURHOOD ANALYSIS
Authors: Emamgholian S. , Taleai M. , Shojaei D.
Published: September 2017

Abstract. Population growth and lack of land in urban areas have caused massive developments such as high rises and underground infrastructures. Land authorities in the international context recognizes 3D cadastres as a solution to efficiently manage these developments in complex cities. Although a 2D cadastre does not efficiently register these developments, it is currently being used in many jurisdictions for registering land and property information. Limitations in analysis and presentation are considered as examples of such limitations. 3D neighbourhood analysis by automatically finding 3D spaces has become an issue of major interest in recent years. Whereas the neighbourhood analysis has been in the focus of research, the idea of 3D neighbourhood analysis has rarely been addressed in 3 dimensional information systems (3D GIS) analysis. In this paper, a novel approach for 3D neighbourhood analysis has been proposed by recording spatial and descriptive information of the apartment units and easements. This approach uses the coordinates of the subject apartment unit to find the neighbour spaces. By considering a buffer around the edges of the unit, neighbour spaces are accurately detected. This method was implemented in ESRI ArcScene and three case studies were defined to test the efficiency of this approach. The results show that spaces are accurately detected in various complex scenarios. This approach can also be applied for other applications such as property management and disaster management in order to find the affected apartments around a defined space.
BUILT-UP AREA DETECTION BASED ON SUBSPACE PROJECTIONS USING POLARIMETRIC SAR DATA
Authors: Bordbari R. , Maghsoudi Y. , Salehi M.
Published: September 2017

Abstract. The task of detecting and identifying objects remotely has long been an area of intense interest and active research. Active sensing of objects with radio waves is a whole new domain of target detection which is made available by radar remote sensors. Land cover/use information extraction is one of the most important applications of radar remote sensing, especially in urban areas. In this paper, we take a new look at the built-up area extraction problem in polarimetric SAR (PolSAR) data and assume canonical scattering mechanisms as our signal sources which combination of them with appropriate weight fractions formed a scattering vector of each pixel. The set of the scattering mechanisms is divided into two groups: the scattering mechanism of built-up area, and non-objected scattering mechanisms. Then, we describe a technique which simultaneously annihilates the effect of non-objected scattering mechanisms, and detects the presence of a scattering mechanism of interest. The experimental results on several quad-polarimetric datasets show the significant agreement with expected results, while saving computational complexity.
ESTIMATING ORIGIN-DESTINATION MATRICES USING AN EFFICIENT MOTH FLAME-BASED SPATIAL CLUSTERING APPROACH
Authors: Heidari A. A. , Moayedi A. , Ali Abbaspour R.
Published: September 2017

Abstract. Automated fare collection (AFC) systems are regarded as valuable resources for public transport planners. In this paper, the AFC data are utilized to analysis and extract mobility patterns in a public transportation system. For this purpose, the smart card data are inserted into a proposed metaheuristic-based aggregation model and then converted to O-D matrix between stops, since the size of O-D matrices makes it difficult to reproduce the measured passenger flows precisely. The proposed strategy is applied to a case study from Haaglanden, Netherlands. In this research, moth-flame optimizer (MFO) is utilized and evaluated for the first time as a new metaheuristic algorithm (MA) in estimating transit origin-destination matrices. The MFO is a novel, efficient swarm-based MA inspired from the celestial navigation of moth insects in nature. To investigate the capabilities of the proposed MFO-based approach, it is compared to methods that utilize the K-means algorithm, gray wolf optimization algorithm (GWO) and genetic algorithm (GA). The sum of the intra-cluster distances and computational time of operations are considered as the evaluation criteria to assess the efficacy of the optimizers. The optimality of solutions of different algorithms is measured in detail. The traveler's behavior is analyzed to achieve to a smooth and optimized transport system. The results reveal that the proposed MFO-based aggregation strategy can outperform other evaluated approaches in terms of convergence tendency and optimality of the results. The results show that it can be utilized as an efficient approach to estimating the transit O-D matrices.
VOLUNTEERED GEOGRAPHIC INFORMATION (VGI) AND ENVIRONMENTAL MONITORING, A NEW APPROACH IN DEVELOPING COUNTRIES
Authors: Manouchehri M. , Kiavarz Moghaddam M.
Published: September 2017

Abstract. Increasing world population and unprecedented expansion of urbanization in the world has caused many environmental problems. The relationship between man and the environment is bidirectional one that have great short-term and long-term impacts on the cities and regions. The best way to deal with it is the participation of the people themselves. The use of new technologies has now become one of the most important methods for monitoring the environment that can increase the participation of citizens, improving environmental problems to provide the cheapest and the most accessible form. Developing countries such as Iran, which faces enormous environmental problems are suitable for the development of technological methods of monitoring. Large population and citizens’ participation feasibility using VGI can have a positive effect on developing countries. Finally, by using F-VGI that ensures the validity and accuracy of data we can access an appropriate platform that leads us to suitable model for environment monitoring in the form of the application.
A NEW MULTI-CRITERIA EVALUATION MODEL BASED ON THE COMBINATION OF NON-ADDITIVE FUZZY AHP, CHOQUET INTEGRAL AND SUGENO λ-MEASURE
Authors: Nadi S. , Samiei M. , Salari H. R. , Karami N.
Published: September 2017

