فهرست مطالب ara toomanian

Nowadays, extracting knowledge from voluminous and distributed spatial data is a vital need and ultimate goal of spatial data community. In this regard, a number of spatial data mining methods and algorithms have been developed, but there are a number of reasons these methods could not be cover spatial data community requirements. Like early years of GIS development, collecting data on a repository is still applied for knowledge extraction. In adaption, interoperability and usability are two important issues that have been neglected in traditional knowledge extraction methods. To respond the requirements from knowledge perspective a set of Web service-based geographical knowledge extraction is necessary. The services required a technological infrastructure or framework to extract useful knowledge from the different and geographically distributed data sources. In order to overcome this challenges, a general Spatial Knowledge Infrastructure (SKI) proposed to facilitate knowledge extraction in distributed scenarios. The SKI is a widespread interoperable framework through which geographical/spatial knowledge is created, organized, shared, managed and used in many domains. It creates a mechanism to make the necessary processes of geographic knowledge with the highest efficiency and usability. SKI's primary goal is to combine Spatial Data Infrastructure (SDI), Spatial Web Services (SWS) and Spatial Data Mining (SDM) concepts to facilitate geographical knowledge extraction as a collection of services. With a combination of SDI, SWS and SDM concepts this study presents the nature of SKI, including definition, levels, components, and architecture.

Among the types of airborne particles, particles with a diameter of less than 10 microns have many adverse effects on human health. Meteorological parameters and the movement of a large volume of vehicles are considered the most important modulating factors in the distribution and concentration of atmospheric pollutants. In this study, in order to predict the concentration of PM-10 pollutant during a long-term interval in Tehran city, GA-ANFIS hybrid model was used. Wind speed, wind direction, temperature, relative humidity and traffic volume were considered as inputs and pollutant concentration PM_10 as the output of the model. The results of the calculation of the performance indicators showed that the combined GA-ANFIS model provides a better framework than the ANFIS model in predicting the pollutant concentration PM_10. In order to evaluate the spatio-temporal patterns of PM_10 pollutant concentration and to identify hot and cold spots in Tehran city, local Moran's statistic and Ard-J Gettys statistic were calculated. The results showed that there is a high level of clustering of PM_10 pollutant in Tehran (with 95% confidence level). The clusters of PM_10 have divided the city into two northern and southern parts so that most of the cold spots in the northern half and the hot spots in the south have spread to the center of the city

The findings of the associational rule extraction revealed a spatial relationship between the distance from the police station and the usage of opiates and hashish, which has a direct relationship on the use of the park. Additionally, there is a direct correlation between the use of marijuana and grass and police stations, indicating that these drugs have been used close to these institutions. The majority of hashish, opium, and grass drug use occur in close proximity to roads, parks, and schools. Additionally, compared to other substances, marijuana and grass have a direct spatial relationship with the police station, and use of these drugs can be witnessed close to the station. Despite being extremely prevalent in the database, it appears that the opium drug did not significantly correlate spatially with the chosen uses. Finally, the top 10 laws were determined and their effects on confidence, support, and lifting were examined. In the context of a spatial information system, the usage of spatial distribution functions can be useful for pinpointing crime hotspots and assisting law enforcement with management decisions. With its research of crime trends and crime statistics, it can assist the police and other relevant agencies in locating potential crime hotspots and preventing them in the future. Additionally, it might be highly helpful to apply data mining techniques to anticipate and prevent crimes as well as their locations.

Background and Objective:

 Soil as a heterogeneous natural resource and the largest organic carbon storage in terrestrial ecosystems is composed of complicated processes and mechanisms. The necessity of accurately estimating soil properties on the national and regional scales for improving soil management, and understanding their influence on agriculture have resulted in attracting researchers’ attentions to this field. Soil Organic Matter (SOM) is considered as an indicator of soil quality in fertility and food production. It is also considered as a key variable in environmental and agricultural issues. Thus, using rapid and cost effective and more accuracy estimation of the SOM content in soil resources assessment and management can be helpful. In precision agriculture, the scale of soil data required for management of lands and products is very large. The scale of collecting filed data usually cannot fulfil those needs. Sampling, preparing and analyzing the large number of soil samples as well as producing the distribution map for large areas are very difficult. In addition, traditional laboratory methods of soil analysis are boring, time-consuming, and costly. In fact, they need specialized laboratory operators. The aim of the present study is to compare the performance of the two Partial Least Squares Regression (PLSR) and Boosted Regression Tree (BRT) for predicting SOM using VNIR spectrometry data. With the use of combining Wavelet transform and diagnosis of independent bands, noises existing in soil spectroscopic data has reduced. In addition, independent and effective spectra and bands in spectroscopy of SOM were selected. Consequently, in the present research, Wavelet-PCA-PLSR and Wavelet-PCA- BRT models were developed and performance were assessed.

