فهرست مطالب پرهام پهلوانی

Landslides can occur due to human activities and environmental factors that have destructive effects. Reaching zoning of the risk of this phenomenon can help to make decisions to reduce losses and damages caused by this phenomenon. In this study, to prepare a landslide risk map, various natural factors including height, slope, slope direction, precipitation, soil type, distance from the waterway and distance from the fault have been used. Also, the historical information on the earthquake occurrence has been used to estimate the effect of each criterion and sub-criteria. For this purpose, by using the frequency ratio (FR) method, the impact of sub-criteria and by using Shannon's entropy method, the weight of each criterion has been calculated. Also, after calculating the weights of criteria and sub-criteria, VIKOR and weighted average (OWA) methods have been used to prepare a risk map. Based on the obtained results, the criterion of soil type with a weight value of 0.33 has the most effective weight. To compare different methods, the FR criterion has been used, which based on the results obtained, the OWA method has better results for α values ​​equal to 10 and 2.

Housing prices are one of the indicators that identify the factors affecting it and could help to increase the efficiency of plans and to present housing planning strategies and policies. Despite many exchange rate fluctuations in recent years, there is a need to create a model that pays attention to the economic factors affecting housing prices in addition to the ordinary housing features. Since the housing price modeling is one of the issues that has a spatial component, therefore, in presenting the model related to housing prices, its location should also be considered. Therefore, in this study, the analysis of the spatial distribution of housing prices in district 5 of Tehran municipality and the factors affecting that have been investigated. In this regard, housing sales data in this region in 2018, 2019, and 2020 have been used to model housing prices. The research results have been obtained by the Multiscale Geographically Weighted Regression (MGWR) method, which provided better results compared to those by both the Geographically Weighted Regression (GWR) and Ordinary Least Squares (OLS) methods. The adjusted coefficient of determination in OLS, GWR, and MGWR algorithms was obtained equal to 0.762, 0.821, and 0.853, respectively. The MGWR method is one of the methods that can model the spatial heterogeneity of housing price data. According to the results, the exchange rate variable (dollar price) has the greatest impact on housing price modeling.

Making a balance between the demand for healthcare services and the response to it requires the operation of new hospitals. But the important problem is finding the optimal for the construction of a hospital. The present research used a mixed method for determining the optimal location for a hospital in District 2 of Tehran. The mixed method is based on the Geospatial Information System (GIS), Best-Worst Method (BWM), and Multi-Criteria Decision-Making Methods, WASPAS, and TOPSIS. Fewer pairwise comparisons in the weighting process of the proposed method have increased the accuracy and reliability of decision-making results. The combination of spatial modeling and ranking has also reduced the search space for suitable places to build a hospital. For this purpose, after determining the appropriate criteria, the weighting process was carried out with BWM, and the spatial layer of each criterion was prepared using GIS. Based on the weighting results, the criteria of distance from existing hospitals and distance from healthcare centers have had the highest and lowest weight, respectively. In the next step, the land suitability map was prepared by combining the spatial layers. Almost 88% of the spatial modeling results corresponded with the realities of the region, and the western half, especially the southwestern part, for the construction of a new hospital, had a higher proportion than other parts. Finally, sites number 2, 5, and 8 among 11 candidate sites were determined as the most optimal places for the construction of a new hospital in the studied area. It is suggested that one or more hospitals should be built in the designated optimal sites in accordance with the population living in areas without optimal access to hospital facilities so that in addition to improving health spatial equity, the cost of citizens’ access to hospitals could be reduced.

Determining the optimal location for the construction of the hospital will increase citizens' satisfaction with health services and increase their quality of life. The aim of the present study is to determine the optimal location for the construction of a hospital in the second district of Tehran’s metropolis. For this purpose, an integrated methodology including GIS, SWARA weighting method, and COPRAS ranking method has been presented. SWARA and COPRAS multi-criteria decision-making methods are among the best methods and have received less attention in the field of determining the optimal location of the hospital in combination with the GIS. The results of the weighting method showed that the criteria of distance to existing hospitals and the geospatial distribution of PM2.5 particles are the most and least important criteria in the decision-making process of the optimal location of the hospital, respectively. The results of geospatial modeling showed that the southwestern parts of the district have a very high potential for hospital construction and almost 48% of the region has the potential to build a hospital. In other words, there is no spatial justice for the citizens of half of the region in using the medical services of the hospitals. The results of ranking candidate sites showed that sites 5 and 6 were the most suitable places to build a new hospital in the second district. According to the research results, the weighting method used has the same accuracy as the fashionable weighting method of the analytic hierarchical process, despite the executive and computational complexity.

