برآورد فرسايشپذيري ذاتي خاک در برابر باد به کمک الگوريتم ژنتيک در ترکيب با شبکه عصبي مصنوعي
محورهای موضوعی : کاربرد کامپیوتر در مسائل آب و خاکساينا جعفريان 1 , علی محمدی ترکاشوند 2 , عباس احمدی 3 , نازنین خاکی پور 4 , مریم مرعشی 5
1 - دانشجوي دکتري مديريت منابع خاک، گروه علوم و مهندسي خاک، واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران.
2 - استاد گروه علوم و مهندسي خاک، واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران.
3 - دانشيار گروه علوم و مهندسي خاک، دانشگاه تبريز، تبريز، ايران.
4 - استاديار گروه علوم و مهندسي خاک، واحد سوادکوه، دانشگاه آزاد اسلامي، سوادکوه، ايران.
5 - استاديار، گروه علوم و مهندسي خاک، واحد علوم و تحقيقات، دانشگاه آزاد اسلامي، تهران، ايران.
کلید واژه: شوری خاک, فرسایش‌پذیری, مدلسازی, EF, SIWE.,
چکیده مقاله :
زمينه و هدف: فرسايشپذيري ذاتي خاک در برابر باد (SIWE)، حساسيت ذاتي ذرات تشکيل دهنده خاک در مقابل کنده شدن و انتقال، در مقابل فرسايش است. اندازه‌گيري فرسايشپذيري ذاتي خاک در برابر باد مي‌تواند به وسيله دستگاه تونل باد صورت بگيرد، که عموما پرهزينه، مشکل و زمان‌بر است. از طرف ديگر به دليل تغيير مداوم شرايط مختلف زراعي و اقليمي اين ويژگي نيز داراي تغييرات زماني و مکاني ميباشد. بنابراين برآورد SIWE به وسيله ابزار هوش مصنوعي مي تواند گامي مهم در برنامه ريزي عرصه هاي تحت فرسايش بادي باشد. در اين تحقيق، برآورد اين شاخص به کمک مدل الگوريتم ژنتيک در ترکيب با شبکه عصبي مصنوعي بررسي شد. روش پژوهش: در منطقه مورد مطالعه که بخشي از دشت الله آباد در استان قزوين در مجاورت استان البرز است، 72 نمونه از عمق 10 -0 سانتي‌متري سطح خاک برداشته شد. شاخص جزء فرسايشپذيري بادي خاک (EF) که درصد خاکدانه هاي با قطر کوچک تر از 84/0 ميلي متر است در نمونه‌ها تعيين شد. همچنين بافت خاک (درصد رس، شن و سيلت)، pH، EC و کربنات کلسيم معادل اندازه-گيري شدند. نمونه‌هاي خاک برداشته شده از مزرعه بعد از هواخشک شدن از الک 75/4 ميلي‌متري عبور داده شده و بر روي سيني دستگاه تونل باد بصورت صاف ريخته شد. سپس دستگاه تونل باد، بادي با سرعت ثابت 18 متر بر ثانيه و به مدت 10 دقيقه ايجاد نمود. با استفاده از وزن رسوبات جمع آوري شده در انتهاي تونل بعد از انجام آزمايش، SIWE تعيين شد. مدل الگوريتم ژنتيک در ترکيب با شبکه عصبي مصنوعي بر طبق الگويتم آموزشي لونبرگ - مارکوارت با توجه به متغيرهاي داراي همبستگي مثبت با SIWE به عنوان ورودي مدل، تهيه و تحليل شد. يافته ها: مقدار pH خاک بين 00/7 تا 81/8 متغير بود. مقادير قابليت هدايت الکتريکي از 84/0 تا 3/49 دسي زيمنس بر متر (dS/m) متفاوت بود. داده هاي اجزاء بافت خاک، نشان دهنده مقدار بيشتر رس در مقايسه با اجزاء سيلت و شن در خاک‌ها مي باشد. حداقل آهک (CCE) در خاک، 15/3 درصد و حداکثر آن، 52/30 درصد بود. فرسايش پذيري ذاتي خاک در برابر باد فقط با دو متغير قابليت هدايت الکتريکي و EF همبستگي معني دار داشت. مدل الگوريتم ژنتيک هيبريد با شبکه عصبي مصنوعي با دو متغير ورودي EF و EC تهيه شد. بررسي صحت و دقت مدل نشان داد که مقدار R2 در داده هاي سري آموزش 9 درصد با داده هاي سري آزمون اختلاف داشت و مقدار خطا (RMSE)،kg s m-4 62/1 بود. در داده هاي سري آموزش، R2 نتايج بدست آمده از مدل (805/0) بيشتر از داده‌هاي نتايج بدست آمده از سري آزمون (714/0) بود. اگرچه داده هاي آموزش از R2 بيشتري برخوردار بودند، لذا خطاي (RMSE) نتايج داده هاي آموزش از آزمون بيشتر بود و در سري آزمون، مدل داراي پراکندگي (GSDER) کمتري بود. نتيجه گيري: از نتايج بدست آمده مي‌توان نتيجه گرفت که شوري خاک و فاکتور جزء فرسايش‌پذير خاک از ويژگي‌هاي مهم خاک هستند که مي‌توانند به عنوان تخمين‌گر مناسب وارد مدلهاي برآورد فرسايشپذيري خاک شوند. همچنين دقت تخمين مدل تلفيقي الگوريتم ژنتيک با شبکه عصبي مصنوعي براي داده‌هاي سري آموزش نسبت به دقت مدل براي داده‌هاي سري آزمون بيشتر است. اما مدل براي داده‌هاي سري آموزش از خطاي بيشتري برخوردار است. مقايسه خطا، دقت و صحت مدل در برآورد فرسايشپذيري ذاتي خاک در برابر باد در مقايسه با مطالعات مختلف فرسايش خاک و خصوصيات فيزيکي و شيميايي خاک، مدل تلفيقي الگوريتم ژنتيک و شبکه عصبي از صحت و دقت مناسبي در پيشبيني و برآورد فرسايشپذيري ذاتي خاک در برابر باد برخوردار است.
