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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- Process‐Guided Deep Learning Predictions of Lake Water Temperature J. Read et al. 10.1029/2019WR024922
- Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta Q. Tian et al. 10.3389/fmars.2024.1407690
- Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
- 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
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- A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation S. Chen et al. 10.1016/j.watres.2023.120895
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- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
- Space-time modelling of groundwater level and salinity F. Akter et al. 10.1016/j.scitotenv.2021.145865
- Prediction of river salinity with artificial neural networks M. Kulisz et al. 10.1088/1742-6596/2676/1/012004
35 citations as recorded by crossref.
- Water quality prediction using SWAT-ANN coupled approach N. Noori et al. 10.1016/j.jhydrol.2020.125220
- Prediction of river salinity with artificial neural networks M. Kulisz et al. 10.1088/1742-6596/2676/1/012004
- Metaheuristic Algorithms Applied in ANN Salinity Modelling Z. Khudhair et al. 10.1016/j.rineng.2024.102541
- Iterative integration of deep learning in hybrid Earth surface system modelling M. Chen et al. 10.1038/s43017-023-00452-7
- Filling up the water quality database to assess the water quality levels and self-cleaning capacities H. Bui et al. 10.1088/1755-1315/1349/1/012019
- Performances of Different Machine Learning Algorithms for Predicting Saltwater Intrusion in the Vietnamese Mekong Delta Using Limited Input Data: A Study from Ham Luong River T. Tran et al. 10.1134/S0097807822030198
- Predicting solid waste generation based on the ensemble artificial intelligence models under uncertainty analysis F. Ghanbari et al. 10.1007/s10163-023-01589-9
- Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters F. Wang et al. 10.3390/en17040945
- An integrated framework of input determination for ensemble forecasts of monthly estuarine saltwater intrusion P. Lu et al. 10.1016/j.jhydrol.2021.126225
- 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
- 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
- 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
- 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
- Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network X. Wang et al. 10.1007/s11269-022-03248-4
- Process‐Guided Deep Learning Predictions of Lake Water Temperature J. Read et al. 10.1029/2019WR024922
- Attribution analysis and forecast of salinity intrusion in the Modaomen estuary of the Pearl River Delta Q. Tian et al. 10.3389/fmars.2024.1407690
- Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
- 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
- A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation S. Chen et al. 10.1016/j.watres.2023.120895
- 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
- Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
- Space-time modelling of groundwater level and salinity F. Akter et al. 10.1016/j.scitotenv.2021.145865
1 citations as recorded by crossref.
Latest update: 16 Jul 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...