The flood is the most important natural disaster that causes human and financial losses every year, then its management is very important. The most basic step in flood disaster management is the preparation of a flood susceptibility map, which integrating the geospatial information system and multi-criteria decision making is an efficient approach to handle it. In order to spatially model flood susceptibility, the present study has presented an approach of fusion the weights of the effective criteria on flood susceptibility using the Dempster-Shafer information fusion theory (DST). The purpose of the fusion of the weights of the criteria is to increase reliability, reduce the uncertainty of the weighting process, and increase the accuracy of flood susceptibility modeling. The present research has presented a hybrid weighting method by integrating the results of two weighting methods (Analytical Hierarchy Process (AHP) and Best-Worst Method (BWM)). The hybrid weighting methods of previous researches are mainly based on simple mathematical operators, and complex operators such as DST used in the present research are less used. The results of the fusion of the weights obtained from the two weighting methods of AHP and BWM indicate the very high weight of the flow accumulation criterion (0.437) and the very low weight of the vegetation index criterion (0.004). Comparing the results of the research with the facts of the studied area showed that the presented hybrid weighting approach with 96% accuracy Compared to each of the basic weighting methods used, is of higher performance. Also, the susceptibility modeling resulting from the new BWM weighting method has been more accurate compared to the common method of the AHP. According to the results of the research, more than 92% of the studied area has moderate to high flood susceptibility and less than 8% of the area has less than moderate susceptibility, which indicates that the studied area is prone to flooding.

The purpose of this research is to select temporary accommodation centers after the earthquake crisis to meet the needs of the victims. Therefore, this research has tried to identify temporary accommodation sites in District 22 of Tehran with the help of effective criteria. The required data has been obtained from the Tehran Disaster Mitigation and Management Organization and the results of the general population and housing census of 2017. The novelty of this research is to present a new combination approach to determine the effective criteria for locating temporary shelter sites. In this regard, the combination of geographically weighted regression (Gaussian and tri-cube kernels) with a binary particle swarm optimization algorithm was used. The recommended combination method is suitable for spatial regression problems because it is compatible with two unique properties of spatial data, i.e. spatial autocorrelation and spatial non-stationarity. The best value of the fitness function (1-R2) for Gaussian and tri-cube kernels was obtained at 0.04616 and 0.0097, respectively, which indicates the high compatibility of the tri-cube kernel with effective criteria. According to the obtained maps, Chitgar Park and Azadi Sports Complex are some of the widest and most suitable areas for the construction of temporary shelter sites after the earthquake crisis in the case study. By identifying temporary shelters, relevant organizations can provide the appropriate infrastructure for these selected centers so that there is no need to spend time to provide these services in the event of an earthquake.

The topic of placement of infill drill holes and providing an optimal pattern for drilling drill hole has long been of interest in the mining process and has become an inseparable part of it. The root of this importance can be found in the financial issues related to the mining process as well as the exploration and extraction sector and issues related to classification. In order to reach an acceptable level of uncertainty, the estimation error method was used in this research to classify the blocks. Also, in order to place supplementary drill holes and weight potential drill holes, CRITIC and TOPSIS methods were used. The CRITIC method was used to assign weight and the TOPSIS method was used to select 5 of the best drill holes. At first, the drill holes were weighted by CRITIC method and with expert opinion, and then they were graded by TOPSIS method. The criteria used for the TOPSIS method include the grade of iron, weight percent grade of remaining iron (geometallurgical component), variance, error of estimation and classification. Finally, after placing the 5 wells in question, the estimation and classification process was carried out again, based on the number of definitively classified blocks, it was increased from 380 to 571, and the estimated average grade of iron also increased and 19% improvement in the classification was obtained. The improvement achieved in the number of blocks with definite classification adds to the certainty and reliability of the model.

PM2.5 pollution is one of the critical environmental problems that occurs after industrialization and the increase in the population of cities. Information about pollutant concentration, including PM2.5 pollutants, significantly impacts how city managers make decisions to improve cities' health. This study used the ordered weighted averaging (OWA) technique to produce PM2.5 pollutant zonation. For this purpose, meteorological information layers include wind speed, maximum temperature, minimum temperature, average temperature, 24-hour precipitation and humidity, normalized vegetation difference index layers (NDVI), and road density are used. The gradient descent algorithm has been used to calculate the weights related to the order of the values to apply the OWA algorithm. The optimal learning rate parameter has been obtained to achieve the optimal value of the weights. Also, the layers of information were combined based on the obtained weights from OWA. Finally, the RMSE index was used to evaluate the obtained results, and the PM2.5 pollutant estimated for the summer and winter seasons had the lowest and highest errors, respectively. The error values for these two seasons were 0.129 and 0.190, respectively. Also, Aqdasiyeh station had the lowest error in all seasons, and Golberg, Region 11, and Shahr-e-ray stations had the highest error.

In this study, the spatial-temporal distribution analysis of housing prices in District 5 of Tehran Municipality and the factors effective on it were investigated. To this end, the data related to housing buying and selling in this district in the years 2018, 2019, and 2020 were used to model the housing price. The results were obtained using GTWR method, which gave in a better measure compared to GWR and OLS methods. The adjusted coefficient of determination in OLS, GWR, and GTWR algorithms were found to be 0.759, 0.798, and 0.835, respectively. GTWR is a method that can model the spatial-temporal heterogeneities that exist in the housing price data. Based on the obtained results, the currency exchange rate (dollar to rial) has the highest effect on modeling housing prices. After that, the physical characteristics of housing – such as its footage and age – are important in modeling housing prices. Finally, the access rate to urban services – such as distance to hospitals, sports centers, educational centers, religious sites, green space, highways, and urban public transportation stations – can improve the modeling of housing prices. The findings of this study show that using dollar-to-rial exchange rate as the independent variable, we can model the housing price with a proper precision.

