Instrument Bias Correction With Machine Learning Algorithms: Application to Field-Portable Mass Spectrometry

In situ sensors for environmental chemistry promise more thorough observations, which are necessary for high confidence predictions in earth systems science. However, these can be a challenge to interpret because the sensors are strongly influenced by temperature, humidity, pressure, or other secondary environmental conditions that are not of direct interest. We present a comparison of two statistical learning methods—a generalized additive model and a long short-term memory neural network model for bias correction of in situ sensor data. We discuss their performance and tradeoffs when the two bias correction methods are applied to data from submersible and shipboard mass spectrometers. Both instruments measure the most abundant gases dissolved in water and can be used to reconstruct biochemical metabolisms, including those that regulate atmospheric carbon dioxide. Both models demonstrate a high degree of skill at correcting for instrument bias using correlated environmental measurements; the difference in their respective performance is less than 1% in terms of root mean squared error. Overall, the long short-term memory bias correction produced an error of 5% for O2 and 8.5% for CO2 when compared against independent membrane DO and laser spectrometer instruments. This represents a predictive accuracy of 92–95% for both gases. It is apparent that the most important factor in a skillful bias correction is the measurement of the secondary environmental conditions that are likely to correlate with the instrument bias. These statistical learning methods are extremely flexible and permit the inclusion of nearly an infinite number of correlates in finding the best bias correction solution.

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Long Short-Term Memory Neural Network (LSTM-NN) for Aquifer Level Time Series Forecasting Using in-Situ Piezometric Observations

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.126800 ◽

2021 ◽

pp. 126800

Author(s):

Ryan Solgi ◽

Hugo A. Loáiciga ◽

Mark Kram

Keyword(s):

Neural Network ◽

Time Series ◽

Short Term Memory ◽

Time Series Forecasting ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Knowledge transfer from simulation to reality via Long Short-Term Memory networks: Estimating groundwater table depth anomalies over Europe

10.5194/egusphere-egu21-590 ◽

2021 ◽

Author(s):

Yueling Ma ◽

Carsten Montzka ◽

Bagher Bayat ◽

Stefan Kollet

Keyword(s):

Short Term Memory ◽

Scientific Data ◽

Groundwater Table ◽

Short Term ◽

Term Memory ◽

Continental Scale ◽

Groundwater Table Depth ◽

Long Short Term Memory ◽

Simulation Results

Near real-time groundwater table depth measurements are scarce over Europe, leading to challenges in monitoring groundwater resources at the continental scale. In this study, we leveraged knowledge learned from simulation results by Long Short-Term Memory (LSTM) networks to estimate monthly groundwater table depth anomaly (wtda) data over Europe. The LSTM networks were trained, validated, and tested at individual pixels on anomaly data derived from daily integrated hydrologic simulation results over Europe from 1996 to 2016, with a spatial resolution of 0.11&#176; (Furusho-Percot et al., 2019), to predict monthly wtda based on monthly precipitation anomalies (pra) and soil moisture anomalies (&#952;a). Without additional training, we directly fed the networks with averaged monthly pra and &#952;a data from 1996 to 2016 obtained from commonly available observational datasets and reanalysis products, and compared the network outputs with available borehole in situ measured wtda. The LSTM network estimates show good agreement with the in situ observations, resulting in Pearson correlation coefficients of regional averaged wtda data in seven PRUDENCE regions ranging from 42% to 76%, which are ~ 10% higher than the original simulation results except for the Iberian Peninsula. Our study demonstrates the potential of LSTM networks to transfer knowledge from simulation to reality for the estimation of wtda over Europe. The proposed method can be used to provide spatiotemporally continuous information at large spatial scales in case of sparse ground-based observations, which is common for groundwater table depth measurements. Moreover, the results highlight the advantage of combining physically-based models with machine learning techniques in data processing.&#160;Reference:Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S. (2019). Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation. Scientific Data, 6(1).

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Application of deep learning methods to predict ionosphere parameters in real time

E3S Web of Conferences ◽

10.1051/e3sconf/202019602007 ◽

2020 ◽

Vol 196 ◽

pp. 02007

Author(s):

Vladimir Mochalov ◽

Anastasia Mochalova

Keyword(s):

Neural Network ◽

Deep Learning ◽

Real Time ◽

Network Architecture ◽

Short Term Memory ◽

Neural Network Architecture ◽

Short Term ◽

Learning Methods ◽

Term Memory ◽

Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

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A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea

Atmosphere ◽

10.3390/atmos11040348 ◽

2020 ◽

Vol 11 (4) ◽

pp. 348 ◽

Cited By ~ 3

Author(s):

Guang Yang ◽

HwaMin Lee ◽

Giyeol Lee

Keyword(s):

Air Pollution ◽

Deep Learning ◽

Short Term Memory ◽

Hybrid Models ◽

Short Term ◽

Learning Methods ◽

Term Memory ◽

Long Short Term Memory ◽

Gated Recurrent Unit ◽

Concentration Levels

Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5.

