基于人工神经网络和随机森林学习模型从土壤属性推测关键成土环境要素的研究

Predicting of Key Environmental Factors from Soil Properties Based on Artificial Neural Network and Random Forest Learning Model

土壤与其发生环境密切相关。如何利用土壤属性准确地推测环境要素的信息,是法庭土壤学的重要研究问题。本文以我国东部4省2市(北京、天津、河北、山东、安徽和江苏)为研究区,基于746个土壤表层样本的理化性质和光谱数据构建特征,使用人工神经网络和随机森林两种机器学习模型对海拔高度、年均温、年均降雨量和地表温度四个关键环境要素进行预测,并对两种模型的预测准确度进行了对比分析。结果显示:两个模型对四个目标环境变量的预测准确度R2在0.39~0.61之间;与神经网络模型相比,随机森林模型能够解释的环境变量的空间变异分别提高了9.9%、16.5%、10.3%、10.9%;同时发现,对海拔高度和降雨的预测效果要优于其他环境要素。这表明,利用机器学习的方法可以有效地从土壤属性反推其成土环境条件的信息,这为法庭土壤物证研究学中未知土壤样本的来源地范围识别提供了技术参考。

Soil is closely related to its formative environment.How to use soil properties to accurately predict the associated environmental information is an important research problem in soil forensics.About 746 soil samples were selected from Beijing,Tianjin,Hebei,Shandong,Anhui and Jiangsu in eastern China.Four key environmental information(elevation,average annual temperature,average annual rainfall and surface temperature)were predicted based on basic soil properties and spectral data using two machine learning models(neural network and random forest).Root mean square error(RMSE),determination coefficients(R2)and concordance correlation coefficient(CCC)were used to calculate the prediction accuracy.Results showed that the prediction accuracy of the two methods were between0.39 and 0.61.Compared with the neural network model,the spatial variation of environmental variables using random forest model were increased by 9.9%(elevation),16.5%(average annual temperature),10.3%(average annual rainfall),and 10.9%(surface temperature).And altitude and rainfall in this study area showed a better prediction accuracy than the other environmental variables.This suggests that the machine learning methods can be effective for predicting environmental information based on soil properties.This study provided a technical support for identifying the source of unknown soil samples in soil forensics.