Hospitals are among the most important service centers, and the selection of the optimal site for them is a very important (yet complex) undertaking, as it can bring about optimal spatial distribution of hospitals and can make them optimally accessible for citizens. In the present study, in order to optimally locate hospitals in District 5 of Tehran metropolis, a combination of the geographical information system, objective weighting methods, and multi-criteria decision making method was used. The geographical information system was used to analyze and manage the optimal hospital locating criteria, the CRITIC weighting method was implemented to account for the correlation between the criteria, and Shannon's entropy method was used to model the existing uncertainty in the criteria. CODAS multi-criteria decision making method was used due to its novelty and the evaluation of alternatives based on two criteria. Based on the results obtained from CRITIC weighting method, distance from health centers, and based on the results of Shannon's entropy method, distance from industrial areas were the most important optimal hospital locating criteria. The results of ranking the candidate sites using CRITIC-CODAS and Shannon’s entropy-CODAS were almost the same, and both methods identified the sites on the western side of the District (that did not have any hospital) as the appropriate sites. The results of the study indicated the high accuracy of combined objective weighting and multi-criteria decision making methods in optimal locating of the hospitals. It might be asserted that these methods can replace thematic weighting methods such as analytical hierarchy process.

Gilan-e Gharb region has been located in the west of Iran and it has special features in terms of geology. Remote sensing data (satellite images), geology, tectonics and mineral data sets of the region were used to prepare an innovative integrated method for the exploration of Gilsonite mineral. Effective criteria and sub-criteria in the exploration of this mineral were identified and evaluated according to the available exploration data. Afterwards, these criteria were weighted using Fuzzy Analytical Hierarchical Process (FAHP). Accordingly, the ideal positive and negative solutions were ranked using a Fuzzy TOPSIS method to prepare a mineral potential mapping (MPM) of Gilsonite in Gilan-e Gharb region. The product of this paper is a potential map used for optimal identification in the early stages of exploration of Gilsonite mineralization that reduces the time, cost, and risk of exploration. Finally, the field’s visit and investigation has been accomplished for the regions with a high potential of Gilsonite mineralization to evaluate the proposed method. The results showed that 82% of the identified points had proper adjustment to the MPM. This high adaptation showed that the proposed method has good performance in Gilsonite exploration.

Due to the increase in the occurrence of floods, especially in the cities, and the emergence of human, financial, and environmental risks due to its increase, the flood zoning areas are of great importance. Therefore, in this study, it was tried zoning the areas of floods with the help of determining effective criteria. The criteria used in this research include Modified Fournier Index, Topographic Position Index, Curve Number, Flow Accumulation, Slope, Digital elevation model, Topographic Wetness Index, Vertical Overland Flow Distance, Horizontal Overland Flow Distance, and Normalized difference vegetation index. The novelty of this study is to present a new combination approach to determine the effective criteria in flood hazard zoning (Maneh and Samalqan County). In this regard, the combination of geographically weighted regression (Gaussian and tri-cube kernels) and binary particle swarm optimization algorithm was used. The recommended combination method is suitable for spatial regression problems because it is compatible with two unique properties of spatial data, i.e. spatial autocorrelation and spatial non-stationarity. The best value of the fitness function (1-R2) for Gaussian and tri-cube kernels were obtained 0.0745 and 0.0022, respectively, which indicates higher compatibility of the tri-cube kernel than the Gaussian kernel. It was also found that the criteria used have a significant effect on the rate of flooding in the study area.

Healthcare is one of the most important urban services and proper access to healthcare centers is critical. Determining the hospital’s prospect location is a difficult problem of multi-criteria decision-making (MCDM) and the use of Geographic Information Systems (GIS) improves this decision. In this study, integration of GIS and MCDM was used in order to determine the prospective location of the hospital in the fifth district of Tehran. For this purpose, firstly, the effective criteria in the process of determining the prospect location of the hospital was selected by reviewing the literature and expert's opinions, and the spatial layer of each criterion was prepared. Then, ten sites in this district were selected and the criteria values were calculated for each. Then, the criteria weights were determined by the Criteria Importance Through Inter-criteria Correlation (CRITIC) weighting method. Weighting results showed that the seismic vulnerability was the most effective criterion in the decision-making process. In the next step, the sites were evaluated and ranked by two MCDM methods, that is, the Combinative Distance-based Assessment (CODAS) and the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), and the results showed the similarity of the performance of the two methods. The ranking results of the two methods showed that sites No. 7 and 1 were the most suitable and the most unsuitable place to build a new hospital, respectively. In order to evaluate the accuracy of the ranking, the ranking results of the two methods were compared with the ranking performed by experts. Evaluation of the accuracy of the methods showed that the CODAS method for selecting the prospect location of the hospital has higher accuracy than the VIKOR method.

