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\setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Alireza Arabameri} \affil[1]{ Islamic Azad University} \renewcommand\Authands{ and } \date{\small \em Received: 13 December 2013 Accepted: 4 January 2014 Published: 15 January 2014} \maketitle \begin{abstract} In recent years, coincide with population growth and industrial expansion, in many countries in the world,Extract water of underground sources expanded and annual withdrawal of ground water is higher than the annual feeding. This means extracting and using the water in layers that has been saved over thousands of years in the underground. Consequently groundwater levels in the area will be extracted every day and eventually drop where the water will not exist. While proper management and control of these resources will eliminate the problems of drop in water level. One way to managing groundwater resources is artificial recharge of groundwater and determine suitable locations for these purpose. growth and development trend of Mashhad city and excessive Extracting of ground water in recent years, has been essential groundwater resources management strategy in the region more than ever implied. The purpose of this study is Zoning Mashhad watershed for artificial recharge of underground aquifers using TOPSIS Model and GIS technique. TOPSIS algorithm is a Multi Criteria Decision Making, a type of compensatory model and an adaptable subgroup with strong ability to solve multi alternative problems because of having ability to overlap indicators in weak and power points . In this model, if quantitative criteria can change in to qualitative criteria, qualitative criteria can be used besides quantitative criteria. In aforementioned model, it is supposed that each indicator and criterion has steady increasing and decreasing utility in decision making matrix; it means if criteria gain more positive amount, they will be more appropriate, on the contrary the more negative amount, the less appropriate. The result and findings of different studies show that in TOPSIS method, zone 3 with (0/669) point promotes in first rank among 5 studied zones and thus it is the most appropriate zone to establish the proper area for artificial recharge of underground aquifers , in contrast zone 1 \end{abstract} \keywords{groundwater, aquifer, algorithm TOPSIS, artificial recharge, zoning, Watershed Mashhad.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{I. Introduction}]{I. Introduction}\par owadays, shortage and decrease in fresh water is approximately under increased all over the world. Based on the statistics published by FAO (Food and Agriculture organization), need for fresh water has almost become double per 21 years, while useful water resources have been reduced by half in relation to 30 years ago. It seems that useful water resources will become one fourth up to 2025 than useful water resources in 1960. Meanwhile, danger of various pollutions for water resources frequently increased the value and importance of them. Due to mentioned cases, if water resources aren't managed in better way, the life of human being will be threatened by the shortage of water. Thus, it is necessary to acquire the exact and up to date information about the condition of water resources and prediction of their situation in future in order to achieve optimum management for water resources.since, the group decision making, evaluation is resulted from different evaluator's view of linguistic variables, its evaluation must be conducted in an uncertain, fuzzy environment . In recent years, water exploitation has become greater for many reasons such as population growth, industrial development, urbanization growth and consequently increased demand for food products. Hence the rate of exploitation and consumption ground water become greater than recharge of them, in other words input of ground water system is less than its output and system with negative balance sheet has positive feedback and it is collapsing. Thus it is very significant to determine and assign the suitable position for this case. Due to continuous decline in per capita water and the importance of nutritious preparation for people it is necessary to control the surface water using damp building or artificial recharge methods. Researchers