مقایسه عملکرد شبکه‌های عصبی مصنوعی و برنامه‌ریزی بیان ژن در برآورد منحنی مشخصه آب در خاک‌های جنگلی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران

2 استادیار-گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران

3 دانشیار -گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران

چکیده

منحنی مشخصه آب خاک یکی از پارامترهای فیزیکی مهم و کاربردی در مطالعات مرتبط با جریان آب در خاک شناخته می­شود. روش مستقیم اندازه­گیری منحنی مشخصه آب خاک مستلزم صرف زمان و هزینه بالایی است. به همین دلیل روش­های غیرمستقیم متنوعی از جمله مدل­های هوشمند توسعه پیدا نموده­اند. در این تحقیق عملکرد سه روش شبکه­های عصبی پرسپترون چندلایه (MLP)، شبکه­های عصبی آبشاری (Cascade-NN) و برنامه­ریزی بیان ژن (GEP) در برآورد منحنی مشخصه آب خاک مورد ارزیابی و مقایسه قرار گرفت. در این پژوهش اطلاعات اندازه­گیری شده مربوط به تعداد ۱۰۸ نمونه خاک مناطق جنگلی شامل درصد توزیع اندازه ذرات خاک، مقادیر رطوبت در هفت مکش مختلف و جرم مخصوص ظاهری مورد استفاده قرار گرفت. سه سناریو شامل ترکیب­های مختلف از داده­های ورودی تعیین و مدل­های مذکور برای هر کدام اجرا شد. مقایسه مقادیر پیش­بینی شده و مشاهداتی رطوبت خاک نشان دهنده عملکرد قابل قبول هر سه مدل بود؛ برای مرحله آزمون مقادیر R2 برای بهترین ساختار در سه روش شبکه­های عصبی MLP، Cascade-NN و GEP به ترتیب ۹۵/۰، ۹۶/۰ و ۹۳/۰ و مقادیر RMSE نیز به ترتیب ۷۴/۳، ۲۵/۳ و ۱۰/۴ درصد بود. مقایسه نتایج سناریوهای مختلف داده ورودی نیز نشان داد، دقت و اختلاف بین نتایج مدل­ها در سناریوی اول کم بود ولی در سناریوی دوم و سوم به ترتیب با اضافه شدن پارامترهای تخلخل و رطوبت نقطه ظرفیت زراعی به داده­های ورودی، دقت و از سوی دیگر اختلاف بین نتایج مدل­ها بیشتر شد. در نهایت شبکه­های عصبی آبشاری با استفاده از تمام داده­های فیزیکی اشاره شده به عنوان گزینه مطلوب شناخته شد.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of the Performance of Artificial Neural Networks and Gene Expression Programming in Estimating the Forest Soil Water Characteristic Curve

نویسندگان [English]

  • Mohammad Mahdi Jafari 1
  • Hassan Ojaghlou 2
  • Masoud Karbasi 3
1 Department of Water Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
2 Assistant professor-Department of Water Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
3 Associate professor - Department of Water Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
چکیده [English]

One of the most important and practical physical parameters in studies of soil water flow is Soil Water Characteristic Curve (SWCC). Measuring the soil moisture characteristic curve through the direct method is expensive and time-consuming. For this reason, a variety of indirect methods including intelligent models have been developed. In this study, the performance of three models included multilayer perceptron neural networks (MLP), cascade neural network (Cascade-NN) and gene expression programming (GEP) were evaluated and compared to estimate of SWCC. The measured data from 108 soil samples, including soil particle size distribution, soil moisture in different suctions and the bulk density were used. In all models, three different input data combinations were used. Comparison of predicted and observed values of soil moisture showed acceptable performance of all three models, however, the Cascade-NN neural network model was relatively superior. The R2 values of test phase for the best structure of the neural networks (MLP), neural networks (Cascade-NN) and gene expression programming (GEP) were 0.95, 0.96 and 0.93, respectively, and the RMSE values were 3.74, 3.25 and 4.10 %, respectively. Comparison of the results of different input data scenarios indicated the low accuracy and difference between the results of the models in the first scenario, but adding the parameters of porosity and moisture at field capacity point to the input data in the second and third scenarios, increased the accuracy and difference between the results achieved by the models. Finally, it can be emphasized that the cascade-NN model was introduced as the superior option, using all the mentioned physical data.

کلیدواژه‌ها [English]

  • Intelligent Models
  • Prediction
  • Soil moisture
  • suction
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