Recently, National Cartographic Center, the Organizationfor Registrationof Deeds and Properties, and alsoon a limited scale some municipalities have developed systems to provide real-time differential positioning services. Although these systems have proved to be efficient for quick mapping purposes in this country, they do not provide accurate differential positioning in coastal and offshore areas and thus cannot meet the needs of navigation and exploration and extraction of marine resources in oil fields. However, Iran has long maritime boundary in its south and north, and maritime economy is considered to be a priorityin its development planning. Since site selection for permanent positioning stationsis considered to be the main step of creating a real-time differential positioning system, finding the most suitable location for permanent positioning stations in the south of the country was selected as the purpose of the present study. To reach this aim, pairwise comparison matrix of the required information layers was first constructed using Delphi methodbased on the opinion of 5 experts, and in the next step, computer coding was performedin MATLAB using Fuzzy Analytic Hierarchy Process to compute the weight of each layer and sublayer.Then, layers were classified in GIS environment based on the weights obtained from the analysis of pairwise comparison matrices for each sublayer. Finally, layers were integrated usingweighted index overlay analysis methodto select optimal sites for permanent stations based on the weights obtained for each layer. Details of the calculations and the results are presented in the article.  

The present study addresses the issue of site selection for permanent GNSS stations. In the first step,pairwise comparison matrix was created for the criteria and sub-criteria and filled in by 5 experts. Then, layers were classified in GIS environment based on the weights obtained for each sub-layers of pairwise comparison matricesand the codes written in MATLAB. Finally, suitable locations for permanent GNSS stations were obtainedby integrating the layers usingweighted index overlay.   

The present study has provided the results of optimal site selection for GNSS permanent stations. These selected sites meet the needsofprecise positioning in the coastal areas of the country and can be used in navigation and exploration and extraction of marine resources and oil fields. Afterthe selection of southern coasts as the study area, 7 criteria (proximity to urban areas and facilities, slope, distance from faults, distance from access roads, soil type, distance from rivers and distance from railways) were selected based on the expert opinion. A pairwise comparison matrix was createdfor these criteria and sub-criteria and 5 expert experts were consulted in this regard. Expert opinions were analyzed using codes written in MATLAB software andFuzzy Analytic Hierarchy Process method and thus, the weight of each criterion and sub-criterion was obtained. These weights were then integrated using the geometric mean method and the final weight of each layer and sublayer was determined. Using Arc map software, these weights were applied to different layers and sublayers, and finally, optimal locations for permanent GNSS stations were divided into 5 classesof very good, good, medium, bad, and very bad stations. Good and very good classes can be considered as optimal places forcontinuously operating reference stations.

Hospitals are among the most essential human services whose role in the health of the citizens is very important. One of the important issues related to hospitals in developed cities is determining the optimal location for their construction. In this study, in order to select the optimal hospital location, a combination of expert opinions, GIS, and MCDM was used. For this purpose, 12 criteria were selected for the spatial modeling of the potential areas of hospital establishment, and then the weight of each criterion was calculated using the weighting method of Analytic Network Process (ANP). Then, a map of the potential areas for the hospital establishment was prepared and 11 sites in the potential areas were selected. In the next step, the weight of each criterion was determined again using Criteria Importance Through Intercriteria Correlation (CRITIC) method. Then, by the integration of two weighting methods, i.e., ANP and CRITIC, with two MCDM methods, i.e., EDAS and VIKOR, these sites were ranked. The results of spatial modeling showed that the western half of the district is more suitable for the construction of a new hospital than the other areas. According to the results of the ANP, the distance from hospitals, and according to the results of the CRITIC, the seismic vulnerability were the most important criterions in determining the optimal hospital location. Based on the ranking results coming from CRITIC-EDAS, CRITIC-VIKOR, and ANP-EDAS, the site No. 1, and according to the ranking results of the ANP-VIKOR, the site No. 8 have been the most suitable places for the construction of a new hospital. In the light of the results of the site evaluations by experts, CRITIC-EDAS and CRITIC-VIKOR methods are more accurate for hospital location selection.

In this study, Shannon's entropy and the TOPSIS techniques have been used for ranking residential areas against the earthquake hazards. For this purpose, 8 variables were examined in 27 districts of Amol city. These variables include released energy, earthquakes of the last 20 years in terms of ton TNT, buildings quality, residential density, building density, population density, network permeability, urban open space, and groundwater depth after construction. Information was ranked based on the proximity to the ideal vulnerability ideal using the Shannon entropy and the TOPSIS multi-character decision model. The rankings obtained from this analysis were added to the city map and the areas of the city were divided into five categories with vulnerability, including ‘too much’, ‘much’, ‘medium’, ‘little’, and ‘too little’ and finally the map of the extent of Amol's vulnerability was prepared. The results showed that the central areas, i.e. 24, 13, 18, 10, and 12, are very vulnerable. Moreover, areas 19, 17, 14, and 16 are highly vulnerable, areas 15, 21, 2, 6, 22, 25, 11, 20, 27, and 4 have moderate vulnerability, areas 26, 9, 23, 3, 1, and 5 have low vulnerability, and finally, areas 8 and 7 have very vulnerable to earthquakes, respectively. Therefore, it is expected that the vulnerability of urban areas will be considered in future constructions.