Abstract. This paper proposes a new model for multi-criteria evaluation under uncertain condition. In this model we consider the interaction between criteria as one of the most challenging issues especially in the presence of uncertainty. In this case usual pairwise comparisons and weighted sum cannot be used to calculate the importance of criteria and to aggregate them. Our model is based on the combination of non-additive fuzzy linguistic preference relation AHP (FLPRAHP), Choquet integral and Sugeno λ-measure. The proposed model capture fuzzy preferences of users and fuzzy values of criteria and uses Sugeno λ -measure to determine the importance of criteria and their interaction. Then, integrating Choquet integral and FLPRAHP, all the interaction between criteria are taken in to account with least number of comparison and the final score for each alternative is determined. So we would model a comprehensive set of interactions between criteria that can lead us to more reliable result. An illustrative example presents the effectiveness and capability of the proposed model to evaluate different alternatives in a multi-criteria decision problem.
GROUNDWATER EXPLORATION USING FUZZY LOGIC APPROACH IN GIS FOR AN
AREA AROUND AN ANTICLINE, FARS PROVINCE
Authors: Rafati S. , Nikeghbal M.
Published: September 2017

Abstract. In the recent years, the over-use of water resource due to the population growth and industrial developing has become serious. With attention to demand for water, it's essential to explore and evaluate new water resource and mapping its potential. In this paper, a fuzzy set theory, as a knowledge driven approach for map combination, was applied to produce a potential map for ground water resources. To achieve this objective, a variety of spatial data including geology, slope, elevation, drainage, fault and joint were complied. Then fuzzy membership functions were evaluated for each data layer. These data were integrated using the fuzzy γ operator with a value of γ = 0.95. The final map indicates Quaternary formation consists of alluvial deposits near the 200 meter distance from the anti-cline as a suitable area for groundwater resource. Finding out an accurate method which accelerates processing for determining the location of groundwater before drilling is an effective solution leading to save budget and time.
ASSESSING URBAN STREETS NETWORK VULNERABILITY AGAINST EARTHQUAKE USING GIS – CASE STUDY: 6TH ZONE OF TEHRAN
Authors: Rastegar A.
Published: September 2017

Abstract. Great earthquakes cause huge damages to human life. Street networks vulnerability makes the rescue operation to encounter serious difficulties especially at the first 72 hours after the incident. Today, physical expansion and high density of great cities, due to narrow access roads, large distance from medical care centers and location at areas with high seismic risk, will lead to a perilous and unpredictable situation in case of the earthquake. Zone # 6 of Tehran, with 229,980 population (3.6% of city population) and 20 km2 area (3.2% of city area), is one of the main municipal zones of Tehran (Iran center of statistics, 2006). Major land-uses, like ministries, embassies, universities, general hospitals and medical centers, big financial firms and so on, manifest the high importance of this region on local and national scale. In this paper, by employing indexes such as access to medical centers, street inclusion, building and population density, land-use, PGA and building quality, vulnerability degree of street networks in zone #6 against the earthquake is calculated through overlaying maps and data in combination with IHWP method and GIS. This article concludes that buildings alongside the streets with high population and building density, low building quality, far to rescue centers and high level of inclusion represent high rate of vulnerability, compared with other buildings. Also, by moving on from north to south of the zone, the vulnerability increases. Likewise, highways and streets with substantial width and low building and population density hold little values of vulnerability.
GIS-BASED SOLAR AND WIND TURBINE SITE SELECTION USING MULTI-CRITERIA ANALYSIS: CASE STUDY TEHRAN, IRAN
Authors: Sadeghi M. , Karimi M.
Published: September 2017

Abstract. Renewable energy has less negative impacts on environment than fossil fuels. Iran has many resources for renewable energy exploitation but because of their high price, it has not been developed. Site selection is an important step for utilizing large investments like solar farms and wind turbines. Multi-criteria evaluation methods are commonly used for site selection. The purpose of this paper is to determine suitable sites for solar farm and wind turbine using GIS and AHP in Tehran, in order to generate a distributed network to increase power network stability. The final land suitability index was grouped in four categories as “most suitable”, “suitable”, “moderate” and “low suitable”. As a result 94.61% (789939.63 km2) is low suitable, 4.47% (37337.17 km2) is moderate, 0.59% (4964.22 km2) is suitable and 0.32% (2680.70 km2) is the most suitable for building wind turbine and for solar farm, 44.07% (8116.88 km2) is low suitable, 12.81% (2359.79 km2) is moderate, 35.10% (6464.29 km2) is suitable and 8.02% (1477.28 km2) is the most suitable.
PROVIDING THE FIRE RISK MAP IN FOREST AREA USING A GEOGRAPHICALLY WEIGHTED REGRESSION MODEL WITH GAUSSIN KERNEL AND MODIS IMAGES, A CASE STUDY: GOLESTAN PROVINCE
Authors: Shah-Heydari pour A. , Pahlavani P. , Bigdeli B.
Published: September 2017

Abstract. According to the industrialization of cities and the apparent increase in pollutants and greenhouse gases, the importance of forests as the natural lungs of the earth is felt more than ever to clean these pollutants. Annually, a large part of the forests is destroyed due to the lack of timely action during the fire. Knowledge about areas with a high-risk of fire and equipping these areas by constructing access routes and allocating the fire-fighting equipment can help to eliminate the destruction of the forest. In this research, the fire risk of region was forecasted and the risk map of that was provided using MODIS images by applying geographically weighted regression model with Gaussian kernel and ordinary least squares over the effective parameters in forest fire including distance from residential areas, distance from the river, distance from the road, height, slope, aspect, soil type, land use, average temperature, wind speed, and rainfall. After the evaluation, it was found that the geographically weighted regression model with Gaussian kernel forecasted 93.4% of the all fire points properly, however the ordinary least squares method could forecast properly only 66% of the fire points.