Articles | Volume 22, issue 5
Hydrol. Earth Syst. Sci., 22, 2987–3006, 2018
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.
Hydrol. Earth Syst. Sci., 22, 2987–3006, 2018
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
Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems
Jason M. Hunter et al.
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Cited
15 citations as recorded by crossref.
- Water quality prediction using SWAT-ANN coupled approach N. Noori et al. 10.1016/j.jhydrol.2020.125220
- 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
- Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques Y. Zhou et al. 10.1016/j.jhydrol.2021.127255
- 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
- An integrated framework of input determination for ensemble forecasts of monthly estuarine saltwater intrusion P. Lu et al. 10.1016/j.jhydrol.2021.126225
- Process‐Guided Deep Learning Predictions of Lake Water Temperature J. Read et al. 10.1029/2019WR024922
- Artificial neural network based hybrid modeling approach for flood inundation modeling S. Xie et al. 10.1016/j.jhydrol.2020.125605
- Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
- 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 review of artificial neural network models for ambient air pollution prediction S. Cabaneros et al. 10.1016/j.envsoft.2019.06.014
- Introductory overview: Optimization using evolutionary algorithms and other metaheuristics H. Maier et al. 10.1016/j.envsoft.2018.11.018
- 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
- Space-time modelling of groundwater level and salinity F. Akter et al. 10.1016/j.scitotenv.2021.145865
15 citations as recorded by crossref.
- Water quality prediction using SWAT-ANN coupled approach N. Noori et al. 10.1016/j.jhydrol.2020.125220
- 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
- Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques Y. Zhou et al. 10.1016/j.jhydrol.2021.127255
- 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
- An integrated framework of input determination for ensemble forecasts of monthly estuarine saltwater intrusion P. Lu et al. 10.1016/j.jhydrol.2021.126225
- Process‐Guided Deep Learning Predictions of Lake Water Temperature J. Read et al. 10.1029/2019WR024922
- Artificial neural network based hybrid modeling approach for flood inundation modeling S. Xie et al. 10.1016/j.jhydrol.2020.125605
- Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
- 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 review of artificial neural network models for ambient air pollution prediction S. Cabaneros et al. 10.1016/j.envsoft.2019.06.014
- Introductory overview: Optimization using evolutionary algorithms and other metaheuristics H. Maier et al. 10.1016/j.envsoft.2018.11.018
- 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
- Space-time modelling of groundwater level and salinity F. Akter et al. 10.1016/j.scitotenv.2021.145865
Latest update: 29 Jun 2022
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...
