本文選題：土壤水分特征曲線　切入點(diǎn)：Van-Genuechten模型參數(shù)　出處：《太原理工大學(xué)》2017年碩士論文

【摘要】：土壤水分特征曲線是表征土壤水吸力(基質(zhì)勢(shì))與土壤含水率關(guān)系的曲線,表示土壤水能量和數(shù)量之間的關(guān)系。作為反映基本土壤水力特性的指標(biāo),土壤水分特征曲線對(duì)研究土壤水分的運(yùn)移與滯留具有重要作用,尤其對(duì)農(nóng)業(yè)灌溉管理具有重要意義。但人們,尤其是基層水利工作者直接測(cè)定土壤水分特征曲線存在費(fèi)時(shí)費(fèi)力,技術(shù)不達(dá),土壤類(lèi)型空間變異性大等問(wèn)題,故本研究從土壤傳輸函數(shù)的理論出發(fā),通過(guò)測(cè)定較易的土壤基本理化參數(shù)來(lái)預(yù)測(cè)得到較難獲取的土壤水分特征曲線模型參數(shù),實(shí)現(xiàn)對(duì)土壤水分特征曲線的預(yù)測(cè)。因此實(shí)現(xiàn)對(duì)土壤水分特征曲線Van-Genuechten模型參數(shù)的預(yù)測(cè)為本研究的核心內(nèi)容。本研究基于黃土高原區(qū)土壤水分特征曲線系列室內(nèi)試驗(yàn),選取精度較高、適用廣泛的Van-Genuechten模型作為擬合模型,運(yùn)用RETC軟件和MATLAB程序進(jìn)行擬合,擬合Van-Genuechten模型參數(shù)α與n,并同步測(cè)試與Van-Genuechten模型參數(shù)α與n相對(duì)應(yīng)的土壤理化參數(shù),構(gòu)建由Van-Genuechten模型參數(shù)和土壤理化參數(shù)組成的數(shù)據(jù)樣本。基于歷時(shí)一年以上的試驗(yàn)過(guò)程所建立的108組數(shù)據(jù)樣本,在分別分析每個(gè)基本理化參數(shù)與Van-Genuechten模型參數(shù)α與n之間的定性定量關(guān)系的基礎(chǔ)上,建立了以土壤基本理化參數(shù)作為輸入變量,Van-Genuechten模型參數(shù)α與n作為輸出變量的非線性模型預(yù)報(bào)、BP神經(jīng)網(wǎng)絡(luò)模型預(yù)報(bào)和支持向量機(jī)模型預(yù)報(bào)研究。主要結(jié)果與結(jié)論如下:(1)影響土壤水分特征曲線的主要基本理化因素有土壤質(zhì)地、干容重、有機(jī)質(zhì)含量和鹽分含量。通過(guò)單因素分析,分別確定了Van-Genuechten模型參數(shù)α與n與各個(gè)土壤基本理化參數(shù)的函數(shù)關(guān)系。參數(shù)α:與質(zhì)地呈線性關(guān)系,與干容重呈對(duì)數(shù)關(guān)系,與有機(jī)質(zhì)含量呈指數(shù)關(guān)系,與鹽分含量呈對(duì)數(shù)關(guān)系;參數(shù)n:與質(zhì)地呈線性關(guān)系,與干容重呈線性關(guān)系,與有機(jī)質(zhì)含量呈對(duì)數(shù)關(guān)系,與鹽分含量呈對(duì)數(shù)關(guān)系;(2)通過(guò)常規(guī)土壤理化參數(shù)對(duì)Van-Genuechten模型參數(shù)進(jìn)行預(yù)測(cè)是可行的,所建立的Van-Genuechten模型參數(shù)α與n的非線性、BP和支持向量機(jī)預(yù)報(bào)模型誤差都在可接受范圍,都是可行的。以Van-Genuechten模型參數(shù)α與n為輸出變量的非線性、BP和支持向量機(jī)預(yù)報(bào)都以土壤質(zhì)地、干容重、有機(jī)質(zhì)含量和鹽分含量為輸入變量,都基于統(tǒng)一的數(shù)據(jù)樣本,包括100組建模樣本和8組驗(yàn)證樣本。非線性預(yù)報(bào)模型:參數(shù)α與n建模樣本的平均相對(duì)誤差分別是10.23%和7.20%,驗(yàn)證樣本的平均相對(duì)誤差分別是7.65%和5.77%;BP神經(jīng)網(wǎng)絡(luò)預(yù)報(bào)模型:參數(shù)α與n建模樣本的平均相對(duì)誤差分別是1.52%和0.67%,驗(yàn)證樣本的平均相對(duì)誤差分別是1.01%和0.28%;支持向量機(jī)預(yù)報(bào)模型:參數(shù)α與n建模樣本的平均相對(duì)誤差分別是8.32%和7.48%,驗(yàn)證樣本的平均相對(duì)誤差分別是6.99%和4.54%。這三種預(yù)報(bào)模型都具有本身特點(diǎn)與規(guī)律,本文研究中均取得較為理想的預(yù)報(bào)效果,較好的建立了土壤水分特征曲線模型參數(shù)的土壤傳輸函數(shù)。(3)非線性預(yù)報(bào)模型或許是首選模型。通過(guò)對(duì)比非線性、BP和支持向量機(jī)預(yù)報(bào)模型結(jié)果,發(fā)現(xiàn)非線性預(yù)報(bào)模型形式簡(jiǎn)單,物理意義十分明確,但精度稍有遜色;BP預(yù)報(bào)模型精度很高,可以精準(zhǔn)地實(shí)現(xiàn)Van-Genuechten模型參數(shù)預(yù)測(cè),但模型形式復(fù)雜,其程序?qū)κ褂萌藛T要求高;作為首次引入土壤水分特征曲線模型參數(shù)預(yù)測(cè)的支持向量機(jī)模型,其精度符合參數(shù)預(yù)報(bào)的要求,且具有理想的預(yù)報(bào)效果,但支持向量機(jī)模型理論繁復(fù),深度較高,在實(shí)踐應(yīng)用上可能要遜色于非線性和BP預(yù)報(bào)模型。因此,綜合推薦使用非線性和BP預(yù)報(bào)模型,非線性預(yù)報(bào)模型或許是首選模型,具體使用何種預(yù)報(bào)模型,應(yīng)根據(jù)不同的使用境況合理選取。本文實(shí)現(xiàn)了土壤水分特征曲線模型參數(shù)的非線性、BP、支持向量機(jī)三種模型的預(yù)測(cè),但在輸入變量的選取方面可能存在不全面性。在今后的研究中還應(yīng)進(jìn)行進(jìn)一步的探討,以進(jìn)一步提高預(yù)測(cè)精度。此外,對(duì)于支持向量機(jī)模型的研究應(yīng)繼續(xù)深入,以進(jìn)一步提高預(yù)報(bào)精度。
[Abstract]:The soil water characteristic curve is the characterization of soil water suction (matrix potential) and the relationship between soil moisture curve, the relationship between soil water energy and quantity. As a reflection of the hydraulic characteristics of basic soil index, migration and hysteresis of soil water characteristic curve of soil moisture retention plays an important role, especially has important significance to the agricultural irrigation management. But people, especially the grassroots water conservancy workers direct determination of soil water characteristic curve are time-consuming, the technology of soil spatial variability and other issues, so this study from the soil transfer function theory, through the determination of easily soil basic physical and chemical parameters to predict the parameters of soil water characteristic curve model difficult to obtain, to predict the soil water characteristic curve. It is used to predict the parameters of soil water characteristic curve of Van-Genuechten model for the research The core content of the research. This research is based on the Loess Plateau soil water characteristic curves of a series of indoor tests, selection of high precision, wide application of the Van-Genuechten model as the fitting model were fitted using the RETC software and the MATLAB program, n alpha and Van-Genuechten model parameters, physicochemical parameters a and n and synchronous test and Van-Genuechten model parameters corresponding to the soil sample data constructed by Van-Genuechten model parameters and soil physicochemical parameters. 