The Discovery of New Water Resources in the Yazd - Ardakan Basin by GIS and RS h. tabatabaei bafruei1*, 1-Senior Experts of National Cloud Seeding Research Center (NACSER), Iran * Corresponding author: seyed hamed tabatabaei bafruei, National Cloud Seeding Research Center (NACSER), Iran, Email: [email protected] Tel: +983518250551-2, Fax: +9835108251403 Abstract: At first, we begin with general recognition of the region, for performing the research project of the discovery of new water resources in the Yazd- Ardakan,tabas, At this stage, along, with the assessment of general character is tics of the region, we regarded the research records of the mentioned range. Then, along with brief assessment of the meteorology of the research domain, we identified the aquifer condition. In this regard, we considered the extension and the kind of aquifer and the kind of aquifer and characteristics of water resources of the above mentioned range. After the general recognition of the region for the purpose of pursuing the surface water layers, at first with the use of remote sensing technique and GIS, required layers was established, then along with development and providing of the digital maps and analyzing the layers in GIS, field data acquiring of the required data was done. At the end, we provided the factor maps with developing the layers, and with couple compare is on method, each of the factors was weighted and with the use of sign super position model and fuzzy model and with the use of Concluosion rules and fuzzy operators of the map, new water regions were provided. Keywords: GIS; fuzzy model; Concluosion rules; Water Resources; overlay 1. INTRODUCTION In programming and management of water resources, using new technologies like receive and process of data through satellite, considering features like wide and general view and high speed of data transmission, is used as an appropriate tool for evaluation of water resource discovery. Geographical Information System (GIS), using software facilities, is able to assimilate all data and spot information of under ground (well, spring and aqueduct) and above ground water resources and provide it for easy use of researchers. Regarding the considerable volume of data process in Hydrologic analyzes, using GIS can improve speed and precision. By wide usage of GIS, RS and satellite photos in water resource study, the time between studies and project implementation is reduced and access to assimilated data is possible because, considering the abilities of GIS, answering the conditional questions is much easier and before studies and implementing expensive projects (geophysics and geo electric) on water resources, using GIS and RS, easier and more accessibly, it is possible to recognize possible areas of water reservoirs and considering different parameters and information layers, make decisions on final findings. . 2. MATERIALS AND METHODS Methodology of this research, for finding new reservoirs is as follow: 2-1- preparation of layers (was done through 4 main methods) 2-1-1- using remote sensing technique and GIS for finding spot locations and layers like cracks, salty lands, some petrology information , determine the plant variety and denoting them, land usage (Figure no 1), nominating the soil type (Figure no 2), recognition of sedimentary areas, water reservoir stone layers, sand hills, and plant variety, to detect low depth reservoir layers. Figure No 1 –land usage plan for study area Figure No 2 –soil type plan for study area 2-1-2- preparation and aggregation of numeral plans along with descriptive information of geology plans (Figure no 3) 2-1-3- producing the needed layers through GIS analyzes , using other layers like DEM and numeral height model, slope plan , slope and canals direction. 2-1-4- site visit, gathering data of existing water resources (Figure no 4) and also basin confine Figure No 3 - geology plan of study area Figure No 4 – water resource plan of study area 2-2- presenting the 'producing layers image' system and then overlay of created numeral layers. 2-3- creating valuation field for all producing layers and evaluating the different parameters record through experts comments. value 3 3 3 1 3 3 2 3 1 3 2 4 4 3 1 3 usage Lake, waterwheel and dam Forest Rain based implant pasture Drench implant Gallant pastures Sterile territories Watery territories Salty lands forest Implanted jungle Salty lake sands basin City zone Poor pastures code 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 15 Table 1 - land usage Value 1 1 3 3 1 4 5 5 2 5 2 Type Mountain Hill plateau Hillside moors Residential and industrial zones Torrent moors Outflow gravels Gravel alluvial Mix territory Shallow territory Other territories Table 2 – soil type Code 1 2 3 4 6 7 8 9 10 11 12 value 1 3 4 2 3 2 1 1 3 4 Plant variation Sterile (without stone emerge) pastures Drench implant and garden desert Implanted jungle Pastures over %50 density Pastures with %25 to %50 density Pastures with %5 to %25 density Residential zone Sand hills Salty lands code BL IF K PF R1 R2 R3 URB SS SL Table 3 – plant variety Value 1 2 3 4 5 Grade Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Code 1 2 3 4 5 Table 4 – canals After preparation and aggregation of