Investigation of the relationship between electrical conductivity ( EC ) of 1 water and soil , and landform classes using fuzzy model and GIS

24 In this research, the relationship between classes of landform, and electrical conductivity ( E C ) 25 o f soil and water in the Shiraz Plain, Fars province Iran was investigated using a combination 26 of geographical information system (GIS) and fuzzy model. The results of the fuzzy method for 27 water EC showed that 36.6% of the land to be moderately land suitable for agriculture; high, 28 31.69%; and very high, 31.65%. In comparison, the results of the fuzzy method for soil EC showed 29 that 24.31% of the land to be as not suitable for agriculture (low class); moderate, 11.78%; high, 30 25.74%; and very high, 38.16 %. In the total, the land suitable for agriculture with low EC is 31 located in the north and northeast of the study area. The relationship between landform and EC 32 shows that EC of water is high for the valley classes, while EC of soil is high in the upland drainage 33 class. In addition, the lowest EC for soil and water are in the plain small class. 34


Introduction
Soil features are largely controlled by the landforms on which they are developed.The physiographic penetration on soil properties is recognized based on the progress of the soil-landform relationship (Ali and Moghanm, 2013).According to landform formed by the same geomorphic processes, it is the main key of feature because it can easily be identified, and it is also that were responsible for making the undercoat material of the soils (Park and Burt, 2002;Henderson et al., 2005;Mini et al. 2007; Poelking et al., 2015).Also the research show that there is a clear relationship between landform and soils.So that the soil and the landforms control the hydrological erosional, biological, and geochemical cycles and based on type of landform can be predicted other parameters of watershed such as soil, erosion, biological and so on (Berendse et al., 2015;Brevik et al., 2015;Decock et al., 2015;Keesstra et al., 2012;Smith et al., 2015) Usage of remote sensing and geography information system (GIS) enable the production of multi presentive layers of soil properties, which provide a great source of data for the land use planners (Ali et al., 2007).GIS, with features like the ability to acquire and exchange many different sources, organization, retrieval and display of data, analysis of numerous data, and possibility to provide multiple services, has been introduced as an efficient tool in the planning.Combining GIS with fuzzy logic provides a comparatively new land evaluation method (Badenki and kurtener, 2004; Oinam et al, 2014; Wang et al., 2015).Incorporating both of these methods is more flexible, and reflects human creativeness and understanding more and more to make decisions.Fuzzy inference is considered as a deduction for mathematical modeling in imprecise and vague processes, uncertainty about data and thus makes a context for modeling uncertainly (Kurtener, 2005).
Ali and Moghanm (2013) studied the variation of soil properties over the landforms around Idku Lake, Egypt.The spatial distribution of CaCO3, EC, organic matter (OM), pH, nitrogen (N), phosphor (P), potassium (K), iron (Fe), manganese (Mn), copper (Cu) and zinc (Zn) over the various landforms was discussed in detail.The results show that the change of CaCO3, EC and OM is minimal in the landforms of sand sheets, hammocks, sabkhas, clay flats and former lake-bed.
Aliabadi and Soltanifard (2014) apply GIS and fuzzy inference for determination of the impact of water and soil EC, and calcium carbonate on wheat crop.Regarding the results of the fuzzy inference system, 76% was achieved using the of Mamdani and 52 percent of accuracy for the technique Sugeno were achieved.One of the largest wheat producing regions was located in the Shiraz Plain, Fars province Iran (Bijanzadeh et al., 2014).The aim of this study is to investigate of the relationship between landform classes and EC of water and soil in the Shiraz Plain using a combination of GIS and fuzzy model.The methodology employed in this study is summarized in Figure 1.The study area has an area of 3,909 km 2 and is located at longitude of N 29° 06΄-29° 43΄and 83 latitude of E 52° 18΄ to 53° 28΄ (Figure 2).The altitude of the study area ranges from the lowest  The evaluation of land suitability for agricultural production (in particular wheat crop) in the area is essentialist critical, and should consider environmental factors and human conditions (Soufi, 2004;Bijanzadeh et al., 2014).One of the factors that is main in the amount of soil and water salinity.

Inverse Distance Weighted (IDW)
IDW model was used for interpolating the EC properties.IDW interpolation explicitly implements the assumption that things that are close to one another are more alike than those that are farther apart.To predict a value for any unmeasured location, IDW will be used that measures neighborhood values in the predicted location.Assumed value of an attribute f at any unsampled point is an average of distance-weighted of sampled points lying within a defined neighborhood around that unsampled point.Basically it is a weighted moving average (Burrough, et al., 1998): Where x0 is the estimation point and xi are the data points within a chosen surrounding.The weights (r) are related to distance by dij.

Fuzzy method
In the research, model functions are accustomed to compute membership function (MF), as described in Figure 3 (Burrough and McDonnell, 1998).In such status, an asymmetric function needs to be applied (Models 1 and 2) (Figure 3).
In this study, in order to define fuzzy rule based membership functions, the categories shown in Tables 1 and 2 are used.Also the classes of canyons, deeply incised streams, midslope and upland drainages, shallow valleys, and tend to have strongly negative plane form curvature values.On the other hand, local ridges / hills in valleys, midslope ridges, small hills in plains and mountain tops, and high ridges have strongly positive plane form curvature values.

