本文關鍵詞： 紫色土 飽和導水率 影響因素 多元非線性回歸 BP神經網絡　出處：《西南大學》2017年碩士論文　論文類型：學位論文

【摘要】：紫色土廣泛分布于我國西南和南方山地丘陵區(qū),川渝丘陵區(qū)及低山區(qū)分布最廣。三峽庫區(qū)中紫色土耕地面積占70%以上,并且?guī)靺^(qū)內耕地以坡耕地為主。紫色因其特殊的成土過程導致抗蝕性差,而且土壤的質地松軟,紫色土分布的地區(qū)也是水土流失高發(fā)區(qū)。同時,三峽庫區(qū)因地勢起伏變化大,雖然年降雨量大但時空分布不均,致使庫區(qū)內土壤養(yǎng)分和水土流失異常嚴重。因此,本文以三峽庫區(qū)腹地的云陽縣、奉節(jié)縣、開州區(qū)、萬州區(qū)、梁平區(qū)、忠縣和豐都縣7個渝東北區(qū)縣為研究區(qū)域,在研究區(qū)內設置土壤樣品采樣點,挖取土壤剖面,分別在0-10 cm、10-20 cm及20-30 cm處采集土樣,通過試驗測定飽和導水率及相關的土壤理化性質,分析研究區(qū)飽和導水率及土壤理化性質的空間分布特性,以及各土壤理化性質參數(shù)對土壤飽和導水率的影響,并建立不同土壤層次飽和導水率與土壤理化性質參數(shù)之間的定量模型。研究傳統(tǒng)傳遞函數(shù)模型在本研究區(qū)的適應性,并應用多元非線性回歸法和BP神經網絡技術,分別構建不同土壤層次的土壤飽和導水率傳遞函數(shù)模型,為山地丘陵區(qū)土壤中物質的運移、區(qū)域耗水規(guī)律、水土保持治理、農業(yè)面源污染治理等提供了一定的方法參考和決策支持,主要結論如下:(1)隨著土壤深度增加,土壤飽和導水率、飽和含水量和土壤有機質均呈現(xiàn)出逐漸遞減的規(guī)律,而土壤容重卻隨土壤深度增加而增加,不同深度的土壤顆粒均以粉粒為主,其次為砂粒、粘粒,土壤顆粒以0.05~0.002 mm居多。各層次土壤的飽和導水率與有機質、飽和含水量聯(lián)系較為緊密,而與土壤容重和土壤質地的相關性不顯著。(2)三個土層的飽和導水率均隨有機質含量的增加而增加,它們之間的相關性較高,并且飽和導水率與有機質含量呈指數(shù)函數(shù)關系變化;三個土層的飽和導水率隨土壤容重的增大在減小,并呈指數(shù)函數(shù)關系,且土壤容重對飽和導水率的影響不顯著;淺層土壤(0-10 cm、10-20 cm)的飽和導水率與飽和含水量均呈現(xiàn)顯著正相關,并且呈二次曲線關系,而20-30 cm土層飽和導水率與飽和含水量的相關性不顯著;研究區(qū)飽和導水率受土壤質地的影響較小,僅20-30cm土層粘粒含量對飽和導水率有顯著負相關關系,其它土層的相關性不顯著。(3)運用Campell、Cosby、Saxton、Wosten1997、Wosten1999和Puckett 6種傳遞函數(shù)對不同深度土壤飽和導水率進行估算的結果都不理想,表明前人建立的傳遞函數(shù)模型已經不能適用于本研究區(qū)飽和導水率的預測。(4)采用多元非線性回歸方法建立的不同深度土壤的飽和導水率傳遞函數(shù)模型估算效果較好,模型的預測值與實測值基本相當,模型的擬合效果能夠滿足估算的要求,表明利用有機質、飽和含水量、土壤質地等通過多元非線性傳遞函數(shù)模型進行不同土壤層次的飽和導水率的預報是可行的。(5)利用MATLAB工作平臺建立的0-10 cm、10-20 cm和20-30 cm三個土壤層次的飽和導水率BP神經網絡傳遞模型,其飽和導水率的預測值與實測值誤差最小,是本文研究中預測精度最高的模型。(6)在本文的研究中,構建的多元非線性回歸模型和BP神經網絡模型預測土壤飽和導水率的精度均較高,并且BP神經網絡傳遞模型的誤差更小,但其構建過程繁雜,不如多元非線性回歸模型簡單易于操作,在實際生產應用中,應綜合考慮實際情況合理選擇預報模型。
[Abstract]:Purple soil is widely distributed in Southwest China and southern hilly area, Sichuan Hilly and low mountainous areas. The most widely distributed in purple soil area of cultivated land in Three Gorges area accounted for more than 70%, and the area of cultivated land in purple slope land. Because of the special soil processes lead to poor corrosion resistance, and the soil texture is soft, the purple soil distribution area and soil erosion in Three Gorges Reservoir area. At the same time, because of the terrain changes, although the annual rainfall is large but uneven distribution, the area of soil nutrient and soil erosion is very serious. Therefore, the hinterland of the Three Gorges Reservoir Area in Yunyang County, Fengjie County, the State District, Wanzhou District, Liangping area Fengdu County, Zhongxian and 7 northeast of Chongqing county as the study area in the study area is arranged in the soil sample sampling points, digging soil profile, respectively at 0-10 cm, 10-20 cm and 20-30 cm soil samples were collected, through test determination of saturated water The physicochemical properties of soil and the related rate, analysis of the spatial distribution characteristics of saturated hydraulic conductivity and soil physical and chemical properties, and the physicochemical properties of soil parameters on the effect of soil saturated hydraulic conductivity, and the establishment of different soil layers of saturated hydraulic conductivity and soil physicochemical properties of the quantitative model between the parameters of traditional. Transfer function model in the study area and adaptability, using multiple nonlinear regression method and BP neural network technology, were constructed in different soil layers and soil saturated hydraulic conductivity transfer