برآورد هدایت هیدرولیکی اشباع خاک با استفاده از برنامه‌ریزی بیان ژنی و رگرسیون ریج (مطالعه موردی در استان آذربایجان شرقی)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیات علمی گروه علوم خاک دانشکده کشاورزی دانشگاه تبریز

2 دانشجوی سابق کارشناسی ارشد دانشگاه آزاد اسلامی واحد تبریز

3 دانشجوی دکتری، دانشگاه تبریز

چکیده

هدایت هیدرولیکی خاک از ویژگی­های مهم فیزیکی خاک است که در مدل‌سازی انتقال آب، املاح و آلاینده‌ها کاربرد دارد. اندازه‎گیری مستقیم هدایت هیدرولیکی خاک وقت‌گیر و پرهزینه بوده و گاهی اوقات به دلیل خطاهای آزمایشی و عدم یکنواختی خاک نتایج بدست آمده چندان قابل اعتماد نمی‌باشد. از طرف دیگر این پارامتر می‌تواند با استفاده از پارامتر‌های زودیافت خاک برآورد شود. هدف از این پژوهش، ارائه مدل‌های برنامه‌ریزی بیان ژنی و رگرسیون خطی بر اساس ویژگی‌های زودیافت هست. برای این منظور 160 نمونه خاک با خصوصیات متفاوت از مناطق مختلف استان آذربایجان‌شرقی برداشته شد. سپس برخی ویژگی‌های فیزیکی و شیمیایی آن‌ها همانند درصد شن، سیلت، رس و مواد آلی، جرم مخصوص ظاهری، pH و EC اندازه‌گیری شد. سپس داده‌ها بطور تصادفی به دو دسته داده‌های سری آموزش (75 درصد) و داده‌های سری آزمون (25 درصد) تقسیم شدند. شش تابع انتقالی (PTFs) با ترکیبی از عملگرهای ریاضی متفاوت توسط برنامه‌ریزی بیان ژنی طراحی شد. در نهایت یکی از توابع که از دقت و صحت بیشتری نسبت به بقیه برخوردار بود، انتخاب گردید. همچنین از رگرسیون ریج برای ارائه تابع انتقالی رگرسیونی استفاده شد. دقت و صحت توابع با معیارهای آماری R2، RMSE و MAE ارزیابی گردید. نتایج نشان داد که تابع انتقالی ارائه شده با روش برنامه‎ریزی بیان ژنی از دقت و صحت بیشتری نسبت به مدل رگرسیونی برخوردار می‌باشد. به‌طوری‌که مقادیر R2، RMSE (Cm h-1) و MAE (Cm h-1) برای تابع انتقالی برنامه‌ریزی بیان‎ژنی در داده‌های سری آموزش به‌ترتیب برابر 91/0، 82/1 و 23/1 و برای داده‌های سری اعتبار‌سنجی برابر 92/0، 27/2 و 59/1 بود. در حالی که مقادیر معیارهای فوق در مدل رگرسیونی، برای داده‌های سری آموزش به‎ترتیب برابر 70/0، 48/3 و 07/2 و برای داده‌های سری اعتبار‌سنجی برابر 76/0، 11/3 و 88/1 بود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimation of saturated hydraulic conductivity by using gene expression programming and ridge regression (A case study in East Azerbaijan province)

نویسندگان [English]

  • Abbas Ahmadi 1
  • Peyman Palizvan zand 2
  • Habib Palivan zand 3
1 University of Tabriz
2 Islamic Azad University of Tabriz
3 University of Tabriz
چکیده [English]

The hydraulic conductivity of soil is an important physical characteristic, which is used for water modeling and the modeling of solutes and pollutants transport. The direct measurement of soil hydraulic conductivity is a time-consuming and costly process, and due to experimental errors and soil heterogeneity, the results are sometimes unrealistic. Besides, it could be estimated by easily measurable soil properties. The purpose of this study is to develop genetic programming and linear regression models to estimate the saturated hydraulic conductivity of soil using readily available soil properties. With this purpose, 160 soil samples with different properties were gathered from various areas of East Azerbaijan province of Iran. Then some physical and chemical characteristics of soil such as the proportions of sand, silt and clay in the soil, and organic matter, bulk density, pH and EC values were measured. Then the data was divided into two different data sets, namely training (75% of data) and testing (25% of data) datasets. GeneXproTools 4.0 and Statistica softwares were used to calibrate Genetic programming and regression models, respectively. Six pedotranfer functions (PTFs) with a combination of different mathematical operators were designed by the genetic programming. Finally, one of the PTFs which was more accurate than the others was selected. Also, the ridge regression was utilized to develop regression PTFs. The accuracy and reliability of PTFs were determined by R2, RMSE, and MAE criteria. The research results showed that the genetic programming PTF (GP-PTF) is more accurate and reliable in comparison with the regression-PTF. In a way that the R2, RMSE (Cm h-1) and MAE (Cm h-1) of GP-PTF were 0.91, 1.82 and 1.23 for the training dataset, respectively, and for the test dataset, the values were 0.92, 2.27 and 1.59, respectively; whereas the values of the above mentioned criteria of regression-PTF for the training dataset were 0.70, 3.48 and 2.07, respectively, and for the test dataset were 0.76, 3.11 and 1.88, respectively.

کلیدواژه‌ها [English]

  • Genetic programming
  • modeling
  • Pedotransfer functions

مراجع

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تحقیقات آب و خاک ایران
دوره 48، شماره 5
بهمن و اسفند
بهمن 1396
صفحه 1087-1095

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