of water sciences have studied the damp building and artificial recharge projects all over the world, drawn logarithm curve for cost against the amount of savable running water and concluded that it is frugal economically to accomplish artificial recharge projects especially flood distribution instead of damp building for the volume less than 30 million cube meter {\ref (Bize, et al., 1972)}. the experiences of under developed countries show that compressing the agriculture caused quick output purposes but they destroy the basic resources for a long term. It can be noticed in pasture destruction, forest resources reduction, deserts increase, reduction and destruction of surface water resources and ground water and exponential compress to the basic resources. In our country, planning in agricultural, rural and natural resources development has always been founded at the N Volume XIV Issue VIII Version I level of political development. This traditional attitude toward planning and development caused instability in using basic resources. During 2 previous decades, our country has taken activities to develop agriculture and natural resources comprehensively. Although these activities were slow and sluggish, they can develop a new attitude among experts, connoisseurs and decision makers in agriculture section. Based on this attitude, casual, one-direction and one-dimensional activities can solve part of short term problems and difficulties related to agriculture section and have pathetic effects on this section in long term. Water resources management is a set of various management activities aimed at the optimum utilization of water resources and reduction of economical, social and environmental damages and losses. Decision making issue in water resources management is very complex and complicated because of several decision indicators and criteria. Achieving a determine purpose, there are a lot of solutions with different priorities for various issues such as environmental, social, organizational and political problems. These necessities leads to use of multiple criteria decision making aimed at selection of best solution among different solutions. There are many examples of applications of TOPSIS in literature For instance: Saraf and Choudhury (1998) used remote sensing capabilities in extracting different layers like land usage, geomorphology, vegetation, and their integration in GIS environment to determine the most suitable area for artificial recharge of ground water. {\ref Mahdavi (1997, 16)} investigated water management and artificial recharge of ground water in Jourm city and indicated that controlling usage and recharge of water tables by the watershed management is the main management technique. {\ref Abdi and Ghayoumian (2001, 86)} prioritized the suitable areas for storing surface water and reinforcing ground water based on geophysics data, land usage, topography, their integration and analysis in GIS environment.Kia \hyperref[b15]{Heyrati (2004)} studied the function of flood distribution system in recharge of ground water in Moughar plain in Isfahan. Mahdavi et.al {\ref (2005)} attempted to find the best position for artificial recharge of ground water by RS and GIS techniques in watershed Shahr Reza in Isfahan and introduced this tool for this case efficiently. Also, {\ref Noori et al (2004, 635)} tried to find the appropriate areas for artificial recharge of ground water by recharge pools (recharge pools) and GIS technique in watershed Gavbandi and introduced alluvial fans and plain head (Dashtsar) as the best area for artificial recharge. \hyperref[b26]{Mousavi et al (2010)} found the potential appropriate areas for artificial recharge of ground water in the vicinity of Kamestan anticline by integration of remote sensing and GIS techniques and introduced broken formations, alluviums and river canals as the best position for artificial recharge. \hyperref[b24]{Mianabadi and Afshar (2008)} investigated and ranked the project of water supply in Zahedan using three methods: Induced Ordered Weighted Averaging (IOWA), Linear Assignment and TOPSIS methods, and then they compared the findings of these methods with the results of adaptable planning method {\ref (Mianabadi, 2008: 34-45)}. \hyperref[b19]{Limon and Martinez (2006)} used Multi Attribute