function model for migration of substances in the soil hilly area in the region, water consumption, soil and water conservation, provides some reference and decision making method support the agricultural non-point source pollution control, the main conclusions are as follows: (1) with the increase of soil depth, soil saturated hydraulic conductivity, saturated water content and soil organic matter showed the law of diminishing, and soil But the soil bulk density increased with soil depth, soil particles with different depth were dominated by silt, followed by sand, clay, soil particles with 0.05~0.002 mm. The saturated hydraulic conductivity and soil organic matter, saturated water content closely, no significant correlation between volume and weight and soil texture and soil. (2) the three soil saturated hydraulic conductivity increased with the content of organic matter, high correlation between them, and the saturated hydraulic conductivity and organic matter content changed in the relation of exponential function; three soil saturated hydraulic conductivity with the increase in soil bulk density decreases, and the exponential function the relationship, and the effect of soil bulk density on saturated hydraulic conductivity is not significant; the shallow soil (0-10 cm, 10-20 cm) of the saturated hydraulic conductivity and saturated water content showed a significant positive correlation, and a two curve, and 20-30 cm soil saturated hydraulic conductivity and No significant correlation between the saturated water; the study area saturated hydraulic conductivity is less affected by soil texture, soil clay content of only 20-30cm significant negative correlation between saturated hydraulic conductivity, no significant correlation between other soil layers. (3) the use of Campell, Cosby, Saxton, Wosten1997, Wosten1999 and Puckett 6 kinds of transfer function different depth of soil saturated hydraulic conductivity were estimated. The results are not ideal, that established the transfer function model is not suitable for the study area. The prediction of water saturated rate (4) using multiple nonlinear regression method to establish the different depth of soil saturated hydraulic conductivity transfer function estimation model better, prediction model the value and the measured value is the result of fitting of the model can meet the requirements that estimate, using organic matter, saturated water content, soil texture through multivariate nonlinear transfer function model. For different levels of soil saturated hydraulic conductivity prediction is feasible. (5) using the MATLAB platform built 0-10 cm, 10-20 cm and 20-30 cm three level of soil saturated hydraulic conductivity BP neural network transfer model, the saturated water values of the minimum error rate prediction and the measured value, the accuracy of the model is the highest prediction in this study. (6) in this study, the prediction of soil saturated hydraulic conductivity of the accuracy of multivariate nonlinear regression model and BP neural network model, BP neural network and error transfer model, but its construction process is complicated, as the multivariate nonlinear regression model is simple and easy to operate in. In practical application, should consider the reasonable selection of prediction model of the actual situation.

【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S152.7

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