scholarly journals A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

2021 ◽  
Vol 9 (3) ◽  
pp. 283
Author(s):  
Rafaela C. Cruz ◽  
Pedro Reis Costa ◽  
Susana Vinga ◽  
Ludwig Krippahl ◽  
Marta B. Lopes
Keyword(s):  
Machine Learning ◽  
Graphical Models ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Data Availability ◽  
Mitigation Measures ◽  
Support Vector ◽  
Governmental Agencies ◽  
Vector Machines ◽  
Source Data

Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.

scholarly journals Machine Learning Classification Algorithms for Predicting Karenia brevis Blooms on the West Florida Shelf

2021 ◽  
Vol 9 (9) ◽  
pp. 999
Author(s):  
Marvin F. Li ◽  
Patricia M. Glibert ◽  
Vyacheslav Lyubchich
Keyword(s):  
Machine Learning ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Large River ◽  
Karenia Brevis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Economic Damage ◽  
The West ◽  
Machine Learning Classification

Harmful algal blooms (HABs), events that kill fish, impact human health in multiple ways, and contaminate water supplies, have increased in frequency, magnitude, and impacts in numerous marine and freshwaters around the world. Blooms of the toxic dinoflagellate Karenia brevis have resulted in thousands of tons of dead fish, deaths to many other marine organisms, numerous respiratory-related hospitalizations, and tens to hundreds of millions of dollars in economic damage along the West Florida coast in recent years. Four types of machine learning algorithms, Support Vector Machine (SVM), Relevance Vector Machine (RVM), Naïve Bayes classifier (NB), and Artificial Neural Network (ANN), were developed and compared in their ability to predict these blooms. Comparing the 21 year monitoring dataset of K. brevis abundance, RVM and NB were found to have better skills in bloom prediction than the other two approaches. The importance of upwelling-favorable northerly winds in increasing K. brevis probability, and of onshore westerly winds in preventing blooms from dispersing offshore, were quantified using RVM, and all models were used to explore the importance of large river flows and the nutrients they supply in regulating blooms. These models provide new tools for management of these devastating algal blooms.

scholarly journals A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

2021 ◽  
Vol 13 (19) ◽  
pp. 3863
Author(s):  
Moein Izadi ◽  
Mohamed Sultan ◽  
Racha El Kadiri ◽  
Amin Ghannadi ◽  
Karem Abdelmohsen
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Harmful Algal Bloom ◽  
Lag Time ◽  
Controlling Factors ◽  
Support Vector ◽  
Red Tides ◽  
Temporal Models

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, we developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models the K. brevis abundance is used as the target, and 10 level-02 ocean color products extracted from daily archival MODIS satellite data are used as controlling factors. The adopted approach addresses two main shortcomings of earlier models: (1) the paucity of satellite data due to cloudy scenes and (2) the lag time between the period at which a variable reaches its highest correlation with the target and the time the bloom occurs. Eleven spatio-temporal models were generated, each from 3 consecutive day satellite datasets, with a forecasting span from 1 to 11 days. The 3-day models addressed the potential variations in lag time for some of the temporal variables. One or more of the generated 11 models could be used to predict HAB occurrences depending on availability of the cloud-free consecutive days. Findings indicate that XGBoost outperformed the other methods, and the forecasting models of 5–9 days achieved the best results. The most reliable model can forecast eight days ahead of time with balanced overall accuracy, Kappa coefficient, F-Score, and AUC of 96%, 0.93, 0.97, and 0.98 respectively. The euphotic depth, sea surface temperature, and chlorophyll-a are always among the most significant controlling factors. The proposed models could potentially be used to develop an “early warning system” for HABs in southwest Florida.

An investigation on machine-learning models for the prediction of cyanobacteria growth

2020 ◽  
Vol 194 (2) ◽  
pp. 85-94
Author(s):  
Johannes Giere ◽  
Derek Riley ◽  
R.J. Nowling ◽  
Joshua McComack ◽  
Hedda Sander
Keyword(s):  
Machine Learning ◽  
General Public ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Prediction Models ◽  
Support Vector ◽  
Great Effort ◽  
Local Health ◽  
Sudden Increase ◽  
Freshwater Lakes

Harmful algal blooms, which are a danger to the lives of humans and animals, are caused by a sudden increase in the concentration of cyanobacteria in freshwater lakes. Cyanobacteria concentrations can be reliably measured using chemical and biological indicators, but the measurement process of the indicators is either labor-intensive or very costly. These limitations do not allow the general public to measure concentrations, so local health organizations or departments regularly assume the responsibility of measuring water quality. While computational models exist to predict algal concentrations, the accuracy of these models and need for customization due to varied lake conditions make them generally not yet reliable. We find that common regression-error functions cannot sufficiently evaluate the performance of cyanobacteria prediction models because the occurrence of harmful algal blooms is rare. Therefore, we present a method of forecasting cyanobacteria concentrations in freshwater lakes based on a machine-learning model trained on a dataset from Lake Utah with automatically-measured indicators from lake buoys. We compare several models and find that a support vector machine with a radial basis function kernel for regression reliably forecasts harmful algal blooms using comparatively few and easy-to-obtain input parameters. The special feature of the model is that it exclusively uses variables that can be measured by the general public without great effort and costs, and the amount of data necessary to train such a model is relatively minimal, allowing different models to be trained to accommodate for the nuances of different lakes.

scholarly journals Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1020 ◽  
Author(s):  
Yong Kown ◽  
Seung Baek ◽  
Young Lim ◽  
JongCheol Pyo ◽  
Mayzonee Ligaray ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Chlorophyll A ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Support Vector ◽  
Landsat 8 ◽  
Artificial Neural

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms.

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

scholarly journals Heart disease prediction using machine learning techniques : a survey

2018 ◽  
Vol 7 (2.8) ◽  
pp. 684 ◽  
Author(s):  
V V. Ramalingam ◽  
Ayantan Dandapath ◽  
M Karthik Raja
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Learning Techniques ◽  
Vector Machines ◽  
Supervised Learning Algorithms ◽  
Life Threatening

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

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