scholarly journals Harmful algal blooms and their impact on fish mortalities in Lampung Bay: an overview

2021 ◽  
Vol 944 (1) ◽  
pp. 012027
Author(s):  
T Sidabutar ◽  
H Cappenberg ◽  
E S Srimariana ◽  
A Muawanah ◽  
S Wouthuyzen
Keyword(s):  
Brackish Water ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Phytoplankton Bloom ◽  
Fish Farming ◽  
Mass Mortality ◽  
The West ◽  
West Side ◽  
Causative Species ◽  
Harmful Species

Abstract The first disaster caused by harmful algal blooms in Lampung Bay was reported in 1991, where mass mortality of cultivated shrimp occurred in the brackish water ponds due to a Trichodesmium bloom. After this incident, the phenomenon reoccured in the following years continuously. Around nine species bloom makers in this bay are namely Pyrodinium sp., Noctiluca sp., Phaeocystis sp., Dinophysis sp., Trichodesmium sp., Ceratium sp., Prorocentrum sp., Pseudonitzhia sp., and Cochlodinium sp. The most frequent causative species, such as green Noctiluca and Trichodesmium, co-occurring during blooms and causing fish mortalities in the fish farming floating nets (KJA). Two species are known as the most potentially harmful species, namely Pyrodinium sp. and Cochlodinium sp. Cochlodinium blooms happened at the end of 2012, and since then, this species has continuously reappeared in the following years. The outbreak of Cochlodinium sp. still appeared in 2017 and 2018, but no fish-killing occurred. Phytoplankton bloom events occur at specific locations, mainly at fish farming floating nets on the west side of the bay, next to Hurun Cove. This paper discusses the occurrence of algal blooms in Lampung Bay and the triggering factors for increasing phytoplankton populations that cause harmful algal blooms.

scholarly journals LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah

2021 ◽  
Vol 18 (14) ◽  
pp. 7650
Author(s):  
Fatin Nadiah Yussof ◽  
Normah Maan ◽  
Mohd Nadzri Md Reba
Keyword(s):  
West Coast ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Time Series Data ◽  
Short Term Memory ◽  
Harmful Algal Bloom ◽  
Series Data ◽  
Severe Illness ◽  
The West ◽  
Chl A

Harmful algal bloom (HAB) events have alarmed authorities of human health that have caused severe illness and fatalities, death of marine organisms, and massive fish killings. This work aimed to perform the long short-term memory (LSTM) method and convolution neural network (CNN) method to predict the HAB events in the West Coast of Sabah. The results showed that this method could be used to predict satellite time series data in which previous studies only used vector data. This paper also could identify and predict whether there is HAB occurrence in the region. A chlorophyll a concentration (Chl-a; mg/L) variable was used as an HAB indicator, where the data were obtained from MODIS and GEBCO bathymetry. The eight-day dataset interval was from January 2003 to December 2018. The results obtained showed that the LSTM model outperformed the CNN model in terms of accuracy using RMSE and the correlation coefficient r as the statistical criteria.

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.

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