108 samples of test process lasted more than one year on, respectively based on qualitative and quantitative analysis of relationship between the alpha and n parameters and Van-Genuechten model parameters of each basic physicochemical on the established soil physicochemical parameters as input variables, the parameters of Van-Genuechten model and N model as the alpha prediction of nonlinear output variables, BP neural network prediction model Support vector machine model and prediction research. The main results and conclusions are as follows: (1) the main factors affecting the soil water characteristic curve of the basic physical and chemical soil texture, bulk density, organic matter content and salt content. Through single factor analysis, the function relationship between Van-Genuechten model basic physicochemical parameters parameters alpha and N with various soil the parameters are determined. Alpha: a linear relationship with the logarithm relationship with the texture, dry bulk density, and exponential relationship between the content of organic matter, the logarithm relationship with salt content; there is a linear relationship between the parameters of n: and texture, showed a linear relationship with the logarithm relationship with dry bulk density, organic matter content, the logarithm relationship with salt content; (2) by conventional soil physicochemical parameters to predict the parameters of the Van-Genuechten model is feasible, nonlinear alpha and n parameters of Van-Genuechten model established by BP, and the support vector machine model prediction errors are acceptable The range is feasible. The alpha and n parameters of Van-Genuechten model for nonlinear output variables, BP and support vector machine prediction with soil texture, dry bulk density, organic matter content and salt content as the input variables are based on the unified data samples, including 100 form appearance and 8 groups of nonlinear test samples. The average forecast model parameters a and N modeling sample relative error are 10.23% and 7.20%, the average relative error of testing samples were 7.65% and 5.77%; BP neural network prediction model, the average relative error of parameter modeling and N samples respectively is 1.52% and 0.67%, the average relative error of testing samples were 1.01% and 0.28%; support vector machine prediction model: the average parameter alpha and N modeling samples with the relative error are 8.32% and 7.48%, the average relative error of testing samples were 6.99% and 4.54%. of the three prediction model has the Personal characteristics and rules, prediction of ideal effect were obtained in this study, the soil transfer function well established parameter model of soil water characteristic curve. (3) the nonlinear prediction model may be the preferred model. By comparing the nonlinear BP and SVM prediction model results, found that the form of nonlinear prediction model is simple, physical the meaning is clear, but the precision is slightly inferior; the accuracy of BP prediction model is very high, can accurately realize the Van-Genuechten model parameter prediction model, but its complex forms, procedures on the use of personnel requirements; as for the first time into the model of soil water characteristic curve parameter prediction model of support vector machine, its accuracy meets the requirement of prediction parameters. The forecast effect and has the ideal, but the model of support vector machine theory complicated, higher depth may be inferior to BP and nonlinear prediction model in practice. This comprehensive, recommended the use of nonlinear and BP prediction model, nonlinear prediction model may be the preferred model, what the specific use of prediction model should be reasonably selected according to the different use condition. This paper realizes nonlinear, parameter model of soil water characteristic curve of BP prediction, support vector machine model three, but there may be not comprehensive in the selection of input variables. In the future research should be conducted to further explore, to further improve the prediction accuracy. In addition, for the study of support vector machine model should be further, to further improve the prediction accuracy.

【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S152.7

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