In the present study, firstly, the effective factors of Golestan national park forests fire have been identified. Then, by using these factors, forest fire development modeling is performed based on cellular automata. Multiple linear regression and genetic algorithm are used to determine the effective factors on Golestan national park forests fire. In order to investigate the effect of spatial resolution of the maps are used on the results of modeling, effective factors have been generated in different spatial resolution and these data are used as the input of the proposed algorithm. Also, the neighboring filters 3×3, 5×5, and 7×7 are used to investigate the effect of the neighboring filter in the forest fire development process. Cellular automata is used for modeling Golestan national park forests fire development, and the artificial bee colony is proposed to calibrate it. The results of this study show that using the proposed algorithm with 3×3 neighboring filter is more accurate than the other neighboring filters. In the best case, the Kappa index, the overall accuracy, and the relative operating characteristic are 0.924, 0.960, and 0.494, respectively that these results are for spatial resolution of 30 meters on November 17, 2010.

In spite of the occurrence many evidences that indicate several rich regions of the geothermal resource in Iran, very few exploration studies have been carried out so far specially in Zanjan province that have a lot of hot springs and other relevant evidences. Geothermal heat flux is one of the main parameters to be investigated in geothermal exploration programs but few direct heat flux measurements are available in Iran. We can get reliable and better result by using all exploration criteria related to the geothermal resources such as lithological, remote sensing, structural and geophysical information layers, if we use a suitable method to overlaying and fusion these layers. In this study we firstly use index overlay and fuzzy methods for overlaying these data and the DS-fuzzy AHP technique is then used for data fusion and prioritizing of the results.

 Methodology and Approaches:

 In the first step, relevant digital information layers were prepared. They comprised of lithology layer, alteration layer obtained from ASTER data, structural layer acquired from lithology, ASTER and airborne magnetic data. Then, Curie depth, thermal gradient and thermal flow maps were obtained using a set of high resolution airborne magnetic data. All of these layers were then integrated by fuzzy logic and index overlay methods to determine the promise regions of geothermal resources. Finally, the favorable geothermal areas, obtained in the previous stage, were prioritized using DS-FAHP fusion method. 

Results and Conclusions

By examining and evaluating the results and considering the existing evidences such as hot water springs, it is found that the data fusion DS-fuzzy AHP methods can provide an acceptable and reliable results. Moreover, the prioritization of the results using these data fusion methods indicates that high potential geothermal regions are related to all the evidences of geothermal resources. The obtained results also show that by applying fuzzy AHP for weighting exploration layers, one can consider uncertainty in decision making, and then, by using DS method for data fusion more reliable results can be acquired.

Nowadays, Determining the effective factors on fire is so important, because the plenty areas of forests around the world are destroyed annually by fire and recurrence of that in the long term can irreparably damage to the earth and its inhabitants. It helps us to identify most dangerous locations and times in forest fire. Hence, we can prevent many of driving factors of forest fire by law enforcement, efficient forest management policies and more supervision. In the current study, we identified the effective factors on the fire in Golestan forest through integration of three different methods including multiple linear regression, logistic regression and multivariate adaptive regression spline with Genetic Algorithm.

Golestan Province is in the North of Iran and 18% of it is covered by forests. Golestan Province is a touristic province and several roads pass through its forests and according to statistical records, most of the occurred fires were in proximity of these roads. Our study area is located in 36°53′-37°25′N and 55°5′- 55°50′E and its area is about 3719.5 km2. We selected this area, because includes the most of fires have been occurred in Golestan Province in recent years.

