A novel hybrid water quality forecast model based on real-time data decomposition and error correction-Reference-Cited by-同舟云学术

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A novel hybrid water quality forecast model based on real-time data decomposition and error correction

Published:2022-06 Issue: Volume:162 Page:553-565
ISSN:0957-5820
Container-title:Process Safety and Environmental Protection
language:en
Short-container-title:Process Safety and Environmental Protection

Author:

Kim Jusong,Yu Jinwon,Kang Chollyong,Ryang Gwangil,Wei Yanjie,Wang Xiaoli

Funder

Natural Science Foundation of Tianjin Science and Technology Correspondent Project

National Key Research and Development Program of China Stem Cell and Translational Research

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,General Chemical Engineering,Environmental Chemistry,Environmental Engineering

Reference52 articles.

1. On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: case studies of river and lake in USA;Alizamir;J. Clean. Prod.,2021

2. Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model;Barzegar;Stoch. Environ. Res. Risk A,2020

3. Prediction of dissolved oxygen content in aquaculture based on clustering and improved ELM;Cao;IEEE Access,2021

4. A combined model of dissolved oxygen prediction in the pond based on multiple-factor analysis and multi-scale feature extraction;Cao;Aqacult. Eng.,2019

5. A review of the artificial neural network models for water quality prediction;Chen;Appl. Sci.,2020

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1. A long-term dissolved oxygen prediction model in aquaculture using transformer with a dynamic adaptive mechanism;Expert Systems with Applications;2025-01

2. Lite approaches for long-range multi-step water quality prediction;Stochastic Environmental Research and Risk Assessment;2024-08-29

3. Robust kernel extreme learning machines with weighted mean of vectors and variational mode decomposition for forecasting total dissolved solids;Engineering Applications of Artificial Intelligence;2024-07

4. Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection;Environmental Science & Technology;2024-05-06

5. Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features;Journal of Environmental Management;2024-05

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