Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Xianming Dou; Yongguo Yang; Jinhui Luo

doi:10.3390/su10010203

Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Sustainability ◽

10.3390/su10010203 ◽

2018 ◽

Vol 10 (1) ◽

pp. 203 ◽

Cited By ~ 8

Author(s):

Xianming Dou ◽

Yongguo Yang ◽

Jinhui Luo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Eddy Covariance ◽

Carbon Flux ◽

Carbon Fluxes ◽

Forest Carbon ◽

Machine Learning Techniques ◽

Support Vector ◽

Inference System ◽

Learning Techniques

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the scientific community aiming to gain new insights into the temporal and spatial variation of different carbon fluxes in terrestrial ecosystems. In this study, adaptive neuro-fuzzy inference system (ANFIS) and generalized regression neural network (GRNN) models were developed to predict the daily carbon fluxes in three boreal forest ecosystems based on eddy covariance (EC) measurements. Moreover, a comparison was made between the modeled values derived from these models and those of traditional artificial neural network (ANN) and support vector machine (SVM) models. These models were also compared with multiple linear regression (MLR). Several statistical indicators, including coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), bias error (Bias) and root mean square error (RMSE) were utilized to evaluate the performance of the applied models. The results showed that the developed machine learning models were able to account for the most variance in the carbon fluxes at both daily and hourly time scales in the three stands and they consistently and substantially outperformed the MLR model for both daily and hourly carbon flux estimates. It was demonstrated that the ANFIS and ANN models provided similar estimates in the testing period with an approximate value of R2 = 0.93, NSE = 0.91, Bias = 0.11 g C m−2 day−1 and RMSE = 1.04 g C m−2 day−1 for daily gross primary productivity, 0.94, 0.82, 0.24 g C m−2 day−1 and 0.72 g C m−2 day−1 for daily ecosystem respiration, and 0.79, 0.75, 0.14 g C m−2 day−1 and 0.89 g C m−2 day−1 for daily net ecosystem exchange, and slightly outperformed the GRNN and SVM models. In practical terms, however, the newly developed models (ANFIS and GRNN) are more robust and flexible, and have less parameters needed for selection and optimization in comparison with traditional ANN and SVM models. Consequently, they can be used as valuable tools to estimate forest carbon fluxes and fill the missing carbon flux data during the long-term EC measurements.

Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06407-2 ◽

2020 ◽

Author(s):

Hossein Safarzadeh ◽

Marco Leonesio ◽

Giacomo Bianchi ◽

Michele Monno

Keyword(s):

Neural Network ◽

Machine Learning ◽

Regression Model ◽

Hybrid Models ◽

Physical Models ◽

Machine Learning Techniques ◽

Support Vector ◽

Grinding Wheel ◽

Wheel Wear ◽

Learning Techniques

AbstractThis work proposes a model for suggesting optimal process configuration in plunge centreless grinding operations. Seven different approaches were implemented and compared: first principles model, neural network model with one hidden layer, support vector regression model with polynomial kernel function, Gaussian process regression model and hybrid versions of those three models. The first approach is based on an enhancement of the well-known numerical process simulation of geometrical instability. The model takes into account raw workpiece profile and possible wheel-workpiece loss of contact, which introduces an inherent limitation on the resulting profile waviness. Physical models, because of epistemic errors due to neglected or oversimplified functional relationships, can be too approximated for being considered in industrial applications. Moreover, in deterministic models, uncertainties affecting the various parameters are not explicitly considered. Complexity in centreless grinding models arises from phenomena like contact length dependency on local compliance, contact force and grinding wheel roughness, unpredicted material properties of the grinding wheel and workpiece, precision of the manual setup done by the operator, wheel wear and nature of wheel wear. In order to improve the overall model prediction accuracy and allow automated continuous learning, several machine learning techniques have been investigated: a Bayesian regularized neural network, an SVR model and a GPR model. To exploit the a priori knowledge embedded in physical models, hybrid models are proposed, where neural network, SVR and GPR models are fed by the nominal process parameters enriched with the roundness predicted by the first principle model. Those hybrid models result in an improved prediction capability.

A Machine Learning View on Momentum and Reversal Trading

Algorithms ◽

10.3390/a11110170 ◽

2018 ◽

Vol 11 (11) ◽

pp. 170 ◽

Cited By ~ 2

Author(s):

Zhixi Li ◽

Vincent Tam

Keyword(s):

Neural Network ◽

Machine Learning ◽

Stock Market ◽

Short Term Memory ◽

Predictive Ability ◽

Trading Strategies ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Learning Techniques

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8468 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1925-1930

Author(s):

Ambeshwar Kumar ◽

R. Manikandan ◽

Robbi Rahim

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Brain Tumor ◽

Tumor Detection ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Techniques ◽

Convolution Algorithm ◽

The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

10.7287/peerj.preprints.27020v1 ◽

2018 ◽

Author(s):