A big fire was occurred on 12 December, 2010 in our study area and we used it as the dependent variable. The actual burnt area and some other data, such as Digital Elevation Model (DEM), the roads network, the rivers, the land uses, and soil types in the area were provided from Golestan Province Department of Natural Resources. Also, geographic coordination of the synoptic weather stations near the area and their data, including maximum, minimum, and mean temperature; total rainfall, as well as maximum wind speed and azimuth in December 2010 were obtained from National Meteorological Organization of Iran.
The land use and soil layers were in scale of 1:100000 and the roads and the rivers layers were in 1:5000 and all of them were provided in 2006. The region DEM is generated from topographic maps of Iran National Cartographic Center in scale of 1:25000 with positional resolution of 30m and we produced the slope and the aspect layers from it in ArcGIS software with the same resolution. The roads and the rivers were in vector format, hence, we used the Euclidean Distance analysis to generate rasters that each cell of them shows the distance from the nearest road or river.
At first we had 5 weather stations, which is very few for GWR. In this regard, we generated 1000 random points in the area and interpolated data to these points using Ordinary Kriging method with exponential semivariogram model in 30m resolution in ArcGIS software.
The multiple linear regression (MLR) model is the generalization of simple linear regression that is modeling the linear relation between one dependent variable and some independent variables. The general formula of MLR is seen below: (1)The unknown coefficients are obtained using least squares adjustment as follows:  (2)The logistic regression (LR) model is a nonlinear model for determination of the relation between a binary dependent variable and some independent variables. If we use the values of 0 and 1 for non-fire and fire points respectively, then the probability that a point be a fire point is obtained by Eq. (3):   (3)If the number of parameters is insignificant compared to the observations, then we use the unconditional maximum likelihood estimation shown by Eq. (4) to compute the unknown coefficients of this model. (4)The multivariate adaptive regression spline (MARS) model is a flexible non-parametric model that requires no assumption about the relation between the dependent andindependent variables. Hence it has a high ability in determination of complex nonlinear relations among the variables. The general formula of MARS is seen below:   (5) is the m’th basic function that is obtained by Eq. (6):  (6)These basic functions are chosen in such a way that leads to minimum RMSE of model.
We use the genetic algorithem (GA) with the fitness function of the normalized RMSE to select the optimum combination of effective factors on forest fire.

In this paper we study the dependence of the forest fire to 14 factors shown in table 1, in the study area. Our results are shown in figures 1 to 3.
 

This research shows that both of the biophysical and anthropogenic factors have significant effects on forest fire in our study area. Just two factors were identified as impressive factors in all three cases including the minimum temperature and the maximum speed of wind. This study concluded to the NRMSE=0.4291 and R2=0.9862 for the multiple linear regression, NRMSE=0.9416 and R2=0.9912 for the logistic regression and NRMSE=0.1757 and R2=0.9886 for the multivariate adaptive regression spline and totally the multivariate adaptive regression spline method showed a better performance in comparison to the other two methods.

Determination of the rate of penetration is one of the most important factors in oil industries. Generally, two methods have been proposed for modeling the rate of penetration which include physical and artificial neural networks methods. Artificial neural networks can be used accurately in order to predict the rate of penetration in which the prediction of the rate of penetration does not include experimental coefficients and bit specifications. Furthermore, in this method, the rate of penetration only depends on the input data. In this paper, the rate of penetration using almost 2000 daily drilling reports and rock mechanical properties was applied. These data were reduced to approximately 1800 data according to data preprocessing. The rate of penetration was modeled by two artificial neural networks including a Multi-Layer Perceptron and an Elman with a hidden layer. After preprocessing the input data and sensitivity analysis of the number of neurons in the hidden layer, seven neurons were chosen as the optimized number of neurons in the Multi-Layer Perceptron with the correlation and mean square error of 77.1% and 1.31, respectively. Also, the Elman neural network showed the correlation and mean square error of 77.6% and 1.33, respectively. Thereafter, the fuzzy AHP method was applied for imposing weights, gaining by expert comments, on the input data resulted in improvements of the artificial neural networks. The results of this investigation have shown insignificant superiority of the Multi-Layer Perceptron neural network for prediction of the rate of penetration comparing to the Elman neural networks. Therefore, the proposed weighted Multi-Layer Perceptron neural network models the rate of penetration accurately and appropriately using available data.

Forest areas are among the most important natural and ecological resources on the Earth and are considered as one of the main pillars of sustainable development in any country. Fires ruins almost 5500 hectares of Iran’s forests yearly. In this research, firstly, the fire points were identified using the fire data of Forest Organization in combination with MODIS sensor data between 2012 and 2017. Due to the fact that more than 75% of fires were happened in the hot season of the year (June, July, and August), the data of the three months was used for modeling. Then, the effective parameters in fire occurring were evaluated and the dependent parameters were removed. Accordingly, two methods, including multiple linear regression and multivariate adaptive regression spline were studied to predict the fire risk. Some important parameters including the root-mean-square error (RMSE), R2, the correct estimation percentage of fire and non-fire points, and error distribution were used to evaluate. After modeling, it was found that the multivariate adaptive regression spline has better performance—where its RMSE of test data was 0.1628, its R2 of test data was 0.893, and its correct estimation percentage of test fire points and test non-fire points was near 94% and 88% respectively, as well as its error distribution was better than the other method. This actually shows that modeling with a local method is very better than modeling with a global method. Therefore, the risk map resulted by multivariate adaptive regression spline has better reliability compared to those of the other method. Finally, the high-risk areas were recognized using the risk map of this method. The traits of these areas were a short distance to residential areas and roads, having rich soil with organic materials, relatively high temperature, and low height.
 