Utility theory for optimum allocation of agriculture water in north of Spain {\ref (Limon, 2006: 313-336)}. Ahmadi et al (2002) used multiple criteria decision making to rank different projects of refining agriculture water to reuse them (Ahmadi, 2002: 339-352). Also, Anand Raj and \hyperref[b1]{Kumar (1996)} ranked management options of river basin by ELECTRE method {\ref (Anand, 1996: 326-335)}. The purpose of this study is zoning the best area for artificial recharge of underground basins in Mashhad watershed using effective factors in recharging underground water table by TOPSIS and GIS technique. In another way, this study aimed at the selection of most appropriate area to establish soil damps for the purpose of sustainable development of water resources using TOPSIS Method. of the area. Mentioned classes represent the studied zones in the area and subsequent calculations were done in each of these classes. Slope layer prepared base on digital elevation model o the area by surface analyses tool in 3D analyses. There were different processes to prepare drainage density layer and habitual density such as digitizing main and minor waterways layers on the topographical map1:50000 of the area, digitizing main and minor fault on geological map 1:100000 of area and density tool in Spatial Analyses. Iso-Precipitation layer prepared by interpolating method like cringing technique and linear relationship between rain-height using Interpolate tools in 3D analyses. Second, the investigated criteria for each height zones were calculated (Tables \hyperref[tab_2]{2 , 10}) and their layers prepared separately. After achieving a few numbers in each layer, the numbers were analyzed by TOPSIS method. Then considered watershed was ranked to select the best area for establishing soil damp. The ideal solution is the solution that maximizes the benefit criteria and minimizes the cost criteria, whereas the negative ideal solution is the solution that maximizes the cost criteria and minimizes the benefit criteria. In short, the ideal solution consists of all of best values attainable of criteria, whereas the negative ideal solution is composed of all worst values attainable of criteria. The optimal alternative is the one which has the shortest distance from the ideal solution and the farthest distance from the negative ideal solution. situation of TOPSIS method among the other Multi Criteria Decision Making showed in Figure \hyperref[b12]{(12)}. Making matrix has steady increasing and decreasing utility. \section[{II. Methods and Materials}]{II. Methods and Materials}? ?? = ? ? ? ? ? a\par 4. Creating dimensionless weighted matrix (V) to implement vector W as an input for algorithm:\par In order that the amounts of entries in matrix R gain equal value, , sum of weights of parameter (? j ) are multiplied to the column of this matrix one by one. The acquired matrix is normalized and weighted matrix which is shown by sign (V) (Table \hyperref[tab_4]{4}).? ?? = ? ?? . ? ?×? = ? v 11,? v 1j,? v 1n . . . . . . v m1,? v mj,? v mn ? 5.\par Determining positive ideal (A + ) and negative ideal (A -) by equations ( \hyperref[formula_3]{4}) and ( \hyperref[formula_4]{5}) respectively:? ?+ = ?? ?? ?? ? ?? ? ? 2 ? ?=1 ; ? = 1,2, ? , ?\textbf{(4)}? ?? = ?? ?? ?? ? ? ¯?? 2 ? ?=1 ; ? = 1,2, ? , ?\textbf{(5)}\par 6. Calculating distance size of i-alternaive with ideals and using Euclidean method, by equations ( \hyperref[formula_5]{6}) and \hyperref[b7]{(7)}:? ?+ = dictance of i ? alternative from positive ideal = ?? ?? ?? ? ?? ? ? 2 ? ?=1 ; ? = 1,2, ? , ?\textbf{(6)}\par ? ?? = distance of i ? alternative from negative ideal = ?? ?? ?? ? ? ¯?? \section[{? ?=1}]{? ?=1}\par ; ? = 1,2, ? , ?\par 7. Calculating relative closeness for i-alternative (A i ) i to ideal solution using equation ( 8):8. ?? ?+ = ? ?? ? ?+ +? ?? ; 0 ? ?? ?+ ? 1 ; ? = 1,2, ? , ?