Articles | Volume 14, issue 7
https://doi.org/10.5194/hess-14-1309-2010
© Author(s) 2010. 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-14-1309-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
16 Jul 2010
Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites
Yen-Ming Chiang
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan
Li-Chiu Chang
Department of Water Resources and Environmental Engineering, Tamkang University, Taipei, Taiwan
Meng-Jung Tsai
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan
Yi-Fung Wang
Water Resources Agency, Ministry of Economic Affairs, Taipei, Taiwan
Fi-John Chang
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan
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Cited
36 citations as recorded by crossref.
- Evaluating the contribution of multi-model combination to streamflow hindcasting by empirical and conceptual models Y. Chiang et al. https://doi.org/10.1080/02626667.2017.1330543
- Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks A. Garzón et al. https://doi.org/10.1016/j.watres.2024.122396
- Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia M. Ahooghalandari et al. https://doi.org/10.1080/13241583.2015.1116183
- Online multistep-ahead inundation depth forecasts by recurrent NARX networks H. Shen & L. Chang https://doi.org/10.5194/hess-17-935-2013
- Applicability of Artificial Neural Network in Hydraulic Experiments Using a New Sewer Overflow Screening Device M. Aziz et al. https://doi.org/10.7158/13241583.2013.11465421
- Performance of a Fuzzy ARTMAP Artificial Neural Network in Characterizing the Wave Regime at the Port of Sines (Portugal) F. Santos et al. https://doi.org/10.2112/JCOASTRES-D-15-00044.1
- Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning S. Wang et al. https://doi.org/10.1016/j.watres.2024.122614
- Predicting the urban stormwater drainage system state using the Graph-WaveNet M. Li et al. https://doi.org/10.1016/j.scs.2024.105877
- Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things S. Yang & L. Chang https://doi.org/10.3390/w12061578
- Long-term seasonal rainfall forecasting using linear and non-linear modelling approaches: a case study for Western Australia I. Hossain et al. https://doi.org/10.1007/s00703-019-00679-4
- Enhancement of Urban Drainage System Resilience by Artificial Intelligence: A Comprehensive Review H. Yan et al. https://doi.org/10.1021/acsestengg.5c00700
- Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions A. Garzón et al. https://doi.org/10.1029/2021WR031808
- An ensemble data-driven approach for incorporating uncertainty in the forecasting of stormwater sewer surcharge F. Schmid & J. Leandro https://doi.org/10.1080/1573062X.2023.2240309
- A Data-Driven Multi-Step Flood Inundation Forecast System F. Schmid & J. Leandro https://doi.org/10.3390/forecast6030039
- Hydraulic modeling and deep learning based flow forecasting for optimizing inter catchment wastewater transfer D. Zhang et al. https://doi.org/10.1016/j.jhydrol.2017.11.029
- A Disaggregation‐Emulation Approach for Optimization of Large Urban Drainage Systems O. Seyedashraf et al. https://doi.org/10.1029/2020WR029098
- Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks Y. Chiang et al. https://doi.org/10.5194/hess-15-185-2011
- Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks . Li-Chiu Chang et al. https://doi.org/10.1109/TNNLS.2012.2200695
- An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system W. Zhu et al. https://doi.org/10.5194/hess-27-2035-2023
- Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control F. Chang et al. https://doi.org/10.1016/j.jhydrol.2014.06.013
- A Feature-Informed Data-Driven Approach for Predicting Maximum Flood Inundation Extends F. Schmid & J. Leandro https://doi.org/10.3390/geosciences13120384
- Manage Sewer In-Line Storage Control Using Hydraulic Model and Recurrent Neural Network D. Zhang et al. https://doi.org/10.1007/s11269-018-1919-3
- Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al. https://doi.org/10.1016/j.ejrh.2023.101471
- Flood forecasting within urban drainage systems using NARX neural network Y. Abou Rjeily et al. https://doi.org/10.2166/wst.2017.409
- Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning Z. Wu et al. https://doi.org/10.1109/ACCESS.2020.3017277
- Artificial Intelligence for Integrated Water Resources Management in Taiwan 斐. 张 https://doi.org/10.12677/JWRR.2013.25045
- Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information F. Chang et al. https://doi.org/10.1016/j.jhydrol.2013.11.011
- AI-based design of urban stormwater detention facilities accounting for carryover storage S. Yang et al. https://doi.org/10.1016/j.jhydrol.2019.06.009
- Storm Water Management Model: Performance Review and Gap Analysis M. Niazi et al. https://doi.org/10.1061/JSWBAY.0000817
- A Novel Machine Learning Based Cluster-Then-Forecast Framework for Sensor Placement Optimization and Real-Time Water Level Prediction in Low Impact Development (LID) Stormwater System E. Chen & X. Yu https://doi.org/10.1021/acsestwater.4c01277
- Revolutionizing urban flooding predictions: a segmented deep learning model with fine-tuning update capabilities W. Zhu et al. https://doi.org/10.1007/s11069-025-07891-z
- Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China Z. Xie et al. https://doi.org/10.1007/s11069-015-1648-3
- Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling F. Chang et al. https://doi.org/10.1016/j.jhydrol.2013.07.008
- Reinforced recurrent neural networks for multi-step-ahead flood forecasts P. Chen et al. https://doi.org/10.1016/j.jhydrol.2013.05.038
- Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations A. Najah et al. https://doi.org/10.5194/hess-15-2693-2011
- Modelling Intelligent Water Resources Allocation for Multi-users F. Chang et al. https://doi.org/10.1007/s11269-016-1229-6
36 citations as recorded by crossref.
