scholarly journals Exploring Wetland Dynamics in Large River Floodplain Systems with Unsupervised Machine Learning: A Case Study of the Dongting Lake, China

2020 ◽  
Vol 12 (18) ◽  
pp. 2995
Author(s):  
Lei Jing ◽  
Yan Zhou ◽  
Qing Zeng ◽  
Shuguang Liu ◽  
Guangchun Lei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Average Rate ◽  
Satellite Image ◽  
Large River ◽  
Dongting Lake ◽  
Unsupervised Machine Learning ◽  
Wetland Dynamics ◽  
River Floodplain ◽  
Management Actions

Large river floodplain systems (LRFS) are among the most diverse and dynamic ecosystems. Accurately monitoring the dynamics of LRFS over long time series is fundamental and essential for their sustainable development. However, challenges remain because the spatial distribution of LRFS is never static due to inter- and intra-annual changes in environmental conditions. In this study, we developed and tested a methodological framework to re-construct the long-term wetland dynamics in Dongting Lake, China, utilizing an unsupervised machine-learning algorithm (UMLA) on the basis of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) time series. Our results showed that the UMLA achieved comparable performance to the time-consuming satellite image segmentation method with a Kappa coefficient of agreement greater than 0.75 and an overall accuracy over 85%. With the re-constructed annual wetland distribution maps, we found that 31.35% of wet meadows, one of most important ecological assets in the region, disappeared at an average rate of c.a. 1660 ha year−1 during the past two decades, which suggests that the Dongting Lake is losing its ecological function of providing wintering ground for migratory water birds, and remediation management actions are urgently required. We concluded that UMLA offers a fast and cost-efficient alternative to monitor ecological responses in a rapidly changing environment.

scholarly journals Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 974
Author(s):  
Lorena Alves Santos ◽  
Karine Ferreira ◽  
Michelle Picoli ◽  
Gilberto Camara ◽  
Raul Zurita-Milla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Satellite Image ◽  
Spatiotemporal Patterns ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Self Organizing Maps ◽  
Machine Learning Methods ◽  
Land Use And Cover

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.

scholarly journals Integrating Open Data Cube and Brazil Data Cube Platforms for Land Use and Cover Classifications

2021 ◽  
Vol 73 (4) ◽  
pp. 1036-1047
Author(s):  
Felipe Menino Carlos ◽  
Vitor Conrado Faria Gomes ◽  
Gilberto Ribeiro de Queiroz ◽  
Felipe Carvalho de Souza ◽  
Karine Reis Ferreira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Satellite Image ◽  
Open Data ◽  
Data Cube ◽  
Machine Learning Techniques ◽  
Multidimensional Data ◽  
Temporal Dimension ◽  
Data Cubes

The potential to perform spatiotemporal analysis of the Earth's surface, fostered by a large amount of Earth Observation (EO) open data provided by space agencies, brings new perspectives to create innovative applications. Nevertheless, these big datasets pose some challenges regarding storage and analytical processing capabilities. The organization of these datasets as multidimensional data cubes represents the state-of-the-art in analysis-ready data regarding information extraction. EO data cubes can be defined as a set of time-series images associated with spatially aligned pixels along the temporal dimension. Some key technologies have been developed to take advantage of the data cube power. The Open Data Cube (ODC) framework and the Brazil Data Cube (BDC) platform provide capabilities to access and analyze EO data cubes. This paper introduces two new tools to facilitate the creation of land use and land over (LULC) maps using EO data cubes and Machine Learning techniques, and both built on top of ODC and BDC technologies. The first tool is a module that extends the ODC framework capabilities to lower the barriers to use Machine Learning (ML) algorithms with EO data. The second tool relies on integrating the R package named Satellite Image Time Series (sits) with ODC to enable the use of the data managed by the framework. Finally, water mask classification and LULC mapping applications are presented to demonstrate the processing capabilities of the tools.

Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning

2021 ◽  
Author(s):  
Ilan Sousa Figueirêdo ◽  
Tássio Farias Carvalho ◽  
Wenisten José Dantas Silva ◽  
Lílian Lefol Nani Guarieiro ◽  
Erick Giovani Sperandio Nascimento
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Anomaly Detection ◽  
Unsupervised Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Unsupervised Machine Learning

Abstract Detection of anomalous events in practical operation of oil and gas (O&G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an annotated dataset, this paper intends to perform a comparative evaluation performance of unsupervised learning algorithms to support experts for anomaly detection and pattern recognition in multivariate time-series data. So, the goal is to allow experts to analyze a small set of patterns and label them, instead of analyzing large datasets. This paper used the public 3W database of three offshore naturally flowing wells. The experiment used real data of production of O&G from underground reservoirs with the following anomalous events: (i) spurious closure of Downhole Safety Valve (DHSV) and (ii) quick restriction in Production Choke (PCK). Six unsupervised machine learning algorithms were assessed: Cluster-based Algorithm for Anomaly Detection in Time Series Using Mahalanobis Distance (C-AMDATS), Luminol Bitmap, SAX-REPEAT, k-NN, Bootstrap, and Robust Random Cut Forest (RRCF). The comparison evaluation of unsupervised learning algorithms was performed using a set of metrics: accuracy (ACC), precision (PR), recall (REC), specificity (SP), F1-Score (F1), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision-Recall Curve (AUC-PRC). The experiments only used the data labels for assessment purposes. The results revealed that unsupervised learning successfully detected the patterns of interest in multivariate data without prior annotation, with emphasis on the C-AMDATS algorithm. Thus, unsupervised learning can leverage supervised models through the support given to data annotation.

scholarly journals Satellite Image Time Series Analysis for Big Earth Observation Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2428
Author(s):  
Rolf Simoes ◽  
Gilberto Camara ◽  
Gilberto Queiroz ◽  
Felipe Souza ◽  
Pedro R. Andrade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Satellite Image ◽  
Earth Observation ◽  
Observation Data ◽  
Accuracy Measurement ◽  
Series Analysis ◽  
Earth Observation Data

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.

scholarly journals Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms

2020 ◽  
Author(s):  
Ilan Figueirêdo ◽  
Lílian Lefol Nani Guarieiro ◽  
Erick Giovani Sperandio Nascimento
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Time Series Data ◽  
Meteorological Data ◽  
Multivariate Time Series ◽  
Operating Conditions ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Unsupervised Machine Learning

The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data.

scholarly journals Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review

2021 ◽  
Vol 13 (23) ◽  
pp. 4822
Author(s):  
Waytehad Rose Moskolaï ◽  
Wahabou Abdou ◽  
Albert Dipanda ◽  
Kolyang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Satellite Images ◽  
Spatial Information ◽  
Time Series Prediction ◽  
Satellite Image ◽  
Machine Learning Algorithms ◽  
Temporal Dimension ◽  
Prediction Problems

Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classification, segmentation, anomaly detection, and prediction. Several traditional machine learning algorithms have been developed and successfully applied to time series for predictions. However, these methods have limitations in some situations, thus deep learning (DL) techniques have been introduced to achieve the best performance. Reviews of machine learning and DL methods for time series prediction problems have been conducted in previous studies. However, to the best of our knowledge, none of these surveys have addressed the specific case of works using DL techniques and satellite images as datasets for predictions. Therefore, this paper concentrates on the DL applications for SITS prediction, giving an overview of the main elements used to design and evaluate the predictive models, namely the architectures, data, optimization functions, and evaluation metrics. The reviewed DL-based models are divided into three categories, namely recurrent neural network-based models, hybrid models, and feed-forward-based models (convolutional neural networks and multi-layer perceptron). The main characteristics of satellite images and the major existing applications in the field of SITS prediction are also presented in this article. These applications include weather forecasting, precipitation nowcasting, spatio-temporal analysis, and missing data reconstruction. Finally, current limitations and proposed workable solutions related to the use of DL for SITS prediction are also highlighted.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

Sign in / Sign up

Export Citation Format

Share Document