\par As you can see, if A i =A + , then d i+ =1 and cl i-=0, on the contrary if A i =A¯, then d i+ =1 and cl i-=0. In sum, the more alternative A i is closer to ideal solution, the more value of cl i+ is closer to unit. 9. Ranking alternatives based on descending order of cl i+ : This amount is fluctuating between 0 and 1. Thus, cl i+ =1 represents the highest rank and cl i+ =0 the lowest rank. \section[{III. Discussion}]{III. Discussion}\par In previous decades, decision making in water management problems and selection of better option among suggested options to solve a watershed problems was only done based on economical criteriaprofit in relation to cost-and on changing social and environmental criteria in to the economical criterion. However, today using Multi criteria decision making, it is not necessary to use financial equivalent of social and environmental criteria to select the best option. In fact, various qualitative and quantitative criteria can be used to prioritize and select the best options for water resources management.Nowadays, because of uncontrolled exploitation of ground water, water shortage is became doubled. Accurate control and management of these water resources can alleviate the problem of drought approximately.\par One of the management techniques of ground water resources is artificial recharge of basins and determination of the most appropriate place for it. The ground water resources are the largest and most importance reservoirs of fresh water on the earth for human being \par after glaciers and glacial zones \hyperref[b9]{(Freeze, 1979)}. Since these resources are 99\% of whole available fresh water, it is necessary to determine and exploit the ground water {\ref (Kouthar, 1986-19)}.Furthermore,it includes 80\% of being used resources in arid and semi-arid areas in most countries {\ref (Sedaghat, 1994)}. Due to Iran`s situation in desert and semi-desert area and its average annual rainfall about 250 mm, so there were many ways to prepare fresh water for agriculture, drinking and industry in different parts of country from a long time ago. Therefore, determination and zoning the most appropriate area for artificial recharge of underground aquifers should be considered in this plain.In recent years, water exploitation has become greater for many reasons such as population growth, industrial development, urbanization growth and consequently increased demand for food products. Hence the rate of exploitation and consumption ground water become greater than recharge of them, in other words input of ground water system is less than its output and system with negative balance sheet has positive feedback and it is collapsing. Thus it is very significant to determine and assign the suitable position for this case. The Purpose of This Study is Zoning Mashhad watershed for artificial recharge of underground aquifers using TOPSIS Model and GIS technique The results of TOPSIS method to find the most suitable area for artificial recharge of groundwater aquifers of Mashhad Basin showed in tables \hyperref[b0]{(1)} to \hyperref[b8]{(8)}. Therefore, a matrix is formed with rank (81) for data matrix, with 9 alternatives (height zones) and 9 related indicators (Materials, Precipitation , ) mm ( , Stream density, area(km2), Fault density, Slope, Heat, ) (Table \hyperref[tab_1]{1}). \section[{Conclusion}]{Conclusion}\par In recent years, water exploitation has become greater for many reasons such as population growth, industrial development, urbanization growth and consequently increased demand for food products. Hence the rate of exploitation and consumption ground water become greater than recharge of them, in other words input of ground water system is less than its output and system with negative balance sheet has positive feedback and it is collapsing. Thus it is very significant to determine and assign the suitable position for this case. Water resources management is a set of various management activities aimed at the optimum utilization of water resources and reduction of economical, social and environmental damages and losses. Decision making issue in water resources management is very complex and complicated because of several decision indicators and criteria. Achieving a determine purpose, there are a lot of solutions with different priorities for various issues such as environmental, social, organizational and political problems. These necessities leads to use of multiple criteria decision making aimed at selection of best solution among different solutions. The result and findings of different studies show that in TOPSIS method, zone 3 with (0/669) point promotes in first rank among 5 studied zones and thus it is the most appropriate zone to establish the proper area for artificial recharge of underground aquifers , in contrast zone 1 with (0/302) point goes down to the last rank and so it isn`t suitable for establishing damp and zones (4,2,5) with (0/650, 0/450, 0/325) points are located in next ranks.