Articles | Volume 22, issue 5
https://doi.org/10.5194/hess-22-2987-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-2987-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 May 2018
Research article |
| 22 May 2018
| 22 May 2018
Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems
Jason M. Hunter
CORRESPONDING AUTHOR
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Holger R. Maier
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Matthew S. Gibbs
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Department of Environment and Water, GPO Box 2384, Adelaide, 5001 SA, Australia
Eloise R. Foale
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Naomi A. Grosvenor
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Nathan P. Harders
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
Tahali C. Kikuchi-Miller
School of Civil, Environmental and Mining Engineering, The University
of Adelaide, Adelaide, 5005 SA, Australia
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- Artificial neural network based hybrid modeling approach for flood inundation modeling S. Xie et al. 10.1016/j.jhydrol.2020.125605
- Machine learning-based estimation of riverine nutrient concentrations and associated uncertainties caused by sampling frequencies S. Chen et al. 10.1371/journal.pone.0271458
- Exploding the myths: An introduction to artificial neural networks for prediction and forecasting H. Maier et al. 10.1016/j.envsoft.2023.105776
- ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients B. Wang et al. 10.5194/gmd-13-4253-2020
- Enhanced Artificial Neural Networks for Salinity Estimation and Forecasting in the Sacramento-San Joaquin Delta of California S. Qi et al. 10.1061/(ASCE)WR.1943-5452.0001445
- A hybrid framework for short-term irrigation demand forecasting L. Forouhar et al. 10.1016/j.agwat.2022.107861
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- An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions H. Chu et al. 10.1016/j.envsoft.2019.104587
- Statistical comparison between SARIMA and ANN’s performance for surface water quality time series prediction X. Wang et al. 10.1007/s11356-021-13086-3
- Water quality modeling in sewer networks: Review and future research directions Y. Jia et al. 10.1016/j.watres.2021.117419
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- Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
- Salt transport in a large agro-urban river basin: Modeling, controlling factors, and management strategies C. Hocking & R. Bailey 10.3389/frwa.2022.945682
- A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq Z. Khudhair et al. 10.1080/23311916.2022.2150121
- The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4−2 surface water quality M. Jamei et al. 10.1016/j.jenvman.2021.113774
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- Review on the Change Trend, Attribution Analysis, Retrieval, Simulation, and Prediction of Lake Surface Water Temperature T. Jia et al. 10.1109/JSTARS.2022.3188788
- Enhancing Water Temperature Prediction in Stratified Reservoirs: A Process-Guided Deep Learning Approach S. Kim & S. Chung 10.3390/w15173096
- 地球表层特征参量反演与模拟的机理<bold>-</bold>学习耦合范式 焕. 沈 & 良. 张 10.1360/SSTe-2022-0089
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Latest update: 20 Nov 2024
Short summary
This research proposes a generalised hybrid model development framework and applies it to a case study of salinity prediction in a reach of the Murray River. The hybrid model combines five sub-models which describe one process of salt entry each and are developed based on the amount of system knowledge and data that are available to support each individual process. The model demonstrates increased performance over two benchmark models and has implications for future model development processes.
This research proposes a generalised hybrid model development framework and applies it to a case...
