Zahra Bahari Sojahrood; Reza Aghataher; Mohsen Jafari
Abstract
Extended Abstract Introduction Earth roughness represents a fluctuation of the earth’s surface, and it can be called the complexity of the earth (Wilson, 2012). Roughness calculation is of great importance and is the basis for lots of decision-making. There are various solutions for the roughness ...
Read More
Extended Abstract Introduction Earth roughness represents a fluctuation of the earth’s surface, and it can be called the complexity of the earth (Wilson, 2012). Roughness calculation is of great importance and is the basis for lots of decision-making. There are various solutions for the roughness calculation. The first description of roughness was presented by Kupers, in which the roughness surface is assumed to be a set of points (Kupers, 1957). According to this definition, the deviation from the height criterion of the points is considered as the roughness index. The calculation of roughness in vast areas is possible only through satellite interpretation. The images used for this purpose should be of considerable power (Ghafouri, 1394). The main purpose of this paper is to automatically determine the parts of the area using the digital elevation model (DEM), which are desirable for the user in terms of roughness. To achieve this goal, a local decision-making support system is needed. In most of the mentioned methods, roughness is calculated as a variable in a region. But, the purpose of the paper is to calculate the roughness in different parts and to select the optimal area of the user. In previous methods, in order to achieve the goal, the roughness variable had to be calculated in each range and these ranges had to be compared one by one. This process is time-consuming and sometimes the desirable accuracy is not obtained. Therefore, there is a need for a method that reduces the time and increases the accuracy. For other purposes of this paper, we can refer to the calculation of roughness on a surface. In this research, a new method was developed for determining the areas with the user’s desirable quality of roughness using a DEM and based on the fractal method and spatial decision-making support system and a system with robust tools was designed and implemented for estimating the roughness and it was tested by the digital elevation model of Iran. The results indicate that this method is very accurate. Materials & Methods Ground roughness is an important variable used in the sciences of the earth and astronomy. There is no unique definition for it. It can be defined as a variable to express the variability of the Earth’s surface on a certain scale. In this research, to determine the favorable areas of the user in terms of roughness, a number of methods including sigma T, sigma Z, fractal geometry and a developed method of fractal geometry were used to calculate the roughness. Various spatial analyses were also used in the system. Finally, the spatial decision-making support system was developed for ranking and selecting the patches. Results & Discussion The system was implemented in the ‘Visual Studio’ environment using the ‘C #’ language and the ‘arcengine’ library. This system consists of several parts. First part, is the determination of the area whose roughness is to be determined. The second part, is the extraction of the patches of that area, the third part, which is done after the extraction of spatial complications and descriptive information of each patch, is similar to a filter which is based on roughness calculation methods. The four parts is, the ranking of these patches, and the fifth part, is their classification. The system is designed in such a way that the digital elevation model of any areas with any accuracy can be used. In this research, a 90 meter digital elevation model of Iran and the raster layer of its slope (produced in ArcGIS environment) were used. To display, Google maps were used. This method has a high precision due to its pixel-to-pixel scanning capability of the area and it seems to be more accurate than the existing ones. In most roughness determination methods, there is a method that calculates the roughness in the determined area. But, in this paper, using a spatial decision-making system and using the division of the region into smaller regions, the desired qualitative areas of the user are determined in terms of roughness, therefore, this method is able to decide automatically with regard to the user’s needs. Quality is different for various applications in terms of roughness. Sometimes high roughness and sometimes low roughness is favorable. However, other methods only calculate an amount of roughness of a region and we have to extract the values for each part of the earth and apply the analysis to it, and then compare them to determine their desirability. Several methods of calculating the roughness can also be used in the system simultaneously. Conclusion Earth roughness is a term used to describe the irregularities of an area. In most cases, determining the roughness of the earth is very complicated. There are many methods for calculating the roughness. The proposed method in this project is an innovative idea which is based on spatial analysis, spatial decision-making support system and roughness calculation methods and is calculated using the Digital Elevation Model. The results show that this method is a powerful tool for calculating roughness. In order to improve and continue this work, the correlation of variables is suggested in the calculation and evaluation of the obtained results. In this paper, the values are also calculated at the surface of each patch and in rows regardless of the direction. Various models can be used to consider the order of cells in each patch and compare the results.
