مقایسه شبکه‌های ‌عصبی ‌مصنوعی و توابع انتقالی رگرسیونی برای تخمین ظرفیت تبادل ‌کاتیونی خاک در شمال غرب ایران

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه خاکشناسی، دانشکده کشاورزی، دانشگاه زنجان، ایران

2 عضو هیئت علمی دانشگاه زنجان

3 گروه مهندسی آب، دانشکده کشاورزی دانشگاه زنجان ایران

چکیده

ظرفیت تبادل کاتیونی خاک میزان بار مثبتی است که در واحد جرم خاک قابل تبادل است. مدل‌سازی و تخمین ظرفیت تبادل کاتیونی شاخصی مفید از حاصلخیزی خاک می‌باشد. ارزیابی و طراحی سناریو‌های مختلف مدیریتی احتیاج به داشتن اطلاعات دقیق بانک اطلاعات خاک دارد. بدین منظور برای برآورد ظرفیت تبادل ‌کاتیونی خاک، 32 نیمرخ در دشت تبریز حفر گردید و جهت انجام آزمایش­های فیزیکی و شیمیایی مانند توزیع اندازه ذرات، کربن آلی، pH و ظرفیت تبادل کاتیونی خاک، 131 نمونه خاک از عمق­های مختلف جمع­آوری گردید. سپس 7 مدل رگرسیونی که بر­اساس مطالعات پیشین انتخاب شده بودند برای منطقه مورد مطالعه کالیبره شده و مورد ارزیابی قرار گرفتند. همچنین بر اساس ضرایب موجود در مدل­های رگرسیونی، 7 معماری متفاوت شبکه‌های عصبی مصنوعی جهت پیش‌بینی ظرفیت تبادل کاتیونی خاک طراحی گردید و نتایج حاصل از شبکه‌های عصبی مصنوعی و مدل‌های رگرسیونی چند متغیره با استفاده از پارامترهای ضریب همبستگی (R2)، جذر میانگین مربعات خطا (RMSE) و شیب بهترین معادله خط برازش داده شده بین نقاط پیش‌بینی و اندازه‌گیری شده (a) مورد ارزیابی قرار گرفت. نتایج نشان داد که معماری طراحی شده با شبکه عصبی مصنوعی با ضریب تبیین 86/0، RMSE  برابر با 14/2 و شیب خط برابر با 87/0دارای کارایی بالاتری بود که احتمالاً  به دلیل وجود روابط غیر خطی میان ویژگی های زودیافت خاک (متغیرهای مستقل) و ظرفیت تبادل کاتیونی (متغیر وابسته) بود.

کلیدواژه‌ها


عنوان مقاله [English]

Comparision of artificial neural network and regressionpedotransfer functions for istimation of soil cation exchange capacity in northwest of Iran

نویسندگان [English]

  • Ali Barikloo 1
  • jafar nikbakht 3
1 departement of soil science, faculty of ariculture, university of Zanjan, Iran
3 Department of water engineering , faculty of agriculture, university of zanjan, iran
چکیده [English]

 
Soil cation exchange capacity (CEC) is defined as the amount of positive charge that can be exchanged per mass of soil. Modeling and estimating of CEC is a useful index of soil fertility. Assessing and designing various management scenarios requires having accurate information regarding the soil data bank. In order to estimate the soil CEC, 32 profiles were dug in Tabriz plain, and 131 different samples were collected from different depths and physiochemical experiments such as particle size distribution, organic carbon, pH and CEC of soil samples were performed. Then using seven regression models that were selected based on previous studies, were calibrated and evaluated for the study area. Also seven different architectures of artificial neural networks were designed to predict the CEC of soil and the results of artificial neural networks and multivariate regression models were evaluated using correlation coefficient (R2), root mean square error (RMSE). Results revealed that artificial neural network with R2 = 0.86 and RMSE= 2.14 is better than regression based functions due to the existence of nonlinear relations between the easily available soil properties (independent variables) and the CEC (dependent variable).

کلیدواژه‌ها [English]

  • artificial neural network
  • Cation exchange capacity
  • dasht-e- Tabriz
  • regressionpedotransfer functions
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