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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

Titelangaben

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 (Hrsg.): Proceedings of International Workshop on Impedance Spectroscopy (IWIS 2022). - Chemnitz , 2022 . - S. 117-120
ISBN 979-8-3503-1039-9
DOI: https://doi.org/10.1109/IWIS57888.2022.9975106

Dies ist die aktuelle Version des Eintrags.

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.

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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: 21 Dec 2022 08:14
Letzte Änderung: 21 Dec 2022 08:14
URI: https://eref.uni-bayreuth.de/id/eprint/73173

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