Materials and Methods:

 42 surface (0-30cm) soil samples in the heterogeneous areas of urban-agricultural regions in Tehran province were collected. Soil Organic Carbon (OC) measured using Walki Black method and the samples’ spectrums were measured by ASD FieldSpec-3 spectrometer. First and second derivitation of spectral reflectance and absorbance were calculated. To reduce noises and smooth the spectrum, Sym8 matrix function of wavelet transform was used, wavelet transform is conducted to show and reconstruct characteristics in the spectrum. Principal component analysis and Hotelling's T2 test with 95% confidence level were used for outlier detection. PLSR and BRT was conducted onreflectance, absorbance and their first and second derivatives, at five levels of wavelet transform. Then, by comparing the results, the appropriate model was selected via validation. For doing the PLSR in nonlinear data, Kernel functions were used. When using numerical samples, regression trees are used instead of decision trees. But their processes are the same. In regression trees, the greedy algorithm was used. Therefore, by answering the binary question through which node the maximum data about respons variable is obtained, the root node and its two children are obtained. Producing the structure of trees is recursively repeated and a typical stopping criterion is considered. The stopping criterion can be as achievement to a split which cannot be divided and provides fewer data, or when data in the node contain 5% of the total data. Moreover, the tree size should be minimized. For splitting the node, the Ginny factor, entropy factor, etc. were used for minimizing those factors. In addition, the total square error is calculated in each branches and those with minimized values are selected. In addition, in the regression tree, the pruning process is employed for over-fitting. The BRT consists of the two regression tree and boosting techniques for improving the predictability of each of them. For calibration and validation of the model, 30 and 12 soil samples were randomly selected, respectively and R2 and RMSE were used for quantify the accuracy of models. Moreover, to select the best production factor of the PLSR mode, explained variance residual values and RMSE of validation were considered. Finally, soil organic matter map was produced using Landsat OLI satellite imagery and the proofed method for the study area.

Results and Discussion :

The SOM value acceptably, the creation of continuous mappings with more accuracy based on noise reduction and retention of suitable data have always received researchers’ attentions. The present study tried to find the better method such a more accurate quantization of SOM using soil spectroscopic data. Using wavelet transform and outlier removal based on Hotelling's T2 via the PCA, the suitable data were extracted for producing the more accurate quantization. In this method, independent and effective bands or spectra remain in the model, while Lin et al. used wavelet transform and correlation techniques for selecting appropriate bands in estimating SOM. Since the soil reflectance is more complex and affected by several factors, using correlation method in these heterogeneous areas such as the area studied in the present study does not lead to acceptable results. Considering the data values, the unsupervised PCA method calculates principle components and eigenvalues and eigenvectors. It also tries to maximize the covariance matrix based on Singular Value Decomposition (SVD). SOM estimation models were developed using the PLSR and BRT for reflectance and absurbance spectra and their first and second derivation. Based on the results, the BRT method with RMSE and R2 values as 0.58 and 0.94, respectively leads in the better results for the data of the second derivation of reflectance. Moreover, values of RMSE and R2 in the PLSR were obtained as 1.0338 and 0.938, respectively for the data related to the second derivation of reflectance. However, comparing RMSE of the BRT and PLSR shows better results of the BRT model.

Conclusion:

 In that field measurements of chemical properties of soil such as organic matters are critically time-consuming and costly. Furthermore, measuring those properties is not possible in the large samples. So, the results of the present study indicate that in heterogeneous agricultural-urban areas, potential of the developed models such as wavelet-PCA-PLSR and wavelet-PCA-BRT can be used for estimating SOM. Meanwhile, these two algorithms do not make distributional assumptions and therefore, there are no strong assumptions about normality. Using continuous functions and satellite imagery, the map of the level of SOM in large scales can be prepared in order that it can be utilized in studies such as cultivation potential, soil fertility, and sustainable development of soil.

Allocating the largest share of waste management costs to the collection sector on the one hand, and the necessity for operational efficiency of this system by reducing the spent time on the other hand, highlights the importance of optimization of waste collection and transport system as a principle in waste design and management. The importance of this issue is that even little improvement in the collection system could have a considerable impact on reducing total costs. Therefore, in order to develop a management system for (fleet collection and transportation), this paper presents an appropriate model for vehicles (truck) routing that determines the appropriate truck route, timetable, starting point, destination, and routes of each truck. The main innovation of this paper is the application of WebGIS and the development of optimal routing for waste collection vehicles with an emphasis on spatial parameters and features. To obtain an appropriate answer, routing vehicles with limited waste collection capacity were used in order to minimize the travel time and consider limiting parameters including, 1- maximum total allowable travel time and 2 – maximum allowable covered distance of vehicles. With the aim of optimal management of waste collection, features such as the amount of waste in each trash bin, the amount of time required to discharge each trash bin, the direction of network traffic, vehicle operating hours, road speed limits, time impedance and distance impedance of each edge were considered. The results show that in comparison to the current fleet, the proposed approach reduces travel time, distance, and fuel consumption by 14%, 24%, and 35%, respectively. User satisfaction with the WebGIS was also assessed indicating that 89% of the users are satisfied with this system.

Analyzing and monitoring the spatial-temporal patterns of the new coronavirus disease (COVID-19) pandemic can assist local authorities and researchers in detecting disease outbreaks in the early stages. Because of different socioeconomic profiles in Tehranchr('39')s areas, we will provide a clear picture of the pandemic distribution in Tehranchr('39')s neighbourhoods during the first months of its spread from February to July 2020, employing a spatial-temporal analysis applying the geographical information system (GIS). Disease rates were estimated by location during the 5 months, and hot spots and cold spots were highlighted.

This study was performed using the COVID-19 incident cases and deaths recorded in the Medical Care Monitoring Centre from February 20, to July 20, 2020. The local Getis-Ord Gi* method was applied to identify the hotspots where the infectious disease distribution had significantly clustered spatially. A statistical analysis for incidence and mortality rates and hot spots was conducted using ArcGIS 10.7 software.

  The addresses of 43,000 Tehrani patients (15,514 confirmed COVID-19 cases and 27,486 diagnosed as probable cases) were changed in its Geo-codes in the GIS. The highest incidence rate from February to July 2020 was 48 per 10,000 and the highest 5-month incidence rate belonged to central and eastern neighbourhoods.  According to the Cumulative Population density of patients, the higher number is estimated by more than 2500 people in the area; however, the lower number is highlighted by about 500 people in the neighborhood. Also, the results from the local Getis-Ord Gi* method indicate that COVID-19 has formed a hotspot in the eastern, southeast, and central districts in Tehran since February. We also observed a death rate hot spot in eastern areas.

Because of the spread of COVID-19 disease throughout Tehranchr('39')s neighborhoods with different socioeconomic status, it seems essential to pay attention to health behaviors to prevent the next waves of the disease. The findings suggest that disease distribution has formed a hot spot in Tehranchr('39')s eastern and central regions.

Travel time data is important for most of the vehicle routing problems (VRP) aimed at modeling time constraints like customer time window as well as time objectives, such as minimizing travel time and waiting time. One of the most important advantages of precise travel times in VRPs is the reduction of total travel time and increasing route reliability, which with more accurate travel time data better removes time constraints. The obvious reasons for not using variable travel time in VRP are related to three factors. First, conventional VRP algorithms cannot consider variable travel time without major structural changes. Second, all parameters affecting travel time are often not used. Third, collecting the required data is very difficult. Therefore, designing appropriate models with enough accuracy in predicting travel time based on the effective variables is a necessity in the transportation planning process.In this paper, the appropriate variables for measuring the impact of spatial-temporal and traffic factors on the travel time of medical waste collection vehicles in a single route were defined and by examining and implementing the proposed model, the impact of these variables on the travel time was measured. To estimate the travel time, we used the proposed model called spatial-temporal neural network (ST-NN) which can examine the effect of variables at any moment of the travel time. The study area is Tehran metropolis with its entire urban passages network. The required data were first pre-processed and then entered into the neural network algorithm as model inputs. The model was

Special Economic Zones (SEZs) are areas controlled by specific legislations so as toattain economic prosperity. These zones are commonly established and controlled bygovernment officials and are primarily characterized by growing population and developingtransport infrastructure. One relevant case is the Pars Special Economic Energy Zone(PSEEZ) situated in the south of Iran, on the northern shores of the Persian Gulf. Thisparticular zone has been formed to extract, refine, and export gas. The coast of the PersianGulf has brought on further expansions in sea transportation, thereby increasing shippingactivities in the area. The chief mode of shipping goods and materials in the PSEEZ isMaritime transportation. Identification of areas where traffic is carried out requires thecollection of spatial data of ships. These spatial data are utilized in several applications formodeling of marine ecosystem. The central purpose of this research is to procure spatialdata required for the PSEEZ using optical remote sensing images. Thus, Landsat-8 andSentinel-2 images spanning from 2013 to 2018 were incorporated to identify ships in thestudy area. The threshold-based method was implemented for detecting the ships usinginfrared bands of 172 images, upon which a total of 3361 ships were identified. The map ofareas affected by ship traffic was prepared using Hot Spot analysis. The results specify thatmore than 80 square kilometers of the marine environment has been affected by ship trafficduring the years 2013- 2018.

Introduction Ecosystem services are the benefits people get from ecosystems and human always needs ecosystem services and products for survival (MEA, 2005). These services are divided into the four categories of productive, regulatory, cultural and life support services; the first three categories are directly affecting people, and the fourth one is critical for the continuation of other services provided by ecosystems (Ayanu et al, 2012). Each of these four categories comprises a wide range of services. Since ecosystem services are not fully tradable in commercial markets or are not quantifiable in a way comparable to economic services and capital production, they are often being ignored in policy making and spatial planning (Costanza et al, 1997; Gowan, 2006). Assessing the impacts of landuse/landcover changes on the ecosystem service values is required for the special economic zones that are experiencing rapid changes in land use/land cover. Pars Special Economic Energy Zone (PSEEZ) located in southern Iran is considered as an example of these areas which has become a national and transnational zone within a short period of time due to activities related to the extraction, exploitation, refining and exporting of gas resources. This zone is one of the areas where extensive industrial investment has been made in less than 10 years (Talebian et al, 2008). This area was established in 1998 to extract oil and gas resources from the South Pars oil field with economic activities carried out in the city of Assaluyeh, Bushehr province. The Nayband national marine park is located in the PSEEZ (Davoodi et al., 2017). Nayband Bay along with a part of Nayband's headland with an area of 19,500 hectares was designated as Nayband Protected Area in Iran. This area, along with parts of Persian Gulf waters, with an approximate area of 49,815 hectares, were registered as the first Iranian marine national park in 2003. The presence of coral reefs, mangrove forests, rocky beaches, and real estuaries make this area one of the most diverse and beautiful coastal ecosystems in the world. There are also various aquatic species, marine mammals such as dolphins and whales, endangered reptiles, such as the green and eagle tip turtle and various kinds of aquatic birds in the area. The construction of PSEEZ has led to the development of petrochemical refineries, which in turn resulted in an increase of population (from 2000 to 60,000) and the expansion of human settlements. On the other hand, the development has led to the expansion of land, sea, and air transport infrastructure. Due to the rapid changes in this region, assessing the impacts of landuse/landcover changes on the ecosystem service values is required. In this regard, remote sensing, as the most important way of obtaining the spatial data, allows the quantification and mapping of these services. This technology brings practical benefits to biodiversity conservation and promotes the sustainable utilization of natural resources within the arena of ecosystem services (Cord et al, 2017; Araujo-Barbosa et al, 2015). Methodology This research emphasises on landuse/landcover changes as the basis of the evaluation of ecosystem services. In assessing the status of changes in the PSEEZ, a surface area of 300,000 hectares (3000 square kilometres) were reviewed over a period of 32 years. Those images of 1986 and 1998 were considered to show the trend of regional changes before the establishment of the PSEEZ, and the image of 2018 was used to show the current changes. In selecting Landsat images, we considered climate conditions as well as the characteristics of the Persian Gulf tide and its effects on mangrove forests. The weather condition of the region is suitable for farming during the winter season, so crop fields in the months of January, February and March, had better resolution than other months of the year captured by remote sensing images. To classify the images of area changes, Random Forests technique was used due to the higher classification accuracy and processing speed compared to the other two methods (Inglada et al, 2016). In assessing the economic value of the ecosystem services in the PSEEZ, landuse/landcover changes and global estimations of ecosystem services are integrated (Costanza et al., 2014). Results and discussion The results indicate that agricultural lands and palm groves had a significant increase of 308.79 and 852.48 hectares before the establishment of the PSEEZ and the mangroves forests decreased by 67.68 hectares. With the establishment of the PSEEZ, human built-ups and mangrove forests were increased by 2756.61 and 113.4 hectares, while Barren lands, agricultural lands and palm groves decreased by 4651.92, 397.53 and 579.33 hectares. The drying of wetlands for the construction of roads and airports has greatly reduced the size of the wetland area with an estimated loss of economic value of ecosystem services of 100 million dollars. The results also indicate that the economic value of the ecosystem services of the the PSEEZ for the years 1986, 1998 and 2018 is equal to 570.02, 393.92 and 463.52 million dollars, respectively. The service function and its changes indicate that erosion control, recreation, nutrient cycling, waste treatment and food production are of the highest value in the study area. In contrast, pollination, gas regulation, soil formation, water supply and biological control have shown the lowest value. It was revealed that during these years, water regulation, habitat/refugia, disturbance regulation, erosion control and recreation had the most changes at 22.772, 12.445, 11.89, 10.791 and 9.192 million dollars in the study area. Conclusion Based on the findings of this research, we conclude: - The status of the Nayband wetland is in a state of warning, and human built-ups in its remaining space should be avoided. - The potential of the recreation ecosystem service of this region should be given more attention and planned to be preserved. - Since it is not possible to classify coral reefs in the region (Haleh and Asaloyeh) with these images, studies are recommended to review their ecosystem services' value and services.

A spatial data infrastructure (SDI) is the technology, policies, standards and related activities necessary to acquire, distribute, use and preserve spatial data. An SDI is a coordinated series of agreements on technology standards, institutional arrangements, and policies that enable the discovery and use of geospatial information by users and for purposes other than those it was created for. Modeling and simulation can facilitate the development of SDI system in different level. This study aims to model the development of local SDI in defensive organization using the system dynamics technique. In this regard, the spatial data management parameters were identified and then three indicators; data security, spatial data standardization and participation units were determined. The indicators were simulated by Vensim software and model validation tests were done using by three categories of model Validation: Structural, structural-oriented behavior and behavior pattern tests. These models can be analyzing the system behavior in different conditions. The results show that the priority of the indicators for planning and policy-making are data security, participation units and then defensive spatial data standardization. At the end the important scenarios for each index and strategies to improve them were presented in the defensive organization in the medium term future.

Life in the modern cities takes shape through interaction with various environmental, socio-economic, infrastructural, health, security, political and cultural conditions. The result of this interaction shapes the quality of urban life (QOUL). Quality of lifeis a complex concept involving social, economic, environmental, physical, psychological and political aspects (El Din et al, 2013). In general, Quality of life (QOL) has been evaluatedbytwo objective and subjective points of view. Researches in this field, have mainly been conducted in the form of social studies and in the macro geographical scales of countries or cities,and less attention has been paid to the spatial differences of the life quality in the complex urban environments. In these studies, the principal components analysis (PCA) method has been the most common method used for combining and overlaying of the life quality indicators (Lo, 1998; Jun, 2006; Li and Weng, 2007; Motakan et al, 2010; HatamiNejad et al, 2014; Messer et al, 2014). But,Oneof the disadvantages of PCA is the possibility of deleting some of the useful information.Using Multi-Criteria Decision-Making (MCDM) and Fuzzy Logic methods can also be useful in spatial modeling of life quality. Moreover, QOL as one of the features of geographical environment is a dynamic concept. This means that this feature changesover time and location. The spatiotemporal modeling of this concept can help monitoringthe quality of urban life and planning for its improvement.

Data and Methods: This study offers a framework and process for spatiotemporal modeling of QOUL. For spatial modeling of QOUL, effective indiceswere taken into consideration at first. In this study,the indicators related to the urban quality of life were extracted in 3 three environmental, infrastructural/physical, and socio-economic dimensions.The Analytical Hierarchy Process (AHP) method was used for weighing the parameters(Uyan, 2013). Then, the indicators were combined with each other using the GammaFuzzyModel(Vafai, 2013) and Vikor-Fuzzy overlay technique(Huang et al, 2009). Furthermore, QOUL was modeled temporally due to the variability of environmental indicators and some of infrastructural / physical indicators during the seasons of the year. For this purpose,the cyclic model (developed based on the snapshot approach (Worboys and Duckham, 2004)) was used. In order to assess the developed framework, the quality of lifewasmodeled at urban blocks level in regions 3,6,11 of the city of Tehran. The obtained results showed that applying multi-criteria decision-making and Fuzzy logicmodels in modeling of life quality is capable of showing the spatial difference oflife quality in urban environments. Based on the results of spatial modeling, the quality of life is more desirable in northern parts of the area (region 3) while the desirability decreases towards the southern areas (region 11). The study of Moran’s spatial autocorrelation index (greater than 0.35 for the results of both models and all seasons) emphasize on the non-randomness of the distribution method of the QOL feature in urban blocks and shows the existence of cluster pattern in the study area.The results of temporal modeling indicated that most of the blocks are more favorable in the spring and autumn seasons than in the winter and summer in terms of environmental conditions.

Nowadays banks, credit and financial institutions are trying to increase profits, reduce costs, compete with rivals, attract customers and increase productivity. One of the factors that contributes to the implementation of these strategies is the optimum locations of branches. Locating the new branches of Mehr Eghtesad bank in the region 1 in Tehran city is the aim of this research. Since the focus of service centers such as banks is on maximal or full service to customers, among the all the covering models, Maximal Covering Location model is chosen as the best option to locate the new bank branches. To this end, related literature about locating bank branches, Geographical Information system (GIS) and maximal covering location problem (MCLP) examined. Then, through library Studies and interviewing with managers and experts, the researcher chose effective criteria and sub criteria for locating bank branches. The weights of criteria and sub criteria were determined through filling the questionnaires by managers. GIS used to extract some input data for the model and weighted maximal covering model (MCLM) with partial covering used to choose the best locations. Mathematical programming model formulated with 363 binary variables, 122 constraints, 121 demand areas, 121 potential points, the 1000 m buffer, α = 0.75, b = 50%, θ = 2 and s= 8 & 30 branches (with two different scenarios) and solved with GAMS optimization software. It is clear that by solving the first scenario, eight suitable locations and second scenario thirty suitable locations to open new branches will be generated.

Planners have always faced a set of spatial conflicts in the allocation of land to a particular activity; this implies that a land may be used for two or more parallel applications. Currently in spatial planning programs recognizing priorities in the land allocation process modeled independently without considering the role of other land-use, as well as potential land-use conflicts is an issue that has been ignored. The purpose of this study is to introduce and use the LUCIS model to identify land allocation priorities along with conflict modeling. To implement the model, using the Python programming language an ArcGIS toolbox has been developed. Due to the developed system, with the definition of inputs, weights and stakeholder priorities, land use modeling will be done automatically. Finally, priorities and also major conflicts in the study area have been identified. Results of this study show that, in addition to having the capabilities of other models in land suitability analysis, LUCIS can properly model existing conflicts. Therefore, it is recommended that a separate part under title "Identification of land suitability conflicts" specified in the descriptive setting of spatial planning programs.

