Freshwater Algal Bloom Prediction by Extreme Learning Machine in Macau Storage Reservoirs

2014 ◽  
pp. 95-111 ◽  
Author(s):  
Inchio Lou ◽  
Zhengchao Xie ◽  
Wai Kin Ung ◽  
Kai Meng Mok
Keyword(s):  
Extreme Learning Machine ◽  
Algal Bloom ◽  
Storage Reservoirs ◽  
Learning Machine

Freshwater algal bloom prediction by extreme learning machine in Macau Storage Reservoirs

2014 ◽  
Vol 27 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Inchio Lou ◽  
Zhengchao Xie ◽  
Wai Kin Ung ◽  
Kai Meng Mok
Keyword(s):  
Extreme Learning Machine ◽  
Algal Bloom ◽  
Storage Reservoirs ◽  
Learning Machine

scholarly journals Prediction of short-term algal bloom using the M5P model-tree and extreme learning machine

2018 ◽  
Vol 24 (3) ◽  
pp. 404-411 ◽  
Author(s):  
Hye-Suk Yi ◽  
Bomi Lee ◽  
Sangyoung Park ◽  
Keun-Chang Kwak ◽  
Kwang-Guk An
Keyword(s):  
Extreme Learning Machine ◽  
Algal Bloom ◽  
Short Term ◽  
Model Tree ◽  
M5p Model Tree ◽  
Learning Machine

scholarly journals Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea

2018 ◽  
Vol 15 (10) ◽  
pp. 2078 ◽  
Author(s):  
Hye-Suk Yi ◽  
Sangyoung Park ◽  
Kwang-Guk An ◽  
Keun-Chang Kwak
Keyword(s):  
Neural Network ◽  
Chlorophyll A ◽  
Extreme Learning Machine ◽  
Algal Blooms ◽  
Algal Bloom ◽  
Nakdong River ◽  
Concentration Data ◽  
Chlorophyll A Concentration ◽  
Learning Machine ◽  
The Nakdong River

In this study, we design an intelligent model to predict chlorophyll-a concentration, which is the primary indicator of algal blooms, using extreme learning machine (ELM) models. Modeling algal blooms is important for environmental management and ecological risk assessment. For this purpose, the performance of the designed models was evaluated for four artificial weirs in the Nakdong River, Korea. The Nakdong River has harmful annual algal blooms that can affect health due to exposure to toxins. In contrast to conventional neural network (NN) that use backpropagation (BP) learning methods, ELMs are fast learning, feedforward neural networks that use least square estimates (LSE) for regression. The weights connecting the input layer to the hidden nodes are randomly assigned and are never updated. The dataset used in this study includes air temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a concentration, which were collected on a weekly basis from January 2013 to December 2016. Here, upstream chlorophyll-a concentration data was used in our ELM2 model to improve algal bloom prediction performance. In contrast, the ELM1 model only uses downstream chlorophyll-a concentration data. The experimental results revealed that the ELM2 model showed better performance in comparison to the ELM1 model. Furthermore, the ELM2 model showed good prediction and generalization performance compared to multiple linear regression (LR), conventional neural network with backpropagation (NN-BP), and adaptive neuro-fuzzy inference system (ANFIS).

Performance of Extreme Learning Machine Kernels in Classifying EEG Signal Pattern of Dyslexic Children in Writing

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
A. Z. Ahmad Zainuddin ◽  
◽  
W. Mansor ◽  
Khuan Y. Lee ◽  
Z. Mahmoodin ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Eeg Signal ◽  
Signal Pattern ◽  
Learning Machine

Model selection of extreme learning machine based on latent feature space

2013 ◽  
Vol 33 (6) ◽  
pp. 1600-1603
Author(s):  
Wentao MAO ◽  
Zhongtang ZHAO ◽  
Huanhuan HE
Keyword(s):  
Model Selection ◽  
Extreme Learning Machine ◽  
Feature Space ◽  
Learning Machine ◽  
Selection Of

Sistema Híbrido Baseado Em Extreme Learning Machine E Harmony Search

2016 ◽  
Author(s):  
Edgar Wellington Marques de Almeida ◽  
Mêuser Jorge da Silva Valença
Keyword(s):  
Extreme Learning Machine ◽  
Harmony Search ◽  
Learning Machine
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