In 2000, a convention was established in the United Nations to improve the quality of human life in which the principles of the Millennium Development Goals were adopted. One of these goals was to ensure the stability of the environment and natural resources. In the contemporary world, the value of forests is about 120 billion dollars and the livelihood of almost 9.1 people is dependent on forest (in)directly.
According to the opinion of global experts including FAO, if the forest cover of a country is less than 25% of that country’s area, that country is in critical condition in terms of the human environment. Almost 190000 hectares of Iranian forests have been ruined by fire in a 28-year period. Forest fire not only changes the natural ecosystem and ruins many plant and animal species of a region, but also makes other destructive effects like air pollution, respiratory problems, soil erosion, increased flowing surface waters, increased acidity of soil, decreased fertility, tourism industry losses, manufacturing industry and economy losses, and even climate change.
Immediate and accurate detection of the fire location and the ability to determine the effective parameters on it, as well as the detection of the areas with high-risk of fire is among the main concerns of environmental protection and disaster management. We can prevent the fire by training people, making effective regulations and management policies, and increased monitoring to deal with fire triggers. Moreover, in the case of fire occurrence, we must take necessary actions like deploying fire-fighting equipment near hazardous areas and making easy access to these areas. In fact, nowadays, the increasing importance of protecting the forests and natural resources has led to change the focus from crisis management to risk management.

The modeling was not possible without non-fire points. Accordingly, at the beginning, some points are randomly selected in the whole area with a certain distance from the fire points and are identified as non-fire points. To implement the methods in MATLAB programming environment, firstly, the parameters used in the modeling are extracted using the maps of these parameters for fire and non-fire points. These parameters are used as inputs in each of these methods. 
Constantly, 70% of the selected data were used as the training data and 30% of them were used as the test data. Initially, the multivariate linear regression and then the multivariate adaptive regression spline were used for modeling. The steps of the research implementation are shown in Figure (1).
After implementation of the modeling, the evaluation parameters of each method were provided to compare. Then, the risk map of the area was provided using trial points and Inverse Distance Weighting (IDW) and by employing 12 lateral points for each method (Figures 2 and 3). The points with a high risk were extracted from the resulted map. Then, the main traits of these points are considered as the traits of high-risk points.
 
Fig. 1. The steps of the research implementation

Fig. 2. Fire risk map provided using the MLR method on test data

Fig. 3. Fire risk map provided using the MARS method on test data

 
After removing the dependent parameters from the effective parameters on the fire, the optimal effective parameters are presented in Table (1). These parameters are divided into three groups including climate, ground physical, and human parameters.
The modeling of fire risk was done by two methods. In the training and testing data section, the RMSE and R2 are presented in Table (2) for multivariate adaptive regression spline and multivariate linear regression methods, respectively. The results achieved by the training data section indicate that the training procedure is more accurate (R2 closer to 1) and with less error (less RMSE) in the multivariate adaptive regression spline than those achieved by the multivariate linear regression method. The appropriate amount of evaluation parameters for test data shows that the model does not experience over-fitting in these methods.
Table 1. Effective parameters on fire occurrence in the case-study area

In the linear regression method, the two parameters of the correct estimation percentage of fire points and non-fire points have a low value, hence, the worst possible scenario has happened and the risk map has the least amount of reliability. In the multivariate adaptive regression spline, the fire and non-fire points are simultaneously estimated with a high accuracy. This makes the risk map provided by the multivariate adaptive regression method becomes to be more reliable.
As seen in the results, the risk map provided by the multivariate adaptive regression spline method has a very higher reliability compared to the risk map provided by multivariate linear regression method. Hence, the risk map resulted by the first method was used to determine the features of the areas with a high risk of fire (Figure 4).
Since the fire risk has a normal distribution, the areas which satisfy Equation (1) are among the 2.5% of the areas that have the most fire risk.
(1)
where  is the average,  is the standard deviation, and R is the fire risk. The main features of the mentioned areas can be used as the important tools for decision making. The extraction of high-risk areas is done in ArcGIS environment. Statistical analysis of effective parameters’ features in these areas shows some key points. These features include low distance from the residential regions (less than 2 km), low distance from the road (less than 2 km), having mollisol, relatively high average temperature (more than , and low height (less than 50 m).

Fig. 4. High risk map provided using the MARS method on test data

This research attempted to identify the optimal method for modeling of fire points risk using climate, ground physical, and human parameters. Therefore, an accurate local method (MARS) was used along with a non-local method (MLR).
In the test data and the training data sections, the MARS method had the lowest RMSE and a value closer to 1. The outputs showed that the MARS method had a more accurate performance in the estimation of the fire and non-fire points compared to the MLR method. This indicated the high reliability of the MARS method. After determining the optimal method for the modeling of the area’s fire occurrence, the points of the area with high risk of fire were detected. After doing a statistical analysis it was found that these points have some fundamental features including low distance from the residential regions (less than 2 km), low distance from the road (less than 2 km), having mollisol, relatively high average temperature (more than  and low height (less than 50 m).

