ABSTRACT An artificial neural network (ANN) based rainfall-runoff model was developed to forecast... more ABSTRACT An artificial neural network (ANN) based rainfall-runoff model was developed to forecast flash floods by using satellite-derived forcing data during typhoon periods over Keelung watershed, located on northern Taiwan. The satellite rainfall estimates over Taiwan are generated through PERSIANN CCS at grid of 4 km and temporal resolution of half hour. Validation of satellite estimates with gauge measurements shows the PERSIANN CCS captures the heavy rainfall in terms of trend and peak volume but slightly underestimates the light rainfall, in particular the initial stage of the storm events. The major goal of this study is to investigate the impact of rainfall forcing data from different sources on flood forecasting. First, an ANN flood prediction model was calibrated by using datasets of 13 historical gauge-streamflow events. Second, 6 latest flood events were used to investigate the flood prediction results driven by satellite-derived rainfall and gauge observations, respectively. Finally, the realization of uncertainty quantification was examined by propagating satellite-derived precipitation ensemble into the flood prediction model. Our results exemplify the need for a better representation of satellite-derived precipitation error structure and a detailed investigation of the techniques that propagate the input error into hydrological models.
Summary Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid... more Summary Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.
Agriculture is extremely vulnerable to climate change. Greenhouse farming is recognized as a prom... more Agriculture is extremely vulnerable to climate change. Greenhouse farming is recognized as a promising measure against climate change. Nevertheless, greenhouse farming frequently encounters environmental adversity, especially greenhouses built to protect against typhoons. Short-term microclimate prediction is challenging because meteorological variables are strongly interconnected and change rapidly. Therefore, this study proposes a water-centric smart microclimate-control system (SMCS) that fuses system dynamics and machine-learning techniques in consideration of the internal hydro-meteorological process to regulate the greenhouse micro-environment within the canopy for environmental cooling with improved resource-use efficiency. SMCS was assessed by in situ data collected from a tomato greenhouse in Taiwan. The results demonstrate that the proposed SMCS could save 66.8% of water and energy (electricity) used for early spraying during the entire cultivation period compared to the t...
Taiwan is located in themonsoon zone of the North Pacific Ocean and experiences an average of 4-5... more Taiwan is located in themonsoon zone of the North Pacific Ocean and experiences an average of 4-5 typhoons annually. The particular topography of Taiwan makes rivers short and steep, and thus rivers flow rapidly from catchments to reservoirs within a few hours during typhoon events. This study aims to construct realtime multi-step-ahead reservoir inflow forecast models by using Artificial Neural Networks (ANNs) based on radar rainfall data and reservoir inflow data. The Back PropagationNeural Network (BPNN) and the Recurrent Neural Network (RNN) are adopted for forecasting. Results indicate that the correlation coefficients in the testing phases of both models exceed 0.86 for one- to three-hour-ahead forecasts and exceed 0.69 for six-hour-head forecasts. The RNN model outperforms the BPNN model, which indicates the recurrent property of the RNN can effectively improve forecast accuracy when making several step-ahead forecasts. Results demonstrates that the constructed multi-step-ahe...
The sustainable management of water cycles is crucial in the context of climate change and global... more The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles—as well as urban, agricultural, and industrial water cycles—to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has made notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, non-linear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT...
The frequency of extreme hydrological events varies highly in Taiwan, and increasing attention ha... more The frequency of extreme hydrological events varies highly in Taiwan, and increasing attention has been paid to the optimal reservoir operations. This study establishes an optimization model for watershed management through reservoir operations subject to human and ecosystem needs. The Shihmen Reservoir in Taiwan is used as a case study. This study adopts the Taiwan Eco-hydrological Indicator System (TEIS) to classify river flow patterns. We combine the non-dominated sorting genetic algorithm II (NSGA-II) with the self-organizing radial basis network (SORBN) to develop the optimal model of reservoir operation. The results indicate that it is possible to simultaneously satisfy human and ecosystem needs, where ecosystem diversity can be retained in high SI values (1.7-1.9) while human demands can also be highly satisfied (α higher than 0.85). The proposed approach allows decision makers to easily determine the best compromise in water allocation through the trade-off between human and...
This study proposes a multi-objective optimization model of two cascade reservoirs in the Upper Y... more This study proposes a multi-objective optimization model of two cascade reservoirs in the Upper Yellow River basin for increasing social well-beings in general while simultaneously mitigating ice/flood threats. We first develop a strategy of dimensionality reduction and constraint transformation to largely diminish the complexity of the optimization system and next propose a novel search method that fuses a Feasible Search Space (FSS) into the Particle Swarm Optimization (PSO) algorithm, i.e. FSS-PSO, to effectively solve the optimization problem. To investigate the applicability and effectiveness of the proposed method, this study compares the FSS-PSO model with historical operation. The results indicate that the proposed model produces much better performances in all the objectives than historical operation. To assess the superiority and efficiency of the proposed FSS-PSO, the classical PSO and the Chaos Particle Swarm Optimization (CPSO) are also implemented to compare their comp...
ABSTRACT An artificial neural network (ANN) based rainfall-runoff model was developed to forecast... more ABSTRACT An artificial neural network (ANN) based rainfall-runoff model was developed to forecast flash floods by using satellite-derived forcing data during typhoon periods over Keelung watershed, located on northern Taