at Google ScholarTitle data
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 ; Errachid, Abdelhamid
(ed.):
Proceedings of International Workshop on Impedance Spectroscopy (IWIS 2022). -
Chemnitz
,
2022
. - pp. 117-120
ISBN 979-8-3503-1039-9
DOI: https://doi.org/10.1109/IWIS57888.2022.9975106
This is the latest version of this item.
Abstract in another language
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.
Further data
| Item Type: | Article in a book |
|---|---|
| Refereed: | No |
| Keywords: | Electrical impedance spectroscopy; EIS; long short-term memory; LSTM; recurrent neural network; RNN; feature selection; nitrate |
| Institutions of the University: | Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology > Chair Measurement and Control Technology - Univ.-Prof. Dr.-Ing. Gerhard Fischerauer Faculties |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Date Deposited: | 21 Dec 2022 08:14 |
| Last Modified: | 21 Dec 2022 08:14 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/73173 |
Available Versions of this Item
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Investigation of long short-term memory artificial neural networks as estimators of nitrate concentrations in soil from measured electrical impedance spectra. (deposited 12 Oct 2022 12:45)
- Investigation of long short-term memory artificial neural networks as estimators of nitrate concentrations in soil from measured electrical impedance spectra. (deposited 21 Dec 2022 08:14) [Currently Displayed]