频率、温度、含水量和土壤质地对土壤介电特性的影响:基于深度神经网络的回归模型

IF 0.9 4区 工程技术 Q4 ENGINEERING, CHEMICAL
Prachi Palta, Prabhdeep Kaur, K. S. Mann
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引用次数: 1

摘要

土壤介电特性在微波遥感和土壤处理中有着广泛的应用。在本研究中,使用矢量网络分析仪和开放式同轴探头(85070E, Agilent Technologies)在0.2至14 GHz区域测量了土壤的介电特性(Ɛ'和Ɛ')。观察结果表明,Ɛ'和Ɛ'与频率、质地、含水量和温度密切相关。建立了基于深度神经网络(DNN)的多变量回归模型来模拟它们的行为,利用实验观测数据自动学习其参数。结果显示,实际记录值Ɛ’和预测值Ɛ’之间的交叉验证均方根误差(RMSE)分别为0.0258和0.0336,r2得分分别为1.0000和0.9998。将该模型与基于响应面法(RSM)的模型进行了比较;其中,基于dnn的模型效果明显更好。此外,对含水率为18%(即在15%和20%之间的观测实验)的壤土质地进行了基于dnn的Ɛ'和Ɛ'估计,并与15%和20%的实际观测值进行了绘制,以验证所提出的基于dnn的模型的预测能力。它显示了一个可接受的介电性质估计和快速和创新的基于dnn的方法的有效性,预测土壤的介电性质取决于多个因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dielectric behavior of soil as a function of frequency, temperature, moisture content and soil texture: a deep neural networks based regression model
Abstract Dielectric behavior of soil has utmost applications in microwave remote sensing and soil treatment. In the present study, the soil's dielectric properties (Ɛ' and Ɛ") were measured using the vector network analyzer and an open-ended coaxial probe (85070E, Agilent Technologies) in the region of 0.2 to 14 GHz. The observed results showed that Ɛ' and Ɛ" strongly depend on frequency, texture, moisture content and temperature. A deep neural network (DNN) based multivariable regression model has been developed to model their behavior, using experimentally observed data to learn its parameters automatically. It shows a five-fold cross-validation root mean square errors (RMSE) of 0.0258 and 0.0336, and R2-scores of 1.0000 and 0.9998, between actual recorded and predicted values of Ɛ' and Ɛ", respectively. The results of the proposed DNN-based model have been compared with the response surface method (RSM) based model; among these, the DNN-based model shows significantly better results. Further, the DNN-based estimates of Ɛ' and Ɛ" for loam texture at a moisture content of 18% (i.e. in between observed experiments of 15% and 20%) are made and plotted with actual observed values at 15% and 20% to verify the predictive ability of the proposed DNN-based model. It shows an acceptable estimate of dielectric properties and the effectiveness of the fast and innovative DNN-based approach for predicting soil's dielectric properties depending upon multiple factors.
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来源期刊
Journal of Microwave Power and Electromagnetic Energy
Journal of Microwave Power and Electromagnetic Energy ENGINEERING, CHEMICAL-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
2.50
自引率
6.70%
发文量
21
期刊介绍: The Journal of the Microwave Power Energy (JMPEE) is a quarterly publication of the International Microwave Power Institute (IMPI), aimed to be one of the primary sources of the most reliable information in the arts and sciences of microwave and RF technology. JMPEE provides space to engineers and researchers for presenting papers about non-communication applications of microwave and RF, mostly industrial, scientific, medical and instrumentation. Topics include, but are not limited to: applications in materials science and nanotechnology, characterization of biological tissues, food industry applications, green chemistry, health and therapeutic applications, microwave chemistry, microwave processing of materials, soil remediation, and waste processing.
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