necessary data and layers, factor plans were provided and re categorized. Then, using mutual comparison method , which is a part of AHP process, each factor was evaluated and finally, through index overlay model, prospect model was provided and new water resources plans were created. Also, using fuzzy logic model, each factor was given fuzzy membership and at the end, by using conclusion rules and fuzzy operators map of new water resources area was created. Overlay of layers have different types, which to mention some of them, 'index overlay' and 'fuzzy logic model' can be mentioned. Presenting a proper model for finding new water reservoirs needs exact recognition of variables and effective environmental parameters like topographical elements, plant variety, land usage and so on. Knowing the effect of above told parameters and their effect scale needs descriptive geographical studies so that referring them, the effectiveness of those parameters can be judged. 2-4- evaluation and grading the layers according to different scales and using AHP methods. value 1 2 3 4 5 6 7 8 9 10 11 Bottom stone layer Well aqueduct springs lit logy sill Physiographic type Bottom stone layer Canals Salty lands Land usage Plant variation Table 5 – grade of layers code 1 2 3 4 5 6 7 8 9 10 11 2-5- compilation and analyze of results of evaluation of layers in order to final decision making on choosing proper spots for probable water reservoir levels and new water reservoirs which are done here through fuzzy logic and calculation methods . Description of fuzzy logic: Fuzzy logic is a derivative of fuzzy numbers (intolerable volume logic) which opposing the classic (Bully or 0 and 1) argues approximate measures (for example: not completely right and not completely wrong, or not just 1 and not just 0). This theory focuses on intolerable measures so that it is used for solving very precise problems of actual life (it uses actual life patterns). Most of times, measure of rightness of some thing is mistaken with probability of it. To bring an example, it can be said that if we say some one is good, you might judge that he is only %8 good and your friend judge him to be only %3 good but this doesn't mean that that person is not good (your different point of views does not effect the main idea of that person's goodness, in other words it is the matter of different percentage not probability) in fuzzy logic, every problem or event, does not has an exact answer, it is always defined as an incommensurable measure between 0 and 1 (including 0 and 1 ). To make it more clear, another example is brought: Some one is living in a house with 2 rooms (a sitting room and a kitchen) it can't be said that in all conditions, he is in sitting room or he is in the kitchen (he may stand between them) he can stand between 2 rooms so that a part of his body be in kitchen and the other part in the sitting room, determination of measure of him which is in each room is explained as a member of a fuzzy complex. Fuzzy logic uses values for its measures like, between 0 and 1 (including 0 and 1), different measures of grey (including black and white) and imprecise words like "a little", "totally" and "so much". The theory of 'fuzzy complexes and probabilities' was presented by professor lotfi zadeh in 1965 in Berkley University (California). This logic is widely used and accepted but also is one of the most argued ones. Critics believe that probability is deeply dependent on mathematics and in the other hand, fuzzy logic can't be considered as an independent complex from complex theories because its members are defined with arbitrary words. 2-5-1- classification and creating raster layers from vector layers which some samples are shown in Figure no. 5, 6 and 7 . 2-5-2- making models through calculation in GIS. Figure no 8 2-5-3- classification and creation of fuzzy layers from raster layers (which are created using fuzzy logic) samples of these raster plans are shown in Figure no. 9, 10, 11, 12. 2-5-4- making models of created fuzzy layers using fuzzy logic (no. 13) Figure No 5 - raster map of land usage Figure No. 7 – raster map of soil type Figure No. 9 – fuzzy map of basin confine Figure No 11 – fuzzy map of lit logy Figure No 6 – raster map of geological valuation Figure No 8 – topology map and spot map of new water reservoirs (using calculation in GIS) Figure No 10 – fuzzy map of land usage Figure No 12 – fuzzy map of soil type Figure No 13 – topology map and location map of new water reservoirs (using fuzzy in GIS) References Usage of IT in geology – kazem Alavi panah PhD Remote sensing – Rezaiee and Fatemi GIS – P.A Baro (translated by Taherkia PhD) Remote sensing (principals and application) – Rabiee PhD Main arguments of M.A in GIS and RS – Deleer and Monzavi 6- Enviromental Geology- (K.knodel-G.lange-H.j.voigt) 7- Data Assimilation for atmospheric,oceanic and Hydrologic Applications –(Seon k.park.liang xu) 8. 8- Atmosphoric ultraviolet Remote sensing (Robert E.Huffman) 9. 9- Remote sensing of impervious surfaces (Qihao weng) 10. 10- Image processins for Remot Sensing (C.h.Chen) 11. 11- Thermal Remote sensing in land Surface processing- (A.Quattrochi - C.luvail) 12. 12- Remote sensing- (R.Schott) 1. 2. 3. 4. 5. 6. 7.