Inverse Distance Weighted (IDW)
IDW interpolation was used to produce the prediction of soil and water EC, as shown in Figure 4.The lowest and highest output for IDW were 0.016 and 14.48 respectively for water EC, while the lowest and highest soil EC were 0 and 34.5 respectively.The interpolation maps for soil and

Fuzzy method
Fuzzy maps were prepared for soil and water EC, as shown in Figure 6     water management in this area.Even the studies show that there is relationship between soil structural stability and land use (Saha and Kukal, 2015).The results showed that a degradation of soil physical attributes due to the conversion of natural ecosystems to farming system and increased erosion hazards in the lower.So the soil parameters depend with land use, so that with changes in land use, they also change.In this study, the relationship between classes of landform, and electrical conductivity ( E C ) o f soil and water was in the Shiraz Plain was investigated using a combination of geographical information system (GIS) and fuzzy model.The results of the fuzzy method for water EC showed that 36.6% of the land to be moderately land suitable for agriculture; high, 31.69%; and very high, 31.65%.In comparison, the results of the fuzzy method for soil EC showed that 24.31% of the land to be as not suitable for agriculture (low class); moderate, 11.78%; high, 25.74%; and very high, 38.16 %.In the total, the land suitable for agriculture with low EC is located in the north and northeast of the study area.The relationship between landform and EC shows that EC of water is high for the valley classes, while EC of soil is high in the upland drainage class.In addition, the lowest EC for soil and water are in the plain small class.

Conclusion
Also by El-Keblawy et al (2015) investigated relationships between landforms, soil characteristics and dominant xerophytes in the northern United Arab Emirates.Soil texture, electrical conductivity (EC) and pH were determined in each stand.Also the results show that the soil and the landforms also control the geomorphological and hydrological processes (Cerdà and García-Fayos, 1997, Cerdà, 1998, Dai et al, 2015, Nadal-Romero et al., 2015).

Figure 1 .
Figure 1.Flowchart of the methodology employed to investigate the relationship between landform

84 of 1 , 5 Solid
433 m to the highest of 3,083 m.The region is located in the north of the Fars province, which 85 has cold winters with hot summers.The average temperature for the area is 16.8 °C, ranging 86 between 4.7 and 29.2 °C (Soufi, 2004).The research area is a biodiversity of mountains, relief and 87 lithology, and geological characteristics such as for instance sedimentary basin and elevated reliefs 88 Earth Discuss., doi:10.5194/se-2016-30,2016 Manuscript under review for journal Solid Earth Published: 10 March 2016 c Author(s) 2016.CC-BY 3.0 License.(Soufi, 2004).The main land use types of the region are agriculture, range land, farming and forests.
If MF(xi) shows individual membership value for i th land property x, then in the computation process these model functions (Models 1 to 2) show the following form: For asymmetric left (Model 1): Solid Earth Discuss., doi:10.5194/se-2016-30,2016 Manuscript under review for journal Solid Earth Published: 10 March 2016 c Author(s) 2016.CC-BY 3.0 License.

3. 3 .( 9 Solid
Landform classificationTPI(Weiss, 2001) compares the elevation of each cell in a DEM to the mean elevation of a specified neighborhood around that cell.Positive TPI (Eq.(4)) compares the elevation of each cell in a DEM to the mean elevation of a defined neighborhood around that cell.Mean elevation is subtracted from the elevation value at center the model point under evaluation = elevation of grid n = the total number of surrounding points employed in the evaluation Incorporating TPI at small and large scales permit a number of nested landforms to be distinguished (Table3).The actual breakpoints among classes can be selected to optimize the classification for a specific landscape.As in slope position classifications, additional topographic metrics, such as for Earth Discuss., doi:10.5194/se-2016-30,2016 Manuscript under review for journal Solid Earth Published: 10 March 2016 c Author(s) 2016.CC-BY 3.0 License.example differences of elevation, slope, or aspect within the neighborhoods, can help delineate landforms more accurately (Weiss 2001).

Solid
Earth Discuss., doi:10.5194/se-2016-30,2016 Manuscript under review for journal Solid Earth Published: 10 March 2016 c Author(s) 2016.CC-BY 3.0 License.water EC are shown in Figure 5.The statistical properties of the interpolated soil and water EC 168 are shown in Table 4 .

Figure 5 .
Figure 5. Interpolated maps of study area for (a) water and (b) soil EC.

Figure 6 .
Figure 6.Fuzzy maps of the study area for (a) soil and (b) water EC.

Table 3 .
Topographic Position Index (TPI) thresholds for small and large neighborhoods used to

Table 4 .
Descriptive statistics of the EC water and EC soil

Table 5 .
Areas of the classes for water and soil EC.