Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

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WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

Remote Sensing ◽

10.3390/rs12121966 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1966 ◽

Cited By ~ 2

Author(s):

Muhammad Aldila Syariz ◽

Chao-Hung Lin ◽

Manh Van Nguyen ◽

Lalu Muhamad Jaelani ◽

Ariel C. Blanco

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Convolutional Neural Network ◽

Chlorophyll A ◽

Remote Sensing Images ◽

Two Stage ◽

Chl A ◽

The Neural Network

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

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Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies

Remote Sensing ◽

10.3390/rs13040718 ◽

2021 ◽

Vol 13 (4) ◽

pp. 718

Author(s):

Philipp M. Maier ◽

Sina Keller ◽

Stefan Hinz

Keyword(s):

Neural Network ◽

Deep Learning ◽

Chlorophyll A ◽

Real World ◽

Water Bodies ◽

Simulation Data ◽

Chlorophyll A Concentration ◽

Inland Water ◽

The Real ◽

Inland Water Bodies

Information about the chlorophyll a concentration of inland water bodies is essential for water monitoring. This study focuses on estimating chlorophyll a with remote sensing data, and machine learning (ML) approaches on the real-world SpecWa dataset. We adapt and apply a one-dimensional convolutional neural network (1D CNN) as a deep learning architecture for the first time to address this estimation. Since such a DL approach requires a large amount of data for its training, we rely on simulation data generated by the Water Color Simulator (WASI). This simulation is prepared accordingly and includes a knowledge-based water composition with two origins of the chlorophyll a concentration. Therefore, the training data is independent of the real-world SpecWa dataset, which is challenging for any ML approach. We define two spectral downsampling approaches as a pre-processing step, representing the hyperspectral EnMAP satellite mission (SR-EnMAP) and the multispectral Sentinel-2 mission (SR-Sentinel). Subsequently, we train a Random Forest, an artificial neural network, a band-ratio approach, and the 1D CNN on the WASI-generated simulation training dataset. Finally, all ML models are evaluated on the real SpecWa dataset. For both downsampled data, the 1D CNN outperforms the other ML models. On the finer resolved SR-EnMAP data it achieves an R2=81.9%, RMSE=12.4 μg L−1, and MAE=6.7 μg L−1. Besides, the 1D CNN’s performance decreases on the SR-Sentinel data to R2=62.4%. When focusing on the individual water bodies of the SpecWa dataset, the most significant differences exist between natural and artificial water bodies. We discover that the applied models estimate the chlorophyll a concentration of most natural water bodies satisfyingly. In sum, the newly DL approach can estimate the chlorophyll a values of unknown inland water bodies successfully, although it is trained on an entire simulation dataset.

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Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

Applied Sciences ◽

10.3390/app11146594 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6594

Author(s):

Yu-Chia Hsu

Keyword(s):

Neural Network ◽

Time Series ◽

Pattern Recognition ◽

Logistic Regression ◽

Regression Model ◽

Convolutional Neural Network ◽

Logistic Regression Model ◽

National Football League ◽

Performance Metrics ◽

Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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Chlorophyll-a Retrieval From Sentinel-2 Images Using Convolutional Neural Network Regression

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3070437 ◽

2021 ◽

pp. 1-5

Author(s):

Erchan Aptoula ◽

Sema Ariman

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Chlorophyll A ◽

Sentinel 2

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Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

Mathematical Problems in Engineering ◽

10.1155/2018/2410206 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Siyuan Zhao ◽

Zhiwei Xu ◽

Limin Liu ◽

Mengjie Guo ◽

Jing Yun

Keyword(s):

Neural Network ◽

Natural Language Processing ◽

Natural Language ◽

Convolutional Neural Network ◽

Language Processing ◽

Word Order ◽

Text Analysis ◽

Important Application ◽

Detection Mechanism ◽

Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

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Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2104779118 ◽

2021 ◽

Vol 118 (46) ◽

pp. e2104779118

Author(s):

T. Hannagan ◽

A. Agrawal ◽

L. Cohen ◽

S. Dehaene

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Language Processing ◽

Visual Pathway ◽

Neural Code ◽

Letter Recognition ◽

Invariant Representation ◽

Spoken Language Processing ◽

Visual Word Form Area ◽

Ventral Visual Pathway

The visual word form area (VWFA) is a region of human inferotemporal cortex that emerges at a fixed location in the occipitotemporal cortex during reading acquisition and systematically responds to written words in literate individuals. According to the neuronal recycling hypothesis, this region arises through the repurposing, for letter recognition, of a subpart of the ventral visual pathway initially involved in face and object recognition. Furthermore, according to the biased connectivity hypothesis, its reproducible localization is due to preexisting connections from this subregion to areas involved in spoken-language processing. Here, we evaluate those hypotheses in an explicit computational model. We trained a deep convolutional neural network of the ventral visual pathway, first to categorize pictures and then to recognize written words invariantly for case, font, and size. We show that the model can account for many properties of the VWFA, particularly when a subset of units possesses a biased connectivity to word output units. The network develops a sparse, invariant representation of written words, based on a restricted set of reading-selective units. Their activation mimics several properties of the VWFA, and their lesioning causes a reading-specific deficit. The model predicts that, in literate brains, written words are encoded by a compositional neural code with neurons tuned either to individual letters and their ordinal position relative to word start or word ending or to pairs of letters (bigrams).