In order to increase the quality of industrial wastewater treatment and better manage of them, their approach should be simple and accurate for estimating process. Treatment processes for black box systems are due to the influence of many factors that involved in the system. Because of problems in using physical models, the use of statistics and regression methods could be helpful. Therefore, whatever model is simpler and less input variables so the model will be more important. Influent of the proposed model includes output data of biological unit and effluent is chemical oxygen demand of the clarifier. To compare the models performance three indicators of R-square, Correlation Coefficient(R) and Mean Square Error (MSE) are used. The aim of this study is creating linear data mining model and comparing them with similar methods for quality data. Finally, a linear robust regression with MSE = 0.089054, R = 0.784727 and R-Square = 0.6096 is proposed.

Recently, utilizing the traffic simulation models have been known as an efficient traffic control and management tool. However, all traffic micro-simulation models require a car-following model so that the simulations would be performed based on it. Many car-following methods have been proposed yet, but all these methods have several number of parameters to calibrate, in a way a little variation over them creates considerable disturbance. In this paper a new car following model was proposed using the Multi-variate Adaptive Regression Spline (MARS) method that do not need any calibration stage. The proposed model inputs are leading vehicle velocity, relative velocity of leading and following vehicles and spacing between them and the output is the acceleration of the following car. As a result of this paper, a car following model with the Mean Squared Error (MSE) equal to 0.004 and the correlation coefficient (R) equal to 0.98 was achieved using the function estimation method through the MARS method. Finally, the MARS output was compared with the results achieved by the Helly linear model, the GHR model and the Gipps model. Based on micro simulation experiment it was shown that the proposed method was more accurate than those models.

Nowadays, integrating different kinds of data and images, achieved by the different remote sensing sensors are known as a suitable solution for extracting more useful information. This because of large range of aqucition images, digital format, and high temporal resoulation enable scinentits to extract information form land surface. The passive optical sensors have been used extensively in mapping horizontal structures. However, radar data could be used as a complementary data, since these data would be gathered in different climatic conditions in 24 hours of a day, as well as some geo and manmade structures have a specific response in the radar frequency. Furthermore, LiDAR data could gather precise measurements from vertical structures. On the other hand incorporating these variety of information and data for extracting specific urban features is curucial and challenging task. Hence, by integrating optical, radar, and LiDAR data more features and information would be prepared for different kinds of applications. For example some object may easily find in optic imagery but it is difficult to extract that object form LiDAR or RADAR images. Therefore utilizing procdure for fusing and extracting these object is inevitable. In this research, we used these datasets to detect buildings, roads, and trees in a complex city sense, i.e., San Francisco, by generating 57 features, and also by using the PCA and ICA feature extraction methods, as well as the well-known intrinsic dimension methods, including SML and NWHFC. Finally, the K-NN classifier was utilized in order to detect buildings, roads, and trees and grouping features according to the earned accuracies. Results show the high performance of the proposed approach and support our analyses.

Due to increasing of population and its impacts on urban development and to avoid creating problems, studying and predicting of urban expansion and its impacts is one of the most important issues in scientific societies and environmental academies. For a better planning for the future, a vision of the future would be needed. Prediction of urban expansion gives a good view about the future to experts and decision makers. In this regard, they can make a well plan to meet the needs of the city in the future. Tehran is the capital and one of the most growing cities of Iran. So, the major purpose of this paper is to examine and display the growth of urbanization in Tehran in recent years and forecasting it for future. Thus, the combined results of the optimized feed-forward artificial neural network with different neighboring filters were used to model this issue. Moreover, for the urban growth purpose, only the building land use class was considered as the urban land use. The Landsat images at 1994, 2004, and 2014 were used to produce the urban map with the SVM method in ENVI. To access the trend of changes, the images for the years 1994 and 2004 were used for temporal mapping. In this paper, twelve parameters were considered as the effective parameters in urban expansion for Tehran, including digital elevation model, slope, population density, distance from Building blocks, distance from a new buildings created from 1994 to 2004, distance from villages, distance from roads, distance from farm lands, distance from jungle and parks, number of buildings pixel in 3×3 neighborhood, non-proliferation areas, and existing buildings. These factors were extracted from various existing maps and remotely sensed data using the ArcGIS and ENVI software. The feed-forward artificial neural network was performed with these parameters in two phases: (i) for training the network and (ii) for prediction. In order to predict the suitability map with high precision, the feed-forward artificial neural network architecture was optimized according to the least RMSE value for each number of hidden layer neurons. Then, the predicted suitability map was combined with different neighborhood filters with different thresholds in order to produce the urban map of 2014. Then, a combined method concluded from the results of the different neighborhood filters was presented. For accuracy assessment, the output of the model was validated in two phases: (i) 92.46 % of accuracy of the suitability map was obtained using Relative Operating Characteristic (ROC), and (ii) the final urban map of 2014 resulted from the combined method was compared with the real map of 2014 with the kappa index of agreement, i.e., 82.31 %, and the overall accuracy, i.e., 92.22 %. Finally, the proposed combined method was used to predict the urban map for the year 2024. The results indicate that an uncontrolled growth is going to happen in West and Southwest of Tehran. Accordingly, the results of this paper warned the city managers and experts for better planning and having a good strategy in critical situations.