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.
- Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks A. Garzón et al.
- Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia M. Ahooghalandari et al.
- Online multistep-ahead inundation depth forecasts by recurrent NARX networks H. Shen & L. Chang
- Applicability of Artificial Neural Network in Hydraulic Experiments Using a New Sewer Overflow Screening Device M. Aziz et al.
- Performance of a Fuzzy ARTMAP Artificial Neural Network in Characterizing the Wave Regime at the Port of Sines (Portugal) F. Santos et al.
- Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning S. Wang et al.
- Predicting the urban stormwater drainage system state using the Graph-WaveNet M. Li et al.
- Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things S. Yang & L. Chang
- Long-term seasonal rainfall forecasting using linear and non-linear modelling approaches: a case study for Western Australia I. Hossain et al.
- Enhancement of Urban Drainage System Resilience by Artificial Intelligence: A Comprehensive Review H. Yan et al.
- Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions A. Garzón et al.
- An ensemble data-driven approach for incorporating uncertainty in the forecasting of stormwater sewer surcharge F. Schmid & J. Leandro
- A Data-Driven Multi-Step Flood Inundation Forecast System F. Schmid & J. Leandro
- Hydraulic modeling and deep learning based flow forecasting for optimizing inter catchment wastewater transfer D. Zhang et al.
- A Disaggregation‐Emulation Approach for Optimization of Large Urban Drainage Systems O. Seyedashraf et al.
- Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks Y. Chiang et al.
- Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks . Li-Chiu Chang et al.
- 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.
- Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control F. Chang et al.
- A Feature-Informed Data-Driven Approach for Predicting Maximum Flood Inundation Extends F. Schmid & J. Leandro
- Manage Sewer In-Line Storage Control Using Hydraulic Model and Recurrent Neural Network D. Zhang et al.
- Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al.
- Flood forecasting within urban drainage systems using NARX neural network Y. Abou Rjeily et al.
- Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning Z. Wu et al.
- Artificial Intelligence for Integrated Water Resources Management in Taiwan 斐. 张
- Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information F. Chang et al.
- AI-based design of urban stormwater detention facilities accounting for carryover storage S. Yang et al.
- Storm Water Management Model: Performance Review and Gap Analysis M. Niazi et al.
- 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
- Revolutionizing urban flooding predictions: a segmented deep learning model with fine-tuning update capabilities W. Zhu et al.
- 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.
- Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling F. Chang et al.
- Reinforced recurrent neural networks for multi-step-ahead flood forecasts P. Chen et al.
- Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations A. Najah et al.
- Modelling Intelligent Water Resources Allocation for Multi-users F. Chang et al.
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.
- Transferable and data efficient metamodeling of storm water system nodal depths using auto-regressive graph neural networks A. Garzón et al.
- Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia M. Ahooghalandari et al.
- Online multistep-ahead inundation depth forecasts by recurrent NARX networks H. Shen & L. Chang
- Applicability of Artificial Neural Network in Hydraulic Experiments Using a New Sewer Overflow Screening Device M. Aziz et al.
- Performance of a Fuzzy ARTMAP Artificial Neural Network in Characterizing the Wave Regime at the Port of Sines (Portugal) F. Santos et al.
- Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning S. Wang et al.
- Predicting the urban stormwater drainage system state using the Graph-WaveNet M. Li et al.
- Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things S. Yang & L. Chang
- Long-term seasonal rainfall forecasting using linear and non-linear modelling approaches: a case study for Western Australia I. Hossain et al.
- Enhancement of Urban Drainage System Resilience by Artificial Intelligence: A Comprehensive Review H. Yan et al.
- Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions A. Garzón et al.
- An ensemble data-driven approach for incorporating uncertainty in the forecasting of stormwater sewer surcharge F. Schmid & J. Leandro
- A Data-Driven Multi-Step Flood Inundation Forecast System F. Schmid & J. Leandro
- Hydraulic modeling and deep learning based flow forecasting for optimizing inter catchment wastewater transfer D. Zhang et al.
- A Disaggregation‐Emulation Approach for Optimization of Large Urban Drainage Systems O. Seyedashraf et al.
- Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks Y. Chiang et al.
- Reinforced Two-Step-Ahead Weight Adjustment Technique for Online Training of Recurrent Neural Networks . Li-Chiu Chang et al.
- 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.
- Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control F. Chang et al.
- A Feature-Informed Data-Driven Approach for Predicting Maximum Flood Inundation Extends F. Schmid & J. Leandro
- Manage Sewer In-Line Storage Control Using Hydraulic Model and Recurrent Neural Network D. Zhang et al.
- Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al.
- Flood forecasting within urban drainage systems using NARX neural network Y. Abou Rjeily et al.
- Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning Z. Wu et al.
- Artificial Intelligence for Integrated Water Resources Management in Taiwan 斐. 张
- Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information F. Chang et al.
- AI-based design of urban stormwater detention facilities accounting for carryover storage S. Yang et al.
- Storm Water Management Model: Performance Review and Gap Analysis M. Niazi et al.
- 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
- Revolutionizing urban flooding predictions: a segmented deep learning model with fine-tuning update capabilities W. Zhu et al.
- 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.
- Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling F. Chang et al.
- Reinforced recurrent neural networks for multi-step-ahead flood forecasts P. Chen et al.
- Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations A. Najah et al.
- Modelling Intelligent Water Resources Allocation for Multi-users F. Chang et al.
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