Spatial data is one of the most important and sensitive decisionmaking and planning factors in command and management of military systems. Fulfillment of the mission and goals of the military depends to timely, accurate, applicatory and integrated spatial data. During the past decade spatial data infrastructure (SDI) development has been known one of the most important components of optimal management of spatial data. However, there are a lot of risks with development of SDI in organization. This paper aims to address the identification and assessment of defensive Spatial Data Infrastructure (DSDI) development risks. In this regard, we have extracted the 38 risks in five dimensions of organizational, technical, spatial data, human resources and security
using by studying the literature and doing exploratory deep interview. Then Risk Priority Number (RPN) was calculated for all of 38 risks using by questionnaire and Failure Mode Effects Analysis methods. The results showed that security risks has the highest risk
(RPN=47.55) and spatial data, organizational, technical and human resources risks were ranked respectively after it. Also among the 8 superior ranked risks, the type of security, spatial data and organizational risks were contributed from the majority of the cases.
At the end research, fourteen strategies were presented to successful DSDI planning and development.

Shortage of high quality freshwater is one of the great challenges that human civilization is facing in the 21st century (Shiklomanov, 2000). This issue threatens the social welfare, public health and ecosystem health. On one hand, limited freshwater resources and increasing demand for this vital resource signifies water issue. On the other hand, non-uniformity of spatial and temporal distribution has complicated the management of these problems (Makhdoom, 1999). Climate change is also one of the main reason for worsening the quality and quantity of water while lowering groundwater level (Lambrakis, 2006). Therefore, good management of water resources to predict water demand for the future in different districts is needed. Accordingly, Multi Criteria Decision Making Method (MCDM) is used (Mousseau & Slowinski, 1998). The aim of this study is to compare the results of estimation of the Fuzzy Inference System and Fuzzy Inference System- Monte Carlo models for predicting drinking water demand in different districts of Kermanshah for the year 1400. Factors such as population growth, per capita water usage and urban development have been considered in order to the manage water resources and accurate planning of the Kermanshah city. This is the novelty of this research. In other words, at first the relation between distribution amount of water consumption and population distribution and after that, the relation between population distribution and the physical development of the city of Kermanshah are studied. That is according to physical development of the city, distribution amount of water consumption is determined. Data and At first the parameters related to the research were determined by using expert opinions and previous research. The Electre method was used to reduce indicator numbers. After that Monte Carlo simulation was used for obtaining weighted indexes. Finally, for estimation of distribution of population and water demand, FIS method was employed. Ten indices affecting the population and amount of water were chosen according to previous researches. These identified parameters included slope, distance from the river, elevation, distance from pipelines water, distance from roads (roads- Avenue), a distance of the center of town, population density, land use, distance from the river. Due to existence of numerous gathered parameters, some of them were filtered as inputs for FIS model. For doing this, Electre model was considered to select more efficient indices based on the 40 experts’ opinions. Each index was weighed according to Likert scale. The most effective layer was labeled with "very importance" and the least effective layer with "little importance". For determining the index weight, Monte Carlo simulation method was used. FIS model was also employed to determine density population of districts of the city. FIS model was employed in two states: First, state as combination model in which the consequences of employed Monte Carlo method for weighting of different layers were assessed. In the second state, only FIS method was employed, because of equality in regarding index weights. Conclusion and Elimination of slope aspect, distance from the river and elevation comes from Electre method. It was mentioned that the Monte Carlo method is based on Saati Judgment, assigning to the accuracy of calculation (A=0/01). The value of T and N were calculated: T=%5 and N=10000. The result from FIS model and FIS-Monte Carlo model were considered to determine the amount of water that is needed for districts 1 to 3. Both models determined district 3 as the district that needs the maximum amount of water while district 1 with the minimum amount of water. The evaluation of distribution of water demand for city is a very complicated issue that is influenced by a variety of parameters. These include the effect of population distribution such as natural factors, human infrastructure, policies in urban planning and urban development etc. Multi Criteria Decision Making (MCDM) method is commonly used to solve this problem. Because of high flexibility and power of their theory, fusion model are more efficient in counting complexity of problem (Shu, and Ouarda, 2008; Shahraki and Abbasian, 2014; Leyva and Fernandez, 2003; Asgharpour, 2012; Ghazanfari Rad, 2011; Rahmati et al., 2014; Sultan Panah and Farooq, 2000). So, these models are more efficient with respect to non-inclusive models. In this research two FIS and FIS-Monte Carlo methods were employed to estimate of population distribution and the amount of water demand with different results. According to the above explanation, the result of fusion model FIS-Monte Carlo is more reasonable. This reasonable result can be reported to the organizations that depend on water resources for future scheduling, management of water resources crisis.