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An Improved Cross-Domain Sentiment Analysis Based on a Semi-Supervised Convolutional Neural Network

10.4018/978-1-7998-8413-2.ch007 ◽

2022 ◽

pp. 155-170

Author(s):

Lap-Kei Lee ◽

Kwok Tai Chui ◽

Jingjing Wang ◽

Yin-Chun Fung ◽

Zhanhui Tan

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sentiment Analysis ◽

Language Processing ◽

Research Work ◽

Latent Semantic Indexing ◽

Future Research ◽

Training Time ◽

Cross Domain ◽

Document Frequency

The dependence on Internet in our daily life is ever-growing, which provides opportunity to discover valuable and subjective information using advanced techniques such as natural language processing and artificial intelligence. In this chapter, the research focus is a convolutional neural network for three-class (positive, neutral, and negative) cross-domain sentiment analysis. The model is enhanced in two-fold. First, a similarity label method facilitates the management between the source and target domains to generate more labelled data. Second, term frequency-inverse document frequency (TF-IDF) and latent semantic indexing (LSI) are employed to compute the similarity between source and target domains. Performance evaluation is conducted using three datasets, beauty reviews, toys reviews, and phone reviews. The proposed method enhances the accuracy by 4.3-7.6% and reduces the training time by 50%. The limitations of the research work have been discussed, which serve as the rationales of future research directions.

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Seasonal Cycles of Phytoplankton Expressed by Sine Equations Using the Daily Climatology from Satellite-Retrieved Chlorophyll-a Concentration (1997–2019) Over Global Ocean

Remote Sensing ◽

10.3390/rs12162662 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2662 ◽

Cited By ~ 2

Author(s):

Zexi Mao ◽

Zhihua Mao ◽

Cédric Jamet ◽

Marc Linderman ◽

Yuntao Wang ◽

...

Keyword(s):

Time Series ◽

Chlorophyll A ◽

Seasonal Variations ◽

Global Ocean ◽

Boreal Winter ◽

Seasonal Cycles ◽

Chlorophyll A Concentration ◽

Chl A ◽

Global Oceans ◽

Sine Equation

The global coverage of Chlorophyll-a concentration (Chl-a) has been continuously available from ocean color satellite sensors since September 1997 and the Chl-a data (1997–2019) were used to produce a climatological dataset by averaging Chl-a values at same locations and same day of year. The constructed climatology can remarkably reduce the variability of satellite data and clearly exhibit the seasonal cycles, demonstrating that the growth and decay of phytoplankton recurs with similarly seasonal cycles year after year. As the shapes of time series of the climatology exhibit strong periodical change, we wonder whether the seasonality of Chl-a can be expressed by a mathematic equation. Our results show that sinusoid functions are suitable to describe cyclical variations of data in time series and patterns of the daily climatology can be matched by sine equations with parameters of mean, amplitude, phase, and frequency. Three types of sine equations were used to match the climatological Chl-a with Mean Relative Differences (MRD) of 7.1%, 4.5%, and 3.3%, respectively. The sine equation with four sinusoids can modulate the shapes of the fitted values to match various patterns of climatology with small MRD values (less than 5%) in about 90% of global oceans. The fitted values can reflect an overall pattern of seasonal cycles of Chl-a which can be taken as a time series of biomass baseline for describing the state of seasonal variations of phytoplankton. The amplitude images, the spatial patterns of seasonal variations of phytoplankton, can be used to identify the transition zone chlorophyll fronts. The timing of phytoplankton blooms is identified by the biggest peak of the fitted values and used to classify oceans as different bloom seasons, indicating that blooms occur in all four seasons with regional features. In global oceans within latitude domains (48°N–48°S), blooms occupy approximately half of the ocean (50.6%) during boreal winter (December–February) in the northern hemisphere and more than half (58.0%) during austral winter (June–August) in the southern hemisphere. Therefore, the sine equation can be used to match the daily Chl-a climatology and the fitted values can reflect the seasonal cycles of phytoplankton, which can be used to investigate the underlying phenological characteristics.

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A Survey on Supervised Convolutional Neural Network and Its Major Applications

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch059 ◽

2020 ◽

pp. 1058-1071

Author(s):

D. T. Mane ◽

U. V. Kulkarni

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Language Processing ◽

Data Science ◽

Medical Image Analysis ◽

Computational Power ◽

Science Field ◽

Network Applications ◽

Learning Techniques ◽

Neural Network Applications

With the advances in the computer science field, various new data science techniques have been emerged. Convolutional Neural Network (CNN) is one of the Deep Learning techniques which have captured lots of attention as far as real world applications are considered. It is nothing but the multilayer architecture with hidden computational power which detects features itself. It doesn't require any handcrafted features. The remarkable increase in the computational power of Convolutional Neural Network is due to the use of Graphics processor units, parallel computing, also the availability of large amount of data in various variety forms. This paper gives the broad view of various supervised Convolutional Neural Network applications with its salient features in the fields, mainly Computer vision for Pattern and Object Detection, Natural Language Processing, Speech Recognition, Medical image analysis.

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