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 Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion

2020 ◽  
Vol 12 (2) ◽  
pp. 34
Author(s):  
Xiaofan Wang ◽  
Lingyu Xu
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Short Term Memory ◽  
Sudden Change ◽  
Back Propagation ◽  
Red Tides ◽  
Model Errors ◽  
Chl A ◽  
Error Forecast ◽  
Concentration Prediction

Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.

scholarly journals Tracking Atlantic bluefin tuna from foraging grounds off the west coast of Ireland

2020 ◽  
Vol 77 (6) ◽  
pp. 2066-2077
Author(s):  
Thomas W Horton ◽  
Barbara A Block ◽  
Alan Drumm ◽  
Lucy A Hawkes ◽  
Macdara O’Cuaig ◽  
...  
Keyword(s):  
North Atlantic ◽  
West Coast ◽  
Time Series Data ◽  
Fork Length ◽  
Bluefin Tuna ◽  
Series Data ◽  
Future Research ◽  
The West ◽  
Northeast Atlantic ◽  
Atlantic Bluefin Tuna

Abstract Pop-up archival tags (n = 16) were deployed on Atlantic bluefin tuna (ABT) off the west coast of Ireland in October and November 2016 (199–246 cm curved fork length), yielding 2799 d of location data and 990 and 989 d of depth and temperature time-series data, respectively. Most daily locations (96%, n = 2651) occurred east of 45°W, the current stock management boundary for ABT. Key habitats occupied were the Bay of Biscay and the Central North Atlantic, with two migratory patterns evident: an east-west group and an eastern resident group. Five out of six tags that remained attached until July 2017 returned to the northeast Atlantic after having migrated as far as the Canary Islands, the Mediterranean Sea (MEDI) and the Central North Atlantic. Tracked bluefin tuna exhibited a diel depth-use pattern occupying shallower depths at night and deeper depths during the day. Four bluefin tuna visited known spawning grounds in the central and western MEDI, and one may have spawned, based on the recovered data showing oscillatory dives transecting the thermocline on 15 nights. These findings demonstrate the complexity of the aggregation of ABT off Ireland and, more broadly in the northeast Atlantic, highlighting the need for dedicated future research to conserve this important aggregation.

scholarly journals Population dynamics of harmful algal blooms in Lake Champlain: A tale of two phases

2013 ◽  
Author(s):  
Edmund Hart ◽  
Nicholas Gotelli ◽  
Rebecca Gorney ◽  
Mary Watzin
Keyword(s):  
Population Growth ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Time Series Data ◽  
Growth Parameters ◽  
Series Data ◽  
Nutrient Concentrations ◽  
Nutrient Inputs ◽  
Lake Champlain ◽  
Two Phases

Understanding the dynamics of harmful algal blooms (HABs) in lakes can inform management strategies to reduce their economic and health impacts. Previous studies have analyzed spatially replicated samples from a single time or have fit phenomenological models to time series data. We fit mechanistic population models to test the effects of critical nutrient concentrations and the density of potential algal competitors on population growth parameters in HABs in Lake Champlain, U.S.A. We fit models to five years (2003-2006, 2008) of weekly cyanobacteria counts. Plankton dynamics exhibited two phases of population growth: an initial “bloom phase” of rapid population growth and a subsequent “post-bloom phase” of stochastic decline. Population growth rates in the bloom phase were strongly density dependent and increased with increasing TN:TP ratios. The post-bloom phase was largely stochastic and was not obviously related to nutrient concentrations. Because TN:TP was important only in the initial phase of population growth, correlative analyses of the relationship between cyanobacteria blooms and nutrient concentrations may be especially sensitive to when snapshot data are collected. Limiting nutrient inputs early in the season could be an effective management strategy for suppressing or reducing the bloom phase of cyanobacteria population growth.

scholarly journals Population dynamics of harmful algal blooms in Lake Champlain: A tale of two phases

2013 ◽  
Author(s):  
Edmund Hart ◽  
Nicholas Gotelli ◽  
Rebecca Gorney ◽  
Mary Watzin
Keyword(s):  
Population Growth ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Time Series Data ◽  
Growth Parameters ◽  
Series Data ◽  
Nutrient Concentrations ◽  
Nutrient Inputs ◽  
Lake Champlain ◽  
Two Phases

Understanding the dynamics of harmful algal blooms (HABs) in lakes can inform management strategies to reduce their economic and health impacts. Previous studies have analyzed spatially replicated samples from a single time or have fit phenomenological models to time series data. We fit mechanistic population models to test the effects of critical nutrient concentrations and the density of potential algal competitors on population growth parameters in HABs in Lake Champlain, U.S.A. We fit models to five years (2003-2006, 2008) of weekly cyanobacteria counts. Plankton dynamics exhibited two phases of population growth: an initial “bloom phase” of rapid population growth and a subsequent “post-bloom phase” of stochastic decline. Population growth rates in the bloom phase were strongly density dependent and increased with increasing TN:TP ratios. The post-bloom phase was largely stochastic and was not obviously related to nutrient concentrations. Because TN:TP was important only in the initial phase of population growth, correlative analyses of the relationship between cyanobacteria blooms and nutrient concentrations may be especially sensitive to when snapshot data are collected. Limiting nutrient inputs early in the season could be an effective management strategy for suppressing or reducing the bloom phase of cyanobacteria population growth.

scholarly journals Population dynamics of harmful algal blooms in Lake Champlain: A tale of two phases

2013 ◽  
Author(s):  
Edmund Hart ◽  
Nicholas Gotelli ◽  
Rebecca Gorney ◽  
Mary Watzin
Keyword(s):  
Population Growth ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Time Series Data ◽  
Growth Parameters ◽  
Series Data ◽  
Nutrient Concentrations ◽  
Nutrient Inputs ◽  
Lake Champlain ◽  
Two Phases

Understanding the dynamics of harmful algal blooms (HABs) in lakes can inform management strategies to reduce their economic and health impacts. Previous studies have analyzed spatially replicated samples from a single time or have fit phenomenological models to time series data. We fit mechanistic population models to test the effects of critical nutrient concentrations and the density of potential algal competitors on population growth parameters in HABs in Lake Champlain, U.S.A. We fit models to five years (2003-2006, 2008) of weekly cyanobacteria counts. Plankton dynamics exhibited two phases of population growth: an initial “bloom phase” of rapid population growth and a subsequent “post-bloom phase” of stochastic decline. Population growth rates in the bloom phase were strongly density dependent and increased with increasing TN:TP ratios. The post-bloom phase was largely stochastic and was not obviously related to nutrient concentrations. Because TN:TP was important only in the initial phase of population growth, correlative analyses of the relationship between cyanobacteria blooms and nutrient concentrations may be especially sensitive to when snapshot data are collected. Limiting nutrient inputs early in the season could be an effective management strategy for suppressing or reducing the bloom phase of cyanobacteria population growth.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

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