برآورد حد قابل تحمل هدررفت خاک با استفاده از روش‏های رگرسیون خطی و درختی

نوع مقاله : مقالات پژوهشی

نویسندگان

دانشگاه شیراز

چکیده

برای انجام فعالیت­های حفاظتی در برابر فرسایش خاک دانستن میزان حد قابل تحمل هدررفت خاک بسیار ضروری است. از این­رو، پژوهش حاضر با هدف تعیین میزان حد قابل تحمل هدررفت خاک به روش ضخامت و بر اساس معادله پرکاربرد اسکیدمور و توسعه توابع انتقالی رگرسیونی در برآورد این ویژگی در حوضه بالادست سد درودزن انجام شد. برای این منظور تعداد 60 نیم‏رخ خاک با دستگاه بیل مکانیکی حفر و علاوه بر اندازه­گیری عمق خاک، برخی از ویژگی­های فیزیکی و شیمیایی خاک سطحی (0 تا 30 سانتی‏متر) نیز در آزمایشگاه و صحرا اندازه­گیری شد. از روش رگرسیونی خطی چندگانه و رگرسیون درختی برای توسعه توابع انتقالی استفاده شد. نتایج نشان داد مقدار حد قابل تحمل هدررفت خاک با استفاده از روش اسکیدمور با میانگین 04/1 تن در هکتار در سال از حداقل 29/0 تا حداکثر 25/2 تن در هکتار در سال متغیر بود. ماده آلی خاک با داشتن بیشترین ضریب استاندارد شده (64/0=Beta) و بیش‏ترین همبستگی (77/0-) با حد قابل تحمل هدررفت خاک مهم‏ترین ویژگی در برآورد این شاخص خاک بود. بر اساس آماره‏های ارزیابی، روش رگرسیون درختی با میانگین برآوردی حد قابل تحمل هدررفت خاک 08/1 تن در هکتار در سال و داشتن ضریب تعیین بالاتر در هر دو مجموعه داده وا‌سنجی (96/0=R2) و اعتبارسنجی (78/0=R2) و‏ مقدار خطای کمتر در داده واسنجی (26/0 تن در هکتار در سال=RMSE) و اعتبارسنجی (13/0 تن در هکتار در سال =RMSE) کارآیی بیش­تری در مقایسه با روش رگرسیونی چندگانه چندگانه با میانگین برآوردی حد قابل تحمل 13/1 تن در هکتار در سال داشت.

کلیدواژه‌ها


عنوان مقاله [English]

Estimating the Soil Loss Tolerable (T-value) Using Linear and Regression Tree Methods

نویسندگان [English]

  • Y. Ostovari
  • S.A.A. Mousavi
  • H. Mozaffari
Shiraz University
چکیده [English]

Introduction: Soil erosion is one of the most important and serious threats to food security and as a consequence of human life. In order to perform soil protection activities against soil erosion, knowledge about the amount of soil loss tolerable is very important. In fact, the soil loss tolerable is the potential for soil erosion, loss of productivity and lost production, and the final criterion for controlling soil erosion and degradation of land. Soil thickness methods, particularly Skidmore equation, based on their ability to estimate the tolerable amount of soil loss have been widely used. In the mathematical function developed by Skidmore based on soil thickness, the soil loss tolerable is calculated based on the soil's current depth, the lowest and maximum soil depth for sustained growth of crops, and the upper limit of tolerable erosion in accordance with the environment. Since the determination of soil loss tolerance by soil thickness method and the Skidmore equation requires time, cost and energy, the researchers have tried to estimate the soil tolerance is supported by regression methods using pedotransfer functions and easily available soil properties. Therefore, the present study was carried out with the aims of determining the tolerable tolerance of soil loss by thickness method and the development of regression pedotransfer functions for estimating this property in the upstream of the dam.
Materials and Methods: The study is place on Kamfiruz Watershed with an area of 422 km2, an average annual precipitation of 443 mm and an average annual temperature of 14 °C. It is closed to the Dorudzan Dam sub-basins and is considered as one of the five parts of Marvdasht plain in Fars province. For this work, 60 soil profiles were excavated by excavating machine. In addition to measuring the depth of soil, some physico-chemical soil properties were measured from the surface layer (0-30 cm) including; soil texture, organic matter, salinity, percentage calcium carbonate, mean weight diameter in the laboratory and filed. In order to develop regression models for estimating the tolerable soil loss, information from 60 soil profiles was divided into two data-sets. One set of the data with 42 samples (70% of whole samples) was used for developing the models and another set of the data with 18 soil samples (30% of whole samples) was used for validation. Multiple linear regression was used to develop the linear models. The same soil properties used in the multiple regression method were considered as inputs in the tree regression method to estimate the tolerable amount of loss.
Results and Discussion: The results showed that the minimum and maximum Z1 parameters (the lowest soil depth for stable growth of crops in the study area) were considered as 0.25 and 0.51 m based on the current depth of soil. Organic matter of the soils with the highest standardized coefficient (Beta = 0.44) and the highest correlation (-0.77) with soil loss tolerance was the most important soil properties for estimating the soil loss tolerance. In the regression model, only the coefficients of four characteristics of permeability, soil aggregate stability, pH and organic matter appeared among the soil grazing characteristics and entered into the model. Based on the evaluation statistic, tree regression method with the highest determination coefficient in both calibration data sets (R2 = 0.96) and validation (R2 = 0.78) and the lowest error value in the validation data (RMSE= 0.29 ton ha-1 year-1) and validation (RMSE = 0.125 ton ha-1 year-1) were more efficient than the multiple regression method in estimating the tolerable soil loss.
Conclusion: Soil loss tolerance was estimated using regression methods (multiple linear regression and regression tree) in Doroudzan Watershed, Fars province. The soil loss tolerable determined using Skidmore method, was 1.04 tons per hectare per year ranging from 0.29 to 2.25 ton ha-1 year-1. The soils of this area are slightly deep and their depth varies from 0.4 m in the marginal areas in the upstream parts of the catchment area of the dam and the slope of mountain up to 2 meters in the center of the plain with agricultural lands uses. In general, the tree regression method had a better performance than linear regression method for estimating the soil loss tolerance based on the statistical indices.

کلیدواژه‌ها [English]

  • Calcareous soils
  • Erosion
  • Multiple regression
  • Skidmore method
  • Soil erodibility
  • Transfer functions
  • Tree regression
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