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Ma, Xiaohu ; Fischerauer, Gerhard:
Investigation of long short-term memory artificial neural networks as estimators of nitrate concentrations in soil from measured electrical impedance spectra.
In: Kanoun, Olfa
(Hrsg.):
Abstract Book 15th International Workshop on Impedance Spectroscopy (IWIS 2022). -
Chemnitz
,
2022
. - S. 125-128
ISBN 978-3-949744-01-3
Abstract
Monitoring the nitrate concentration in soil is crucial to guide the use of nitrate-based fertilizers. This study presents an investigation of long short-term memory (LSTM) recurrent artificial neural networks with regard to their suitability to extract nitrate concentrations from electrical impedance spectra of soil samples. Based on measured impedance spectra and physical properties of various synthetic sandy soils, the importance of different features for model training was investigated first. Both Random Forests and LSTM were tested as feature selection methods. Then numerous LSTM networks were trained to predict the nitrate concentration in sandy soils. The resulting regression models showed coefficients of determination between true and predicted nitrate concentrations as high as 0.95.
Weitere Angaben
| Publikationsform: | Aufsatz in einem Buch |
|---|---|
| Begutachteter Beitrag: | Nein |
| Keywords: | Electrical impedance spectroscopy; EIS; long short-term memory; LSTM; recurrent neural network; RNN; feature selection; nitrate |
| Institutionen der Universität: | Fakultäten > Fakultät für Ingenieurwissenschaften Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Mess- und Regeltechnik Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Mess- und Regeltechnik > Lehrstuhl Mess- und Regeltechnik - Univ.-Prof. Dr.-Ing. Gerhard Fischerauer Fakultäten |
| Titel an der UBT entstanden: | Ja |
| Themengebiete aus DDC: | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften |
| Eingestellt am: | 12 Okt 2022 12:45 |
| Letzte Änderung: | 12 Okt 2022 12:45 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/71743 |
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