\par Volume XIV Issue VIII Version I \begin{figure}[htbp] \noindent\textbf{1}\includegraphics[]{image-2.png} \caption{\label{fig_0}Figure 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2}\includegraphics[]{image-3.png} \caption{\label{fig_1}Figure 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{8}\includegraphics[]{image-4.png} \caption{\label{fig_2}Figure 8 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{11}\includegraphics[]{image-5.png} \caption{\label{fig_3}Figure 11 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{12}\includegraphics[]{image-6.png} \caption{\label{fig_4}Figure 12 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{}\includegraphics[]{image-7.png} \caption{\label{fig_5}}\end{figure} \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{P{0.4057216494845361\textwidth}P{0.17788659793814432\textwidth}P{0.008762886597938144\textwidth}P{0.06572164948453608\textwidth}P{0.14896907216494845\textwidth}P{0.042938144329896906\textwidth}} \tabcellsep \tabcellsep \tabcellsep \tabcellsep on the importance of criteria. It is considerable that sum\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep of criteria weights should be equal to 1. In this study,\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep AHP model has been used to calculate the amount of\\ \multicolumn{4}{l}{. . a m1 a m2 ? a mn ? . . . . ? ? ? ? 2. Standardizing data and preparing normalized matrix}\tabcellsep (? j )\tabcellsep n ? ? j j=1 ? j . ? ? = 1 w´? = ? ? j . w j n j=1\\ \tabcellsep \multicolumn{3}{l}{(matrix R) by Equation (1):}\\ \tabcellsep \multicolumn{3}{l}{Since it is possible that quantitative amount of}\\ \multicolumn{4}{l}{criteria and indicators don`t have equal unit , the}\\ \multicolumn{4}{l}{dimensions of their units should be omitted. Thus, all}\\ \multicolumn{4}{l}{amounts of entries of Decision Making matrix should be}\\ \multicolumn{4}{l}{changed into dimensionless amount with following}\\ \multicolumn{2}{l}{formula:}\tabcellsep \tabcellsep \\ R IJ =\tabcellsep ? ?? ? ?? ?=1 ?? ?\tabcellsep 2\tabcellsep (1)\\ \tabcellsep \tabcellsep ? ? ? ? ?\tabcellsep ? 11 ? 12 ? ? 1? . . . . . . ? ?1 ? ?2 ? ? ?? ? ? ? ? ? 21 ? 22 ? ? 2? ?\\ \multicolumn{4}{l}{3. Determining weights for whole indicators (? j ) by}\\ \tabcellsep \multicolumn{3}{l}{equation (2) and modifying calculated (? j ) by}\\ \tabcellsep \multicolumn{2}{l}{equation (3):}\tabcellsep \\ \tabcellsep \multicolumn{3}{l}{In this process, the weights of all indicators are}\\ \multicolumn{4}{l}{calculated by expertise theories and approaches,}\\ \multicolumn{4}{l}{Linmap method, AHP model, Antropi model and based}\end{longtable} \par \caption{\label{tab_0}}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.032903225806451615\textwidth}P{0.038387096774193545\textwidth}P{0.1288709677419355\textwidth}P{0.10419354838709677\textwidth}P{0.08774193548387096\textwidth}P{0.10145161290322581\textwidth}P{0.08225806451612903\textwidth}P{0.06580645161290323\textwidth}P{0.07129032258064516\textwidth}P{0.13709677419354838\textwidth}} \tabcellsep \tabcellsep (\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Regions\tabcellsep Materials\tabcellsep Precipitation ) mm\tabcellsep Stream density\tabcellsep area ) km 2 (\tabcellsep Fault density\tabcellsep Slope\tabcellsep Heat\tabcellsep height\tabcellsep habitual density\\ 1\tabcellsep 3\tabcellsep 108.85\tabcellsep 48.52\tabcellsep 3090\tabcellsep 39.84\tabcellsep 20.34\tabcellsep 20.4\tabcellsep 1028\tabcellsep 7552.5\\ 2\tabcellsep 5\tabcellsep 122.1\tabcellsep 36.68\tabcellsep 2977\tabcellsep 38.79\tabcellsep 22.59\tabcellsep 17.3\tabcellsep 1348\tabcellsep 3916.14\\ 3\tabcellsep 9\tabcellsep 146.56\tabcellsep 41.73\tabcellsep 1629\tabcellsep 38.1\tabcellsep 27.37\tabcellsep 14.55\tabcellsep 1675\tabcellsep 2504.49\\ 4\tabcellsep 7\tabcellsep 189.4\tabcellsep 41.74\tabcellsep 1439\tabcellsep 38.26\tabcellsep 29.22\tabcellsep 10.7\tabcellsep 2058\tabcellsep 1448.65\\ 5\tabcellsep 1\tabcellsep 283.59\tabcellsep 36.5\tabcellsep 764\tabcellsep 39.26\tabcellsep 26.01\tabcellsep 5.2\tabcellsep 2755\tabcellsep 1590.05\end{longtable} \par \caption{\label{tab_1}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.1571698113207547\textwidth}P{0.027264150943396227\textwidth}P{0.07056603773584905\textwidth}P{0.08820754716981133\textwidth}P{0.07858490566037735\textwidth}P{0.07698113207547169\textwidth}P{0.07698113207547169\textwidth}P{0.06415094339622641\textwidth}P{0.06254716981132075\textwidth}P{0.06575471698113207\textwidth}P{0.08179245283018868\textwidth}} Year 2014 Year 2014\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ 2 50\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Volume XIV Issue VIII Version I\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ )\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ ( B\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Global Journal of Human Social Science -\tabcellsep Regions 1 2 3 4 5\tabcellsep Materials 0.2335 0.3892 0.7006 0.5449 0.0778\tabcellsep Precipitation ) mm ( 0.2684 0.3010 0.3613 0.4669 0.6991\tabcellsep Stream density 0.5258 0.3975 0.4522 0.4523 0.3955\tabcellsep area ) km 2 ( 0.6345 0.6112 0.3345 0.2955 0.1570\tabcellsep Fault density 0.4585 0.4465 0.4385 0.4404 0.4519\tabcellsep Slope 0.3594 0.3991 0.4836 0.5163 0.4595\tabcellsep Heat 0.6240 0.5292 0.4451 0.3273 0.1591\tabcellsep height 0.2457 0.3222 0.4003 0.4919 0.6585\tabcellsep habitual density 0.8276 0.4291 0.2744 0.1587 0.1742\end{longtable} \par \caption{\label{tab_2}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.15727650727650727\textwidth}P{0.0971933471933472\textwidth}P{0.028274428274428276\textwidth}P{0.08835758835758836\textwidth}P{0.07598752598752599\textwidth}P{0.07068607068607069\textwidth}P{0.06361746361746362\textwidth}P{0.05831600831600832\textwidth}P{0.02650727650727651\textwidth}P{0.03711018711018711\textwidth}P{0.047713097713097716\textwidth}P{0.09896049896049897\textwidth}} \tabcellsep \tabcellsep \tabcellsep (\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Parameters\tabcellsep \tabcellsep Materials\tabcellsep Precipitation ) mm\tabcellsep Stream density\tabcellsep area ) 2 km (\tabcellsep Fault density\tabcellsep Slope\tabcellsep Heat\tabcellsep height\tabcellsep habitual density\tabcellsep Wij\\ \tabcellsep \multicolumn{2}{l}{Materials 1}\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 5\tabcellsep 6\tabcellsep 7\tabcellsep 8\tabcellsep 9\tabcellsep 0.307\\ Precipitation\tabcellsep ) mm (\tabcellsep 0.5\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 5\tabcellsep 6\tabcellsep 7\tabcellsep 8\tabcellsep 0.2182\\ \multicolumn{3}{l}{Stream density 0.33}\tabcellsep 0.5\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 5\tabcellsep 6\tabcellsep 7\tabcellsep 0.1543\\ Area(km2)\tabcellsep \tabcellsep 0.25\tabcellsep 0.33\tabcellsep 0.5\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 5\tabcellsep 6\tabcellsep 0.1089\\ \multicolumn{3}{l}{Fault density 0.2}\tabcellsep 0.25\tabcellsep 0.33\tabcellsep 0.5\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 5\tabcellsep 0.0764\\ \tabcellsep \multicolumn{2}{l}{Slope 0.16}\tabcellsep 0.2\tabcellsep 0.25\tabcellsep 0.33\tabcellsep 0.5\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 0.0533\\ \tabcellsep \multicolumn{2}{l}{Heat 0.14}\tabcellsep 0.16\tabcellsep 0.2\tabcellsep 0.25\tabcellsep 0.33\tabcellsep 0.5\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 0.037\\ \tabcellsep \multicolumn{2}{l}{height 0.12}\tabcellsep 0.14\tabcellsep 0.16\tabcellsep 0.2\tabcellsep 0.25\tabcellsep \multicolumn{2}{l}{0.33 0.5}\tabcellsep 1\tabcellsep 2\tabcellsep 0.0259\\ \multicolumn{3}{l}{habitual density 0.11}\tabcellsep 0.12\tabcellsep 0.14\tabcellsep 0.16\tabcellsep 0.2\tabcellsep \multicolumn{2}{l}{0.25 0.33}\tabcellsep 0.5\tabcellsep 1\tabcellsep 0.0189\\ \tabcellsep \multicolumn{2}{l}{Sum 2.81}\tabcellsep 4.7\tabcellsep 7.58\tabcellsep 11.44\tabcellsep 16.2\tabcellsep 22\tabcellsep 28.8\tabcellsep 36.5\tabcellsep 45\tabcellsep 1\end{longtable} \par \caption{\label{tab_3}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.026771653543307086\textwidth}P{0.08700787401574803\textwidth}P{0.10931758530183727\textwidth}P{0.09816272965879265\textwidth}P{0.09593175853018372\textwidth}P{0.09593175853018372\textwidth}P{0.07808398950131233\textwidth}P{0.0758530183727034\textwidth}P{0.08031496062992126\textwidth}P{0.10262467191601049\textwidth}} \tabcellsep \tabcellsep (\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ Regions\tabcellsep Materials\tabcellsep Precipitation ) mm\tabcellsep Stream density\tabcellsep area ) km 2 (\tabcellsep Fault density\tabcellsep Slope\tabcellsep Heat\tabcellsep height\tabcellsep habitual density\\ 1\tabcellsep 0.0717\tabcellsep 0.0586\tabcellsep 0.0811\tabcellsep 0.0691\tabcellsep 0.0350\tabcellsep 0.0192\tabcellsep 0.0231\tabcellsep 0.0064\tabcellsep 0.0156\\ 2\tabcellsep 0.1195\tabcellsep 0.0657\tabcellsep 0.0613\tabcellsep 0.0666\tabcellsep 0.0341\tabcellsep 0.0213\tabcellsep 0.0196\tabcellsep 0.0083\tabcellsep 0.0081\\ 3\tabcellsep 0.2151\tabcellsep 0.0788\tabcellsep 0.0698\tabcellsep 0.0364\tabcellsep 0.0335\tabcellsep 0.0258\tabcellsep 0.0165\tabcellsep 0.0104\tabcellsep 0.0052\\ 4\tabcellsep 0.1673\tabcellsep 0.1019\tabcellsep 0.0698\tabcellsep 0.0322\tabcellsep 0.0336\tabcellsep 0.0275\tabcellsep 0.0121\tabcellsep 0.0127\tabcellsep 0.0030\\ 5\tabcellsep 0.0239\tabcellsep 0.1526\tabcellsep 0.0610\tabcellsep 0.0171\tabcellsep 0.0345\tabcellsep 0.0245\tabcellsep 0.0059\tabcellsep 0.0171\tabcellsep 0.0033\end{longtable} \par \caption{\label{tab_4}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.0425\textwidth}P{0.1105\textwidth}P{0.068\textwidth}P{0.059500000000000004\textwidth}P{0.06375\textwidth}P{0.09775\textwidth}P{0.09775\textwidth}P{0.102\textwidth}P{0.1275\textwidth}P{0.08075\textwidth}} Ideals\tabcellsep habitual density\tabcellsep height\tabcellsep Heat\tabcellsep Slope\tabcellsep Fault density\tabcellsep area ) km 2 (\tabcellsep Stream density\tabcellsep Precipitation ) ( mm\tabcellsep Materials\\ A+\tabcellsep 0.215\tabcellsep 0.152\tabcellsep 0.081\tabcellsep 0.069\tabcellsep 0.035\tabcellsep 0.019\tabcellsep 0.005\tabcellsep 0.017\tabcellsep 0.015\\ A-\tabcellsep 0.023\tabcellsep 0.058\tabcellsep 0.061\tabcellsep 0.017\tabcellsep 0.032\tabcellsep 0.027\tabcellsep 0.023\tabcellsep 0.006\tabcellsep 0.003\end{longtable} \par \caption{\label{tab_5}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{6} \par \begin{longtable}{P{0.10780487804878049\textwidth}P{0.05390243902439024\textwidth}P{0.05390243902439024\textwidth}P{0.05390243902439024\textwidth}P{0.05390243902439024\textwidth}P{0.5265853658536586\textwidth}} \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2014\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 51\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep I\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Version\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep VIII\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Issue\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep XIV\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( B )\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Global Journal of Human Social Science -\\ regions Distance\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 5\\ D i +\tabcellsep 0.1727\tabcellsep 0.1319\tabcellsep 0.0833\tabcellsep 0.0814\tabcellsep 0.1996\\ D i -\tabcellsep 0.0750\tabcellsep 0.1083\tabcellsep 0.1936\tabcellsep 0.1513\tabcellsep 0.0962\\ \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep © 2014 Global Journals Inc. (US)\end{longtable} \par \caption{\label{tab_6}Table 6 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{7} \par \begin{longtable}{P{0.17\textwidth}P{0.136\textwidth}P{0.136\textwidth}P{0.136\textwidth}P{0.136\textwidth}P{0.136\textwidth}} Cl i\tabcellsep C1\tabcellsep C2\tabcellsep C3\tabcellsep C4\tabcellsep C5\\ Amount\tabcellsep 0.3028\tabcellsep 0.4515\tabcellsep 0.7003\tabcellsep 0.6502\tabcellsep 0.3227\end{longtable} \par \caption{\label{tab_7}Table 7 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{8} \par \begin{longtable}{P{0.28936170212765955\textwidth}P{0.10851063829787233\textwidth}P{0.10851063829787233\textwidth}P{0.10851063829787233\textwidth}P{0.1175531914893617\textwidth}P{0.1175531914893617\textwidth}} Region\tabcellsep 1\tabcellsep 2\tabcellsep 3\tabcellsep 4\tabcellsep 5\\ Point (Fuzzy Logic)\tabcellsep 0.3036\tabcellsep 0.4507\tabcellsep 0.6691\tabcellsep 0.6502\tabcellsep 0.3252\\ Rank\tabcellsep Fifth\tabcellsep Third\tabcellsep First\tabcellsep Second\tabcellsep Fourth\\ IV.\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_8}Table 8 :}\end{figure} \footnote{© 2014 Global Journals Inc. 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