Journal Article10.1007/s11356-023-30774-4
Forecasting water quality variable using deep learning and weighted averaging ensemble models.
Mohammad Ghadir Zamani,Mohammad Reza Nikoo,Sina Jahanshahi,Rahim Barzegar,Amirreza Meydani +4 more
23
TL;DR: The study’s findings demonstrated that the EM-NSGA-II stands out with exceptional effectiveness compared to DL and EM-GA models, showcasing improvements of 14% (RNN), 8% (LSTM), 6% (GRU), 8% (TCN), and 3% (EM-GA) during the testing phase during the testing phase.
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About: This article is published in Environmental Science and Pollution Research. The article was published on 24 Nov 2023.
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Citations
Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities
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TL;DR: Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities focuses on the use of digital twins to manage urban drainage systems. The study demonstrates the potential benefits of digital twins for stormwater management and highlights the need for further research in this field.
14
Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.
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TL;DR: This study proposes a hybrid WT-CNN-GRU model for estimating reservoir water quality variables, incorporating spatio-temporal features, and demonstrates its superior performance over individual ML algorithms, achieving 13% improvement in R-squared and DO evaluation indices.
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Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy
Mohammad Reza Nikoo,Mohammad Zamani,Mahdieh Hosseinjani Zadeh,Ghazi Al-Rawas,Malik Al-Wardy,Amir H. Gandomi +5 more
TL;DR: This study utilizes Sentinel-2 satellite data and Bayesian Maximum Entropy-based Fusion to map water quality indicators (DO and Chl-a) in Oman's Wadi Dayqah Dam reservoir, outperforming individual machine learning models with improved accuracy and efficiency.
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Research progress in water quality prediction based on deep learning technology: a review
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Application of VIC-WUR model for assessing the spatiotemporal distribution of water availability in anthropogenically-impacted basins
Hossein Yousefi Sohi,Banafsheh Zahraie,Faezeh Zebarjadian,Neda Dolatabadi +3 more
TL;DR: This study applies the VIC-WUR model to assess spatiotemporal water availability in the anthropogenically-impacted Divandareh-Bijar basin, Iran, highlighting the importance of considering human impacts on blue and green water resources and quantifying uncertainty for reliable water management.
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