Behrouz Alizadeh Savareh ◽

Azadeh Bashiri ◽

Ali Behmanesh ◽

Gholam Hossein Meftahi ◽

Boshra Hatef

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Dimension Reduction ◽

Sleep Disorders ◽

Component Analysis ◽

Machine Learning Techniques ◽

Support Vector ◽

Sleep Scoring ◽

Learning Techniques

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. methods: Sleep-edf polysomnography was used in this study as a dataset. Support Vector Machines and Artificial Neural Network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results: Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy respectively. Discussion and Conclusion: Similar to the state of the art performance, introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

Journal of Environmental Geography ◽

10.2478/jengeo-2020-0005 ◽

2020 ◽

Vol 13 (1-2) ◽

pp. 43-52

Author(s):

Boudewijn van Leeuwen ◽

Zalán Tobak ◽

Ferenc Kovács

Keyword(s):

Neural Network ◽

Machine Learning ◽

Land Use ◽

Land Cover ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

Land Use Land Cover ◽

Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

PeerJ ◽

10.7717/peerj.5247 ◽

2018 ◽

Vol 6 ◽

pp. e5247 ◽

Cited By ~ 3

Author(s):

Behrouz Alizadeh Savareh ◽

Azadeh Bashiri ◽

Ali Behmanesh ◽

Gholam Hossein Meftahi ◽

Boshra Hatef

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Dimension Reduction ◽

Sleep Disorders ◽

Component Analysis ◽

Machine Learning Techniques ◽

Support Vector ◽

Sleep Scoring ◽

Learning Techniques

IntroductionSleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.MethodsSleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.ResultsNeighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.Discussion and ConclusionSimilar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

Modern Physics Letters B ◽

10.1142/s0217984918501324 ◽

2018 ◽

Vol 32 (11) ◽

pp. 1850132 ◽

Cited By ~ 9

Author(s):

Harpreet Singh ◽

Prashant Singh Rana ◽

Urvinder Singh

Keyword(s):

Machine Learning ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Machine Learning Techniques ◽

Support Vector ◽

Prediction Errors ◽

Learning Approaches ◽

Inference System ◽

Drug Synergy ◽

Learning Techniques

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug–drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

10.7287/peerj.preprints.27020 ◽

2018 ◽

Author(s):

Behrouz Alizadeh Savareh ◽

Azadeh Bashiri ◽

Ali Behmanesh ◽

Gholam Hossein Meftahi ◽

Boshra Hatef

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Dimension Reduction ◽

Sleep Disorders ◽

Component Analysis ◽

Machine Learning Techniques ◽

Support Vector ◽

Sleep Scoring ◽

Learning Techniques

Machine Learning Methods to Classify Mushrooms for Edibility-A Review

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst060909 ◽

2020 ◽

Vol 06 (09) ◽

pp. 54-58

Author(s):

Rakesh Kumar Y and Dr. V. Chandrasekhar

Keyword(s):

Neural Network ◽

Machine Learning ◽

Literature Review ◽

Edible Mushroom ◽

Machine Learning Techniques ◽

Support Vector ◽

Applied Machine Learning ◽

Learning Techniques ◽

Artificial Neural Network Ann

There are thousands of species of Mushrooms in the world; they are edible and non-edible being poisonous. It is difficult for non-expertise person to Identify poisonous and edible mushroom of all the species manually. So a computer aided system with software or algorithm is required to classify poisonous and nonpoisonous mushrooms. In this paper a literature review is presented on classification of poisonous and nonpoisonous mushrooms. Most of the research works to classify the type of mushroom have applied, machine learning techniques like Naïve Bayes, K-Neural Network, Support vector Machine(SVM), Artificial Neural Network(ANN), Decision Tree techniques. In this literature review, a summary and comparisons of all different techniques of mushroom classification in terms of its performance parameters, merits and demerits faced during the classification of mushrooms using machine learning techniques.

Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Advances in Meteorology ◽

10.1155/2018/1824317 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 5

Author(s):

Xianming Dou ◽

Yongguo Yang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Water Vapor ◽

Data Driven ◽

Machine Learning Techniques ◽

Support Vector ◽

Group Method ◽

Generalized Regression Neural Network ◽

Learning Approaches ◽

Inference System

Remarkable progress has been made over the last decade toward characterizing the mechanisms that dominate the exchange of water vapor between the biosphere and the atmosphere. This is attributed partly to the considerable development of machine learning techniques that allow the scientific community to use these advanced tools for approximating the nonlinear processes affecting the variation of water vapor in terrestrial ecosystems. Three novel machine learning approaches, namely, group method of data handling, extreme learning machine (ELM), and adaptive neurofuzzy inference system (ANFIS), were developed to simulate and forecast the daily evapotranspiration (ET) at four different grassland sites based on the flux tower data using the eddy covariance method. These models were compared with the extensively utilized data-driven models, including artificial neural network, generalized regression neural network, and support vector machine (SVM). Moreover, the influences of internal functions on their corresponding models (SVM, ELM, and ANFIS) were investigated together. It was demonstrated that most developed models did good job of simulating and forecasting daily ET at the four sites. In addition to strengths of robustness and simplicity, the newly proposed methods achieved the estimates comparable to those of the conventional approaches and accordingly can be used as promising alternatives to traditional methods. It was further discovered that the generalization performance of the ELM, ANFIS, and SVM models strongly depended on their respective internal functions, especially for SVM.