Pouria Kharazi; mohammad reza Yazdani; Haideh Ara; payam khazaealpour
Abstract
Extended Abstract 1. Introduction Problem related to water scarcity has always been one of the most important issues to be considered in arid and semi-arid regions. Due to the seasonality of surface water drainages and subsurface structures in these regions, it is necessary to use structures which ...
Read More
Extended Abstract 1. Introduction Problem related to water scarcity has always been one of the most important issues to be considered in arid and semi-arid regions. Due to the seasonality of surface water drainages and subsurface structures in these regions, it is necessary to use structures which control and store the water flow in order to be used during arid seasons. One of these types of structures is underground dam to control the flow of water in the subsurface. In semiarid and arid areas in which deserts are progressing, exploitation of water has been focused on underground surface water resources having the trouble of stability (Ouerdachi, L., et al., 2012). These conditions require the use of surface and underground short dams for exploiting water in developing countries with arid weather situations. At present, these kinds of structures have been desirably featured in terms of both implication and efficacy in performance (Nilsson 1986, Cavalcanti, N.B., 2001). 2. Materials and methods The area studied is part of Semnan province and its surroundings are 216467 hectares in width which geographically have the coordinates of 53D 81M to 53D 15M eastern longitude and 35D 13M to 35D 85M northern latitude. Evaluation of station climes studied by both Domarten and Amberje indicates that the studied span has a semi dry-cold climate at heights reaching cold-arid climate where lower-height areas exist. The situation of considered area is shown in Figure 1. With respect to climatic conditions, there are many syllabic flows throughout side hills of the area where underground dam’s establishment is a proper way to control aridity problem in arid seasons of this area. The most significant data of this study are related to topography, geology, underground water, and aqueduct resources. Software used in this research are both expert choice to prioritize and Geography information systems to combine data. In this research, basic criteria of evaluation including water, pedestals, fountain, economic-social criteria have been targeted at the highest range placing indicators and incidentals criteria at other categories of AHP. Then, proper locations for underground dam establishment have been prioritized. 3. Results and discussion Based on Gorry and Morton method, decision-making supportive systems are either the systems capable of transporting data or computerized systems which can be used to solve our problems clearly or partly clearly known or unknown respectively (Gorry, G. A., and Morton, M.S., 1971). Decision-making supportive systems are models which receive vast majority of data and deliver many solutions specifically designed to overcome the existing problem (Klosterman, R.E., 1997). Decision support systems (DSs) used in identifying proper location to underground dam establishment possess AHP mode and are used in three stages. 4. Conclusion Considering climatic conditions over the area studied and existing stream, Underground dam establishment highly affects both performance revising and balancing act in underground water removal. Using new methods instead of traditional and time-consuming ones, can highly help with saving time and cost to underground dam location-finding. In the first step of this research, presented DSs considering eliminating criteria in the area of 216467 hectares in width assign suitable pedestal in each limit which is capable of potential underground dam establishment. Then, in the second step, the most suitable strait in each limit is assigned to the underground dam establishment. In the latest step after weighting each main criterion at its related map and as well adding total values of map pixels together in a software called GIS, 8 locations to underground dam establishment at the second step of location-finding in fifth scenario were prioritized as follows: First scenario (weights equality of four main criteria) locations: 5, 3, 2, 4, 6, 1 Second scenario (priority with water criterion) locations: 5, 3, 2, 6, 1, 4 Third scenario (priority with pedestals criterion) locations: 5, 6, 4, 3, 1, 2 Forth scenario (priority with fountain criterion) locations: 3, 6, 4, 5, 1, 2 Fifth scenario (priority with economic-social issues) locations: 3, 1, 5, 4, 2, 6 As shown in most scenarios, pedestals A and D mostly take first to third place. Furthermore, theses pedestals as the best ones catering for all groups’ opinion to underground dam establishment can be offered.