پتانسيل‌يابي منابع آبهاي زيرزميني در دشت خرم آباد با استفاده از دو روش منطق فازي و شبكه عصبي مصنوعي

Groundwater Potential Finding in Khorramabad Plain Using Fuzzy Logic and Artificial Neural Network Methods

در سال‌هاي اخير با افزايش روز افزون جمعيت و توسعه صنعتي بهره‌برداري از منابع آب زيرزميني چندين برابر شده است، با تداوم اين عمل سطح آب‌هاي زيرزميني روز به روز افت كرده است. بنابراين شناسايي اين منابع، استفاده بهينه از آن به معناي برداشت پايدار و هميشگي از اين ثروت خداداي است.هدف ازاين تحقيق ارزيابي و پتانسيل‌يابي منابع آب‌هاي زير‌زميني با استفاده از رو‌ش‌هاي منطق‌ فازي و شبكه عصبي مصنوعي دردشت خرم‌آباد است. دراين تحقيق ابتدا نقشه فهرست چاه‌ها تهيه و بعد پارامترهاي موثر درپتانسيل‌يابي از قبيل: لايه ارتفاع، شيب، جهت شيب، انحناي سطح، شاخص‌رطوبت توپوگرافي، كاربري‌اراضي، خاك، زمين‌شناسي، فاصله از رودخانه، تراكم زهكشي، فاصله از گسل، تراكم گسل مشخص و نقشه آن‌ها در محيط نرم‌افزارArc GIS تهيه شد. جهت تهيه نقشه پتانسيل‌يابي منابع آب زيرزميني از دو روش منطق فازي و شبكه عصبي مصنوعي استفاده شد. در مدل شبكه ‌عصبي‌ مصنوعي الگوريتم پس‌انتشار خطا و تابع فعالسازي سيگموئيد به كار گرفته شد. ساختار نهايي شبكه داراي 11نرون درلايه ورودي، 11 نرون درلايه‌پنهان و 1 نرون درلايه خروجي گرديد. در مدل منطق فازي از اپراتورهاي عملگراجتماع فازي، عملگراشتراك فازي، عملگرضرب جبري فازي، عملگرجمع جبري فازي، عملگرگاما فازي استفاده شد. سپس نتايج هر دو مدل مورد ارزيابي قرار گرفت و در نهايت با مقايسه نتايج به دست آمده روش مناسب جهت پتانسيل‌يابي منابع آب‌هاي زيرزميني به دست آمد و همچنين مهمترين عوامل موثر در پتانسيل‌يابي منابع در منطقه مشخص گرديد. نتايج ارزيابي شبكه عصبي با چاه‌هايي با دبي بالا نشان مي‌دهد كه حدود80 درصد ازچاه‌ها درمناطق با پتانسيل متوسط به بالا است درحالي كه نقشه منطق فازي حدود 75 درصد چاه‌ها درمناطق با پتانسيل متوسط به بالا را نشان مي‌دهد. نتايج اين پژوهش نشان داد كه روش شبكه عصبي جهت پتانسيل‌يابي منابع آب زيرزميني نسبت به روش منطق فازي مناسب ترو كاربردي‌تراست.

In recent years, with the growing population, industrial development of groundwater resources has more than doubled, with groundwater levels continuing to fall and eventually reaching a point where there will be no more water to extract. Therefore identifying these resources, making optimum use of them means a permanent and permanent harvest of this natural gods wealth. Methods and Material Research Methodology: In this first phase, field studies and data collection were done. Wells map was prepared first and then effective parameters were identified: elevation layer, slope, slope direction, surface curvature, topographic moisture index, land use, soil, geology, river distance, drainage density, fault distance, fault density specified. an‎d their plan was made. Satellite images of ENVI and eCognation software were used for mapping the land and the images were classified with basic pixel and object crosshairs and fuzzy logic and artificial neural network methods were used. Based on fuzzy logic, baseline maps are first ranked based on their impact and importance on groundwater resource potential, and then determined using a fuzzy method for each specific class rating factor. In the neural network method, these agents, along with a number of wells, enter the network as the input layer. In this way, the pattern is trained by the network between the input parameters (network input) and the areas where potential water resources exist (network output), then for The input parameters of the catchment to the trained neural network are predicted corresponding outputs which are potential areas of groundwater potential. Then the results of both models are tested and finally by comparing the results of the neural network model with the fuzzy logic model an appropriate method for groundwater resource potential in the catchment is obtained and the most important factors in resource potential in the area are identified. Results and discussion Evaluation of effective layers in the potential of groundwater resources: The results of the elevation factor showed that the highest percentage (33%) of the area with high potential is located at an altitude of 1700-2000 m, which is the average altitude of the region, and the results of the slope factor study show that the highest percentage of high and medium potential areas is on the slope 0-1 is located and the highest percentage of areas with low potential is on the slope of 60- 173 and the highest percentage of areas with high and medium potential to the northeast direction and the highest percentage of areas with low potential is in the southern direction. The results of the study of lithology class (formation) show that the highest percentage of areas with high and medium potential. The weak are in the class (reserves of old and new mountaineering terraces and conifers). an‎d the highest percentage of soil in the region is in areas with high potential and weak in the group (rocky / intulse outlets) and the highest percentage of areas with medium potential in the group (rocky direction outlets / input solo).The results of the study of the distance from the river show that the highest percentage of areas with high potential is located in the nearest distance from the river 200 meters and the highest percentage of areas with medium and weak potential is in a range far from the river. The results of the fault gap survey show that the highest percentage of areas with high and medium potential is located in the closest distance from the fault (0-0.032) and the highest percentage of areas with low potential (0.115-0.170). Precipitation and temperature estimates show that the highest percentage of areas with high, medium and weak potential is in the range of 756-844 and the highest percentage of areas with high, medium and weak potential is in the range of 13-15. an‎d the highest percentage of areas with high and low potential is in the use of medium rangeland and the highest percentage of areas with medium potential is in forest use. The results of the groundwater map show that the highest percentage (36.85) of areas with high potential is in the class of 10.12-6.74. Evaluation of the classification maps of the results shows that the accuracy of kappa in object-class classification is 96% and in base pixel 85%. Neural network results also show that about 39% of the area has high potential of groundwater, while fuzzy logic map shows about 87% of the area with low potential Conclusion Using the descriptions needed for basic pixels and taps, we can provide you with faster access to various sites, loops, content and technical and engineering information in a variety of areas, in the entertainment and leisure markets. Read yourself below. From this research it can be concluded that your neural network can control the energy potential of water Underground is more practical than the appropriate fuzzy logic method because its results are closer to the ground. Factors such as slope factor can be considered as an important factor in the potential of groundwater resources because of the high percentage of potential areas in the range of 0-10%.