基于人工神经网络的普通蓍草(Achilla milleium)和百里香(thyymus kotschianus)空间分布制图(以马赞达兰省Donna牧场为例)

Zeinab Bahrein, Z. Jafarian, M. Shokri
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引用次数: 0

摘要

背景与目的:利用人工神经网络模型对马赞达兰省多纳草原常见蓍草(Achilla millefolium)和百里香(thyymus kotschianus)的空间分布进行研究。方法:对29个同质单位进行等随机分类抽样。在每个单元中,从0-30 cm深度采集3个土壤样品。本研究以20个环境因子为自变量,植物种类的存在为因变量。为编制物种空间分布图,将环境数据在GIS中转换成地图。然后使用频率对每个因素进行分类。在本研究中,使用了最常见的前馈神经网络——网络多层感知器。确定了网络的最优结构为1、20、20。在GIS软件中按低存在度、中存在度和高存在度4类缺失度绘制研究物种分布图。采用ROC曲线和Kappa系数对模型进行评价。结果:千叶和胸腺的AUC分别为96.8和84.7,表明模型的预测效果很好或很好。讨论与结论:千叶和百里草的kappa系数分别为89.0和76.0,预测效果非常好。
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Spatial distribution mapping of common yarrow (Achilla millefolium and thyme (Thymus kotschianus) using artificial neural network (Case study: Donna rangelands, Mazandaran province)
Background and Objective:The purpose of this study was to map the spatial distribution of common yarrow(Achilla millefolium)and thyme (Thymus kotschianus) using artificial neural network model in rangelands Donna, Mazandaran Province. Method:Sampling was carried out with equal random classification in 29 homogenous units. In each unit, 3 soil samples were harvested from depth of 0-30 cm. In this study, 20 environmental factors were the independent variables and the presence of plant species were the dependent variable. For the preparation spatial distribution map of the species, environmental data were converted to maps in GIS. Then each of these factors was classified using the frequency. In this research, network Multilayer Perceptron that is the most common feed forward neural network was used. Optimal structure for the network was determined 1, 20, and 20. Then distribution maps of studied species were prepared with 4 class absence and low presence, medium presence and high presence in the GIS software. Models were evaluated using ROC curves and Kappa coefficient. Findings:AUC were 96.8 and 84.7 for the species Achilla millefolium and Thymus kotschianus was, respectively  that indicates models are excellent or very good for the prediction. Discussion and Conclusion: Also kappa coefficient were calculated as 89.0 and 76.0 for Achilla millefolium and Thymus kotschyanus,  respectively which  indicate very good and good prediction.
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