تاثیر متغیر‌های ورودی بر قابلیت برآورد مقدار رطوبت خاک از طریق مدل های مختلف منحنی نگهداشت آب خاک

نوع مقاله: مقاله پژوهشی

نویسندگان

1 - دانشجوی سابق کارشناسی ارشد، گروه خاکشناسی دانشکده کشاورزی دانشگاه بوعلی سینا

2 استادیار گروه خاکشناسی دانشکده کشاورزی دانشگاه بوعلی سینا

3 دانشیار گروه آبیاری دانشکده کشاورزی دانشگاه بوعلی سینا

چکیده

منحنی نگهداشت آب خاک یکی از ویژگی­های اصلی خاک است و کاربردهای فراوانی دارد. اندازه­گیری مستقیم این منحنی بسیار زمان بر و پر‌هزینه است. بنابراین، این منحنی اغلب با استفاده از روش‌های غیرمستقیم از جمله توابع انتقالی خاک برآورد می­گردد. مدل‌های پرشماری برای کمی­سازی این منحنی ارائه شده است و همچنین توابع انتقالی فراوانی برای پیش‌بینی این منحنی ایجاد گردیده است. با این وجود قابلیت برآورد مقدار رطوبت خاک با استفاده از سطوح متفاوت متغیر‌های ورودی توابع انتقالی از طریق مدل‌های متفاوت منحنی نگهداشت آب خاک با استفاده از شبکه‌های عصبی مصنوعی مورد بررسی قرار نگرفته است. در این پژوهش 75 نمونه خاک از استان گیلان جمع‌آوری و آزمایش­های پایه روی آن‌ها انجام شد. آب خاک در 12 مکش (صفر، 1 ، 2، 5 ،10 ، 25 ، 50 ، 100 ، 200 ، 500 ، 1000 و 1500 کیلوپاسکال) اندازه­گیری و ده مدل بر آن‌ها برازش داده شد. معادله پریر بر داده‌های توزیع اندازه ذرات و خاکدانه­ها برازش شده و پارامترهای فراکتالی مربوطه به‌دست آمدند. پارامترهای فراکتالی ذرات و خاکدانه­ها هر کدام در مراحل جداگانه به همراه رس، شن و جرم مخصوص ظاهری برای برآورد رطوبت از طریق مدل­های مختلف استفاده شدند. در بین مدل­های مورد مطالعه مدل سکی، فرمی و گاردنر با دقت بالاتری در مقایسه با سایر مدل­های منحنی نگهداشت آب خاک برآورد شدند. بر خلاف انتظار دقت برآورد مدل­های دکستر و دورنر پایین بود. نتایج تجزیه کلاستر نشان داد که مدل­های دورنر و دکستر هر کدام در یک گروه جداگانه قرار گرفتند. مشاهده شد که تغییر برآورد‌گرها باعث تغییر در دقت برآورد رطوبت توسط مدل‌ها شده و جایگاه و رتبه‌بندی مدل‌ها در جداول را تغییر داد. در سطح اول مدل­های فرمی و دکستر به ترتیب بهترین و ضعیف­ترین دقت برآورد را داشتند. ولی درسطح دوم برآورد­گرها مدل گاردنر و تانی به‌ترتیب بهترین و ضعیف­ترین دقت برآورد را نشان دادند. 

کلیدواژه‌ها


عنوان مقاله [English]

Effect of Input Variables on Predictability of Soil Water Content through Different Soil Water Retention Curve Models

نویسندگان [English]

  • Eisa Ebrahimi 1
  • Hosein Bayat 2
  • Hamid Zare Abyaneh 3
1 Former MSc Student of Soil Science, Department of Soil Science, Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran.
2 Assistant Professor, Department of Soil Science, Faculty of Agriculture, Bu-Ali Sina University, Hamadan, Iran
3 Associate Professor, Department of Irrigation, Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran
چکیده [English]

Soil water retention curve (SWRC) is one of the main soil characteristics with many applications. Its direct measurement is costly and time-consuming. Therefore, it is often predicted through indirect methods such as pedotransfer functions (PTFs). Many models have been developed for quantitative description of SWRC and also a lot of PTFs has been developed for their estimation. However, predictability of soil water content through different SWRC models by using different input variables and artificial neural networks have not been investigated, so far. In this study, 75 soil samples were taken from Guilan province and basic soil properties have been measured. Water contents were measured at 12 matric potentials (0, 1, 2, 5, 10, 25, 50, 100, 200, 500, 1000 and 1500 kPa). Ten well known and frequently applied SWRC models were fitted to the measured data. The Perrier model was fitted on the particles and aggregates size distributions and fractal parameters were obtained. The fractal parameters of particles and aggregates size distributions along with clay, sand and bulk density were used to estimate water content in two input levels by different SWRC models. Results showed that the models of Seki, Fermi and Gardner were predicted more accurately, in comparison with other models. In spite of the expectation, the models of Dexter and Durner were not predicted accurately and according to the cluster analysis were classified in different groups. It was observed that the prediction capabilities of different models were changed and their arrangements were altered in the tables by changing input variables. Overall, Fermi and Dexter models had the highest and the least predictability with the first input levels, respectively. Gardner and Tani models had the highest and the least predictability with the second input levels, respectively.

کلیدواژه‌ها [English]

  • Cluster analysis
  • Fractal parameters
  • Soil water retention curve models
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