基于机器语言的岷江上游流域表层土壤氢氧稳定同位素空间分布模拟

为了探明利用机器学习方法对表层土壤氢氧稳定同位素组成(δ2H和δ18O)进行空间模拟的可行性,揭示δ2H和δ18O在岷江上游流域的大尺度分布变化规律,本研究在该区域共采集183个0～10 cm土层样本进行氢氧稳定同位素分析.通过变量筛选后,分别采用反向传播(BP)神经网络、随机森林(RF)和支持向量机(SVM)建立研究...

Modeling spatial distributions of hydrogen and oxygen stable isotopes in surface soils of the upper reaches of Minjiang River based on machine languages

To study the feasibility of simulating the spatial distribution of hydrogen and oxygen stable isotopes composition (δ²H and δ¹⁸O) in the surface soil based on the machine learning method and to investigate large-scale distribution of δ²H and δ¹⁸O in the upper reaches of Minjiang River, 183 soil samples were collected from the 0-10 cm soil layer. After variable selection, back propagation (BP) neural network, random forests (RF) and support vector machine (SVM) were used to model the δ²H and δ¹⁸O of the study area, with the accuracies being evaluated. The structural equation model (SEM) was used to reveal the mechanism between the auxiliary variables and the δ²H and δ¹⁸O of soil water. The results showed that the RF model had the highest prediction accuracy, and could explain 75.0% and 64.0% of the variations of δ²H and δ¹⁸O in the surface soil, respectively. In this model, soil water content was the most important auxiliary variable, contributing 48.9% and 37.4% to δ²H and δ¹⁸O. Vegetation factors had stronger influence on δ²H and δ¹⁸O in the surface soil than climate factors, and the influence of climate factors on δ²H and δ¹⁸O was media-ted by vegetation factors. Among all the auxiliary variables, hydrogen/oxygen isotope of precipitation had the lowest effect on δ²H and δ¹⁸O due to the fractionation. The δ²H and δ¹⁸O in the surface soil of the upper reaches of the Minjiang River changed significantly across different months during the growing season. The increases of δ²H and δ¹⁸O in the early growing season and the decreases in the late growing season were mainly affected by vegetation, while climate change led to a small fluctuation in the middle growing season.