- Evaluating the contribution of multi-model combination to streamflow hindcasting by empirical and conceptual models Y. Chiang et al. https://doi.org/10.1080/02626667.2017.1330543
- Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks A. Garzón et al. https://doi.org/10.1016/j.watres.2024.122396
- Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia M. Ahooghalandari et al. https://doi.org/10.1080/13241583.2015.1116183
- Online multistep-ahead inundation depth forecasts by recurrent NARX networks H. Shen & L. Chang https://doi.org/10.5194/hess-17-935-2013
- Applicability of Artificial Neural Network in Hydraulic Experiments Using a New Sewer Overflow Screening Device M. Aziz et al. https://doi.org/10.7158/13241583.2013.11465421
- Performance of a Fuzzy ARTMAP Artificial Neural Network in Characterizing the Wave Regime at the Port of Sines (Portugal) F. Santos et al. https://doi.org/10.2112/JCOASTRES-D-15-00044.1
- Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning S. Wang et al. https://doi.org/10.1016/j.watres.2024.122614
- Predicting the urban stormwater drainage system state using the Graph-WaveNet M. Li et al. https://doi.org/10.1016/j.scs.2024.105877
- Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things S. Yang & L. Chang https://doi.org/10.3390/w12061578
- Long-term seasonal rainfall forecasting using linear and non-linear modelling approaches: a case study for Western Australia I. Hossain et al. https://doi.org/10.1007/s00703-019-00679-4
- Enhancement of Urban Drainage System Resilience by Artificial Intelligence: A Comprehensive Review H. Yan et al. https://doi.org/10.1021/acsestengg.5c00700
- Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions A. Garzón et al. https://doi.org/10.1029/2021WR031808
- An ensemble data-driven approach for incorporating uncertainty in the forecasting of stormwater sewer surcharge F. Schmid & J. Leandro https://doi.org/10.1080/1573062X.2023.2240309
- A Data-Driven Multi-Step Flood Inundation Forecast System F. Schmid & J. Leandro https://doi.org/10.3390/forecast6030039
- Hydraulic modeling and deep learning based flow forecasting for optimizing inter catchment wastewater transfer D. Zhang et al. https://doi.org/10.1016/j.jhydrol.2017.11.029
- A Disaggregation‐Emulation Approach for Optimization of Large Urban Drainage Systems O. Seyedashraf et al. https://doi.org/10.1029/2020WR029098
- Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks Y. Chiang et al. https://doi.org/10.5194/hess-15-185-2011
- Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks . Li-Chiu Chang et al. https://doi.org/10.1109/TNNLS.2012.2200695
- An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system W. Zhu et al. https://doi.org/10.5194/hess-27-2035-2023
- Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control F. Chang et al. https://doi.org/10.1016/j.jhydrol.2014.06.013
- A Feature-Informed Data-Driven Approach for Predicting Maximum Flood Inundation Extends F. Schmid & J. Leandro https://doi.org/10.3390/geosciences13120384
- Manage Sewer In-Line Storage Control Using Hydraulic Model and Recurrent Neural Network D. Zhang et al. https://doi.org/10.1007/s11269-018-1919-3
- Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al. https://doi.org/10.1016/j.ejrh.2023.101471
- Flood forecasting within urban drainage systems using NARX neural network Y. Abou Rjeily et al. https://doi.org/10.2166/wst.2017.409
- Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning Z. Wu et al. https://doi.org/10.1109/ACCESS.2020.3017277
- Artificial Intelligence for Integrated Water Resources Management in Taiwan 斐. 张 https://doi.org/10.12677/JWRR.2013.25045
- Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information F. Chang et al. https://doi.org/10.1016/j.jhydrol.2013.11.011
- AI-based design of urban stormwater detention facilities accounting for carryover storage S. Yang et al. https://doi.org/10.1016/j.jhydrol.2019.06.009
- Storm Water Management Model: Performance Review and Gap Analysis M. Niazi et al. https://doi.org/10.1061/JSWBAY.0000817
- A Novel Machine Learning Based Cluster-Then-Forecast Framework for Sensor Placement Optimization and Real-Time Water Level Prediction in Low Impact Development (LID) Stormwater System E. Chen & X. Yu https://doi.org/10.1021/acsestwater.4c01277
- Revolutionizing urban flooding predictions: a segmented deep learning model with fine-tuning update capabilities W. Zhu et al. https://doi.org/10.1007/s11069-025-07891-z
- Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China Z. Xie et al. https://doi.org/10.1007/s11069-015-1648-3
- Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling F. Chang et al. https://doi.org/10.1016/j.jhydrol.2013.07.008
- Reinforced recurrent neural networks for multi-step-ahead flood forecasts P. Chen et al. https://doi.org/10.1016/j.jhydrol.2013.05.038
- Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations A. Najah et al. https://doi.org/10.5194/hess-15-2693-2011
- Modelling Intelligent Water Resources Allocation for Multi-users F. Chang et al. https://doi.org/10.1007/s11269-016-1229-6
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