نشریه علمی پژوهشی مهندسی آبیاری و آب ایران

نشریه علمی پژوهشی مهندسی آبیاری و آب ایران

تخمین رطوبت، نرخ نیترات‌زایی و هیدرولیز اوره در خاک با به کارگیری روش‌های هوشمند ترکیبی-موجک

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی ساری
2 دانشگاه تهپژوهشگر پسادکتری، مؤسسه ملی تحقیقات علمی (INRS)، مرکز آب زمین محیط‌زیست، کبک، کانادا. ران
10.22125/iwe.2023.410046.1739
چکیده
بررسی تأثیر شیوه‌های مدیریت زراعی بر میزان نیترات و اوره در خاک از اهمیت ویژه‌ای برخودار است. هدف از این مطالعه، مدلسازی و بررسی ارتباط متقابل متغیرهای هیدرولیکی، واکنشی و جذب املاح عمق‌های مختلف خاک جمع‌آوری شده در سال (1400-1399) از مزرعه پایلوت شالیزاری واقع در دانشگاه علوم کشاورزی و منابع طبیعی ساری، استان مازندران، به مساحت 6/0 هکتار، است. در این مطالعه، حجم رطوبت باقیمانده در خاک (θr)، نرخ نیترات‌زایی (kn) و نرخ هیدرولیز اوره (kh) به‌ترتیب بر اساس چهار، چهار و دو سناریو تعریف شده، با به کارگیری مدل‌های شبکه عصبی مصنوعی-موجک (WANN)، سامانه استنتاجی فازی-عصبی-موجک (WANFIS) و برنامه‌ریزی بیان ژن-موجک (WGEP)، مدلسازی شد. نتایج نشان داد که درصد بهبود عملکرد مدل‌های WGEP نسبت به WANFIS و مدل WANFIS نسبت به WANN با در نظر گرفتن شاخص ارزیابی RMSE به‌ترتیب برای سه متغیرθr ، kn و kh (96/16، 87/41)، (72/85، 00/1) و (37/20، 27/3) به دست آمد. مدل WGEP دارای بیشترین مقدار R و کم‌ترین مقادیر RMSE و MAE جهت تخمین متغیرهایθr ، kn و kh می‌باشد. نتایج نشان داد که متغیرهای هیدرولیکی، واکنشی و جذب املاح خاک می‌تواند متأثر از شرایط اقلیمی منطقه، باشد. بنابراین، ارائه مدل‌های کاربردی هوشمند جهت تخمین متغیرهای نیترات و اوره در خاک، می‌تواند مدیران و کشاورزان را در مدیریت صحیح منابع آب و خاک و مصرف بهینه کود نیتروژن با صرف هزینه و زمان کم‌تر، یاری دهد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Estimation of Moisture, Nitrification Rate and Urea Hydrolysis in Soil Using Intelligent Combined-Wavelet Methods

نویسندگان English

Ali Shahnazari 1
sarvin zamanzad ghavidel 2
1 Associate Professor, Water Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran. Email
2 Postdoctoral Researcher, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, Quebec City, Canada, E-mail: sarvin.zamanzad-ghavidel@inrs.ca
چکیده English

Investigating the effect of agricultural management methods on the amount of nitrate and urea in the soil has special importance. The purpose of this study is to model and investigate the interrelation of hydraulic, reactivity, and solute absorption variables of different soil depths collected in the year of (2020-2021) from the pilot rice farm located in the Sari Agricultural Sciences and Natural Resources University, Mazandaran province, with an area of 0.6 hectares. In this study, the residual moisture of the soil (θr), nitrification rate (kn), and urea hydrolysis rate (kh) variables were modeled based on four, four, and two defined scenarios, respectively, using Wavelet-Artificial Neural Network (WANN), Wavelet- Artificial Neural-Fuzzy Inference System (WANFIS), and Wavelet-Gene Expression Programming (WGEP) models. The results showed that the performance improvement percentage of WGEP models compared to WANFIS and WANFIS model compared to WANN considering the RMSE evaluation index were obtained (16.96, 41.87), (85.72, 1.00), and (20.37, 3.27) for three variables of θr, kn, and kh, respectively. the volumetric residual moisture in the soil, and the urea hydrolysis rate variable is also highly dependent on the residual moisture in the soil. Also, the results showed that the hydraulic variables, reactivity and absorption of soil solutes can be affected by the climatic conditions of the region. Therefore, providing intelligent applicable models to estimate nitrate and urea variables in soil can help managers and farmers in proper management of water and soil resources and optimal use of nitrogen fertilizer with less time and cost.

کلیدواژه‌ها English

Artificial Intelligence
Humidity
Soil
Solutes
Wavelet
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