Nothing Special   »   [go: up one dir, main page]

Zheng et al., 2021 - Google Patents

Prediction of harmful algal blooms in large water bodies using the combined EFDC and LSTM models

Zheng et al., 2021

Document ID
13768418080121271027
Author
Zheng L
Wang H
Liu C
Zhang S
Ding A
Xie E
Li J
Wang S
Publication year
Publication venue
Journal of Environmental Management

External Links

Snippet

Harmful algal blooms (HABs) is a worldwide water environmental problem. HABs usually happens in short time and is difficult to be controlled. Early warning of HABs using data- driven models is prospective in making time for taking precaution against HABs. High …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/18—Water

Similar Documents

Publication Publication Date Title
Zheng et al. Prediction of harmful algal blooms in large water bodies using the combined EFDC and LSTM models
Li et al. Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China
Lu et al. Hybrid decision tree-based machine learning models for short-term water quality prediction
Rousso et al. A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes
Pyo et al. Using convolutional neural network for predicting cyanobacteria concentrations in river water
Fang et al. Employing extreme value theory to establish nutrient criteria in bay waters: a case study of Xiangshan Bay
Huo et al. Using artificial neural network models for eutrophication prediction
Chung et al. The influence of physical and physiological processes on the spatial heterogeneity of a Microcystis bloom in a stratified reservoir
Pyo et al. Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage
Kim et al. Augmentation of limited input data using an artificial neural network method to improve the accuracy of water quality modeling in a large lake
Nieto et al. Hybrid PSO–SVM-based method for long-term forecasting of turbidity in the Nalón river basin: A case study in Northern Spain
Kim et al. Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach
Egbueri et al. Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios
Li et al. Forecasting short‐term cyanobacterial blooms in Lake Taihu, China, using a coupled hydrodynamic–algal biomass model
Rostam et al. A complete proposed framework for coastal water quality monitoring system with algae predictive model
Wen et al. Harmful algal bloom warning based on machine learning in maritime site monitoring
Shan et al. Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network
Zhu et al. A hybrid decomposition and Machine learning model for forecasting Chlorophyll-a and total nitrogen concentration in coastal waters
Liu et al. Modelling the effect of weather conditions on cyanobacterial bloom outbreaks in Lake Dianchi: a rough decision-adjusted logistic regression model
Wei et al. Spatial distribution of nutrient loads and thresholds in large shallow lakes: The case of Chaohu Lake, China
Shin et al. Quantifying the contribution of external loadings and internal hydrodynamic processes to the water quality of Lake Okeechobee
García-Nieto et al. A new predictive model for evaluating chlorophyll-a concentration in Tanes reservoir by using a Gaussian process regression
Yang et al. Assessing the Landscape Ecological Health (LEH) of Wetlands: Research Content and Evaluation Methods (2000–2022)
Mitra et al. Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal
Kim et al. Three-dimensional augmentation for hyperspectral image data of water quality: an integrated approach using machine learning and numerical models