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.
Research article
| 
29 Aug 2011
Research article |
| 29 Aug 2011
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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- Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction M. Ehteram et al. 10.1007/s11356-020-10421-y
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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
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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
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- 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
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32 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
- Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction M. Ehteram et al. 10.1007/s11356-020-10421-y
- Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan B. Ziyad Sami et al. 10.1038/s41598-022-06969-z
- 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
- Enhancing sediment transport predictions through machine learning-based multi-scenario regression models M. Abid Almubaidin et al. 10.1016/j.rineng.2023.101585
- Surface water quality status and prediction during movement control operation order under COVID-19 pandemic: Case studies in Malaysia A. Najah et al. 10.1007/s13762-021-03139-y
- Predicting Water Quality Index (WQI) by feature selection and machine learning: A case study of An Kim Hai irrigation system B. Lap et al. 10.1016/j.ecoinf.2023.101991
- 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
- 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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- Water Quality Prediction Model Based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios A. Abobakr Yahya et al. 10.3390/w11061231
- Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy M. Sapitang et al. 10.3390/su12156121
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