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ارزیابی عملکرد شبکههای عصبی مصنوعی با تلفیق الگوریتم ژنتیک در برآورد سرعت نفوذ آب به خاک (مطالعه موردی: منطقه خداآفرین استان آذربایجان شرقی) | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 8، دوره 50، شماره 5، مهر 1398، صفحه 1127-1139 اصل مقاله (1.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2018.264020.667994 | ||
| نویسندگان | ||
| محمد صادق علیائی* 1؛ علی باریکلو2؛ مسلم ثروتی3 | ||
| 1عضو هیات علمی وزارت علوم، تحقیقات و فناوری ( معاونت پژوهش و فناوری) | ||
| 2دانش‌آموخته کارشناسی ارشد علوم خاک، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران | ||
| 3استادیار مرکزآموزش عالی شهید باکری میاندوآب، دانشگاه ارومیه، ارومیه، ایران | ||
| چکیده | ||
| نفوذ، نقش حیاتی را در چرخه هیدرولوژیکی با میزان پراکندگی آب به اجزای سطحی و زیرسطحی ایفا میکند. اندازهگیری مستقیم سرعت نفوذ، معمولاً کاربر، هزینهبر و وقت‌گیر هستند. شبکه عصبی مصنوعی، برنامهریزی بیان ژن و الگوریتم ترکیبی شبکه عصبی مصنوعی-الگوریتم ژنتیک بهعنوان روشهای غیرمستقیم برای تخمین نفوذ آب به خاک استفاده شدند. هدف از این مطالعه، توسعه یک مدل مناسب برای تخمین نفوذ آب به خاک با استفاده از استوانه مضاعف در 88 نقطه از منطقه خدآفرین استان آذربایجان شرقی میباشد. آنالیز همبستگی پیرسون نشان داد که از بین ویژگیهای خاکی، شن، سیلت، تخلخل کل و کربن آلی بیشترین همبستگی را با نفوذ آب به خاک دارند. مقادیر ضریب تبیین و ریشه میانگین مربعات خطای نرمال شده برای مدل شبکههای عصبی مصنوعی و برنامهریزی بیان ژن بهترتیب برابر 88/0، 9/7 و 75/0، 3/11 محاسبه شد که هر دو روش در ارزیابی حداقل و حداکثر مقادیر نفوذ آب به خاک از دقت کافی برخوردار نبودند. در روش شبکههای عصبی مبتنی بر الگوریتم ژنتیک از توابع تانژانت سیگموئیدی در لایه میانی و محرک خطی در لایه خروجی با 5 نرون در لایه فعال استفاده شد. این مدل از دقت و صحت بیشتری نسبت به مدل شبکههای عصبی مصنوعی و برنامهریزی بیان ژن برخوردار میباشد، بهطوریکه مقادیر R2 و NRMSE برای مدل ترکیبی عصبی- ژنتیک بهترتیب برابر 93/0 و 1/6 درصد بود. نهایتاً الگوریتم ژنتیک با بهینهسازی اوزان شبکههای عصبی باعث بهبود مدلسازی شد، لذا این روش بهعنوان روش کارا در تخمین نفوذ آب به خاک معرفی میگردد. | ||
| کلیدواژه‌ها | ||
| برنامهریزی بیان ژن؛ شبکههای عصبی مصنوعی؛ الگوریتم تلفیقی؛ ویژگیهای زودیافت | ||
| عنوان مقاله [English] | ||
| Performance Evaluation of Artificial Neural Networks conjunct with Genetic Algorithm for Estimation of Soil Infiltration Rate (Case Study: Khoda afarin Region of East Azerbaijan Province) | ||
| نویسندگان [English] | ||
| mohamad sadegh oliaei1؛ ali barikloo2؛ moslem servati3 | ||
| 1Member of the faculty of the Ministry of Science, Research and Technology (Department of Research and Technolog) | ||
| 2M.Sc. Graduated of Soil Science Department, Faculty of Agriculture, University of Zanjan, Zanjan, Iran | ||
| 3Assistant Professor, Shahid Bakeri High Education Center of Miandoab, Urmia University, Urmia, iran | ||
| چکیده [English] | ||
| Infiltration plays a pivotal role in the hydrologic cycle by effectively acting to partition water into surface and subsurface components. Direct measurement of infiltration rate is expensive and work and time consuming. Artificial Neural Networks (ANNs), Gene Expression Programing (GEP) and hybrid of ANN and Genetic Algorithm (ANN-GA) can be used for estimation of soil infiltration rate as an indirect methods. The main objective of this research was to develope an infiltration rate model in Khoda afarin region based on the collected data (88 double ring infiltration) and some soil properties. The Pierson correlation revealed among the soil properties, sand and silt contents, porosity and organic matter have the most correlation with the infiltration rate. Determination Coefficient (R2) and Normalized Root Mean Square Error (NRMSE) were calculated to be 0.88 and 7.9%, respectively for the ANN method and 0.75 and 11.3% for the GEP method. Both ANN and GEP methods perform poorly, in extrapolating the minimum and maximum amount of infiltration rate. The hybrid model of ANN-GA was the best model in terms of statistical indices including R2 (0.93) and RMSE (6.1%). This model comprised of 4 neurons (sand, silt, porosity percentage and OM) in input layer and 5 neurons using sigmoidal tangent functions in the hidden layer and linear activation functions in the output layer. The results indicated that the neural-genetics algorithm can be used to optimize weight parameter of artificial neural network. Overall the hybrid ANN-GA model showed better performance than the other models, so that the R2 and NRMSE for the hybrid model were 0.93 and 6.1% respectively. Therefore it is suggested as a powerful tool for estimating infiltration rate. | ||
| کلیدواژه‌ها [English] | ||
| Genetics programming, Artificial Neural Network, combinatory algorithm, easily- measurable paremeters | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 574 تعداد دریافت فایل اصل مقاله: 543 |
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