A novel single-parameter approach for forecasting algal blooms
Harmful algal blooms frequently occur globally, and forecasting could constitute an essential
proactive strategy for bloom control. To decrease the cost of aquatic environmental …
proactive strategy for bloom control. To decrease the cost of aquatic environmental …
Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach
The rapid emergence of deep learning long-short-term-memory (LSTM) technique presents
a promising solution to algal bloom forecasting. However, the discontinuous and non …
a promising solution to algal bloom forecasting. However, the discontinuous and non …
Deep-learning-based approach for prediction of algal blooms
Algal blooms have recently become a critical global environmental concern which might put
economic development and sustainability at risk. However, the accurate prediction of algal …
economic development and sustainability at risk. However, the accurate prediction of algal …
Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method
Many countries have attempted to monitor and predict harmful algal blooms to mitigate
related problems and establish management practices. The current alert system-based …
related problems and establish management practices. The current alert system-based …
Improved predictive performance of cyanobacterial blooms using a hybrid statistical and deep-learning method
H Li, C Qin, W He, F Sun, P Du - Environmental Research Letters, 2021 - iopscience.iop.org
Cyanobacterial harmful algal blooms (CyanoHABs) threaten ecosystem functioning and
human health at both regional and global levels, and this threat is likely to become more …
human health at both regional and global levels, and this threat is likely to become more …
Using convolutional neural network for predicting cyanobacteria concentrations in river water
Abstract Machine learning modeling techniques have emerged as a potential means for
predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river …
predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river …
A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir
Understanding the dynamics of harmful algal blooms is important to protect the aquatic
ecosystem in regulated rivers and secure human health. In this study, artificial neural …
ecosystem in regulated rivers and secure human health. In this study, artificial neural …
A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake, China
Abstract Cyanobacterial Harmful Algae Blooms (CyanoHABs) in the eutrophic lakes have
become a global environmental and ecological problem. In this study, a CNN-LSTM …
become a global environmental and ecological problem. In this study, a CNN-LSTM …
Deep learning application to time-series prediction of daily chlorophyll-a concentration
Algal bloom in rivers is a major environmental concern which threatens the stable water
supply and river ecosystem. Due to its complexity and nonlinearity, previous studies have …
supply and river ecosystem. Due to its complexity and nonlinearity, previous studies have …
Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach
Routine monitoring for harmful algal blooms (HABs) is generally undertaken at low temporal
frequency (eg, weekly to monthly) that is unsuitable for capturing highly dynamic variations …
frequency (eg, weekly to monthly) that is unsuitable for capturing highly dynamic variations …