Articles | Volume 15, issue 8
https://doi.org/10.5194/hess-15-2693-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-15-2693-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations
A. Najah
PhD Candidate, Dept. Civil & Structural Eng, Universiti Kebangsaan Malaysia, UKM, Malaysia
A. El-Shafie
Lecturers, Dept. Civil & Structural Eng, Universiti Kebangsaan Malaysia, UKM, Malaysia
O. A. Karim
Lecturers, Dept. Civil & Structural Eng, Universiti Kebangsaan Malaysia, UKM, Malaysia
O. Jaafar
Lecturers, Dept. Civil & Structural Eng, Universiti Kebangsaan Malaysia, UKM, Malaysia
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33 citations as recorded by crossref.
- Monitoring diel dissolved oxygen dynamics through integrating wavelet denoising and temporal neural networks F. Evrendilek & N. Karakaya 10.1007/s10661-013-3476-9
- A novel hybrid water quality time series prediction method based on cloud model and fuzzy forecasting W. Deng et al. 10.1016/j.chemolab.2015.09.017
- What will the water quality of the Yangtze River be in the future? W. Dong et al. 10.1016/j.scitotenv.2022.159714
- Predicting Coastal Dissolved Oxygen Values with the Use of Artificial Neural Networks: A Case Study for Cyprus E. Hadjisolomou et al. 10.1088/1755-1315/1123/1/012083
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- GIS and Artificial Neural Network–Based Water Quality Model for a Stream Network in the Upper Green River Basin, Kentucky, USA J. Anmala et al. 10.1061/(ASCE)EE.1943-7870.0000801
- Modeling of Dissolved Oxygen Concentration and Its Hysteresis Behavior in Rivers Using Wavelet Transform‐Based Hybrid Models S. Khani & T. Rajaee 10.1002/clen.201500395
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- Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China X. Wen et al. 10.1007/s10661-012-2874-8
- Hybrid soft computing approach for determining water quality indicator: Euphrates River J. Li et al. 10.1007/s00521-017-3112-7
- Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models M. Fuladipanah et al. 10.1016/j.rineng.2023.101604
- Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran R. Barzegar et al. 10.1007/s00477-016-1213-y
- Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia A. Ahmed et al. 10.1080/19942060.2021.2019128
- Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia S. Latif et al. 10.1007/s10668-023-03882-x
- Optimised neural network model for river-nitrogen prediction utilizing a new training approach P. Kumar et al. 10.1371/journal.pone.0239509
- A data-driven model for water quality prediction in Tai Lake, China, using secondary modal decomposition with multidimensional external features R. Tan et al. 10.1016/j.ejrh.2023.101435
- Estimation of dissolved oxygen using data-driven techniques in the Tai Po River, Hong Kong S. Nemati et al. 10.1007/s12665-015-4450-3
- Response of water quality to land use and sewage outfalls in different seasons J. Xu et al. 10.1016/j.scitotenv.2019.134014
- Coastal forecast through coupling of Artificial Intelligence and hydro-morphodynamical modelling P. Kumar & N. Leonardi 10.1080/21664250.2023.2233724
- Assessment of machine learning model performance for seasonal precipitation simulation based on teleconnection indices in Iran J. Helali et al. 10.1007/s12517-022-10640-2
- Streamflow classification by employing various machine learning models for peninsular Malaysia N. AlDahoul et al. 10.1038/s41598-023-41735-9
- Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review F. Fahimi et al. 10.1007/s00704-016-1735-8
- RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW) A. Ahmed et al. 10.1007/s00521-016-2496-0
- Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system K. Boo et al. 10.1016/j.scitotenv.2023.168760
- Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS) A. Hipni et al. 10.1007/s11269-013-0382-4
- Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia A. El-Shafie et al. 10.5194/hess-16-1151-2012
- Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models P. Kumar et al. 10.3390/su12114359
- Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach M. Rezaie-Balf et al. 10.1016/j.jclepro.2020.122576
- River Water Temperature Prediction Using a Hybrid Model Based on Variational Mode Decomposition (VMD) and Outlier Robust Extreme Learning Machine E. Mirzania et al. 10.1007/s40710-024-00716-4
- Estimates of hydroelectric energy generation in BRICS-T countries using a new hybrid model E. Uzlu 10.1080/15567249.2024.2310094
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