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A Spectral-Spatial Cascaded 3D Convolutional Neural Network with a Convolutional Long Short-Term Memory Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs11202363 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2363 ◽

Cited By ~ 2

Author(s):

Wenchao Qi ◽

Xia Zhang ◽

Nan Wang ◽

Mao Zhang ◽

Yi Cen

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Hyperspectral Image ◽

Short Term ◽

Dynamic Learning ◽

Learning Methods ◽

Term Memory ◽

Long Short Term Memory

Deep learning methods used for hyperspectral image (HSI) classification often achieve greater accuracy than traditional algorithms but require large numbers of training epochs. To simplify model structures and reduce their training epochs, an end-to-end deep learning framework incorporating a spectral-spatial cascaded 3D convolutional neural network (CNN) with a convolutional long short-term memory (CLSTM) network, called SSCC, is proposed herein for HSI classification. The SSCC framework employs cascaded 3D CNN to learn the spectral-spatial features of HSIs and uses the CLSTM network to extract sequence features. Residual connections are used in SSCC to accelerate model convergence, with the outputs of previous convolutional layers concatenated as inputs for subsequent layers. Moreover, the data augmentation, parametric rectified linear unit, dynamic learning rate, batch normalization, and regularization (including dropout and L2) methods are used to increase classification accuracy and prevent overfitting. These attributes allow the SSCC framework to achieve good performance for HSI classification within 20 epochs. Three well-known datasets including Indiana Pines, University of Pavia, and Pavia Center were employed to evaluate the classification performance of the proposed algorithm. The GF-5 dataset of Anxin County, obtained from China’s recently launched spaceborne Advanced Hyperspectral Imager, was also used for classification experiments. The experimental results demonstrate that the proposed SSCC framework achieves state-of-the-art performance with better training efficiency than other deep learning methods.

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Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense Disambiguation

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3477578 ◽

2022 ◽

Vol 21 (2) ◽

pp. 1-16

Author(s):

Ali Saeed ◽

Rao Muhammad Adeel Nawab ◽

Mark Stevenson

Keyword(s):

Deep Learning ◽

Language Processing ◽

Short Term Memory ◽

Word Sense Disambiguation ◽

Word Sense ◽

Short Term ◽

Learning Methods ◽

Term Memory ◽

Sense Disambiguation ◽

Long Short Term Memory

Word Sense Disambiguation (WSD), the process of automatically identifying the correct meaning of a word used in a given context, is a significant challenge in Natural Language Processing. A range of approaches to the problem has been explored by the research community. The majority of these efforts has focused on a relatively small set of languages, particularly English. Research on WSD for South Asian languages, particularly Urdu, is still in its infancy. In recent years, deep learning methods have proved to be extremely successful for a range of Natural Language Processing tasks. The main aim of this study is to apply, evaluate, and compare a range of deep learning methods approaches to Urdu WSD (both Lexical Sample and All-Words) including Simple Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Units, Bidirectional Long-Short Term Memory, and Ensemble Learning. The evaluation was carried out on two benchmark corpora: (1) the ULS-WSD-18 corpus and (2) the UAW-WSD-18 corpus. Results (Accuracy = 63.25% and F1-Measure = 0.49) show that a deep learning approach outperforms previously reported results for the Urdu All-Words WSD task, whereas performance using deep learning approaches (Accuracy = 72.63% and F1-Measure = 0.60) are low in comparison to previously reported for the Urdu Lexical Sample task.

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Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities

Brain Sciences ◽

10.3390/brainsci10070442 ◽

2020 ◽

Vol 10 (7) ◽

pp. 442 ◽

Cited By ~ 1

Author(s):

You Wang ◽

Ming Zhang ◽

RuMeng Wu ◽

Han Gao ◽

Meng Yang ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Short Term Memory ◽

Human Subjects ◽

Short Term ◽

Learning Methods ◽

Term Memory ◽

Spatial Features ◽

Long Short Term Memory ◽

Silent Speech

Silent speech decoding is a novel application of the Brain–Computer Interface (BCI) based on articulatory neuromuscular activities, reducing difficulties in data acquirement and processing. In this paper, spatial features and decoders that can be used to recognize the neuromuscular signals are investigated. Surface electromyography (sEMG) data are recorded from human subjects in mimed speech situations. Specifically, we propose to utilize transfer learning and deep learning methods by transforming the sEMG data into spectrograms that contain abundant information in time and frequency domains and are regarded as channel-interactive. For transfer learning, a pre-trained model of Xception on the large image dataset is used for feature generation. Three deep learning methods, Multi-Layer Perception, Convolutional Neural Network and bidirectional Long Short-Term Memory, are then trained using the extracted features and evaluated for recognizing the articulatory muscles’ movements in our word set. The proposed decoders successfully recognized the silent speech and bidirectional Long Short-Term Memory achieved the best accuracy of 90%, outperforming the other two algorithms. Experimental results demonstrate the validity of spectrogram features and deep learning algorithms.

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Multilabel Text Classification in News Articles Using Long-Term Memory with Word2Vec

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1655 ◽

2020 ◽

Vol 4 (2) ◽

pp. 276-285

Author(s):

Winda Kurnia Sari ◽

Dian Palupi Rini ◽

Reza Firsandaya Malik ◽

Iman Saladin B. Azhar

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Text Classification ◽

Large Scale ◽

Short Term Memory ◽

Short Term ◽

Learning Methods ◽

Processing Variable ◽

Term Memory ◽

Long Short Term Memory

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.

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Long Short-Term Memory Neural Network (LSTM-NN) for Aquifer Level Time Series Forecasting Using in-Situ Piezometric Observations

10.1002/essoar.10506314.2 ◽

2021 ◽

Author(s):

Ryan Solgi ◽

Hugo A. Loaiciga ◽

Mark Kram

Keyword(s):

Neural Network ◽

Time Series ◽

Short Term Memory ◽

Time Series Forecasting ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies

Journal of Risk and Financial Management ◽

10.3390/jrfm14100486 ◽

2021 ◽

Vol 14 (10) ◽

pp. 486

Author(s):

Dante Miller ◽

Jong-Min Kim

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Short Term Memory ◽

Moving Average ◽

Short Term ◽

Learning Methods ◽

Term Memory ◽

Machine Learning Methods ◽

Error Measures ◽

Long Short Term Memory

In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures.

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