Background and aim: inherent soil erodibility against wind (SIWE) is the inherent sensitivity of soil constituent particles against uprooting and transport, against erosion. Measuring the inherent erodibility of soil against wind can be done by a wind tunnel device, which is generally expensive, difficult and time-consuming. On the other hand, this feature has significant temporal and spatial changes and depends on the measurement method. Estimating SIWE by artificial intelligence tools can be an important step in planning areas under wind erosion. In this research, the estimation of this index was investigated with the help of genetic algorithm model in combination with artificial neural network. Methods: Seventy two samples were taken from 10 cm of the soil surface in the studied area, which is a part of Allah Abad desert in Qazvin province, adjacent to Alborz province. In the samples, the soil wind erodibility (EF) index, which is the percentage of soil aggregates with a diameter smaller than 0.84 mm, was measured. Also, soil texture (percentage of clay, sand and silt), pH, EC and equivalent calcium carbonate (CCE) were measured. After air-drying, the soil samples taken from the field were passed through a 4.75 mm sieve and poured flat on the tray of the wind tunnel machine. Then, the wind tunnel device created a wind with a constant speed of 18 meters per second for 10 minutes. Using the weight of sediments collected at the end of the tunnel after the test, SIWE was determined. The genetic algorithm model in combination with the artificial neural network was prepared and analyzed according to the Levenberg-Marquardt educational algorithm according to the variables having positive correlation with SIWE as the input of the model. Results: The pH value of the soil varied between 7.00 and 8.81. Electrical conductivity values varied from 0.84 to 49.3 dS/m. The data of the soil texture components show a higher amount of clay compared to the silt and sand in the soils. The minimum lime (CCE) in the soil was 3.15% and the maximum was 30.52%. The inherent erodibility of soil against wind had a significant correlation with only two variables, electrical conductivity and EF. Hybrid genetic algorithm model was prepared with artificial neural network with two input variables EF and EC. The accuracy and precision of the model showed that the value of R2 in the data of the training series was 9% different from test series data and the error value (RMSE) was 1.62 kg s m-4. Conclusion: In the data of the training series, R2 of the results obtained from the model (0.805) was higher than the data of the results obtained from the test series (0.714). Although the training data had more R2, therefore, the error (RMSE) of the training data was higher than the test and in the test series, the model with dispersion (GSDER) was less. Comparing the error, precision and accuracy of the model in estimating the inherent erodibility of the soil against the wind in comparison with different studies of soil erosion and soil physicochemical properties, the integrated model of the genetic algorithm and the neural network of almost appropriate accuracy and precision in predicting and estimating the erosion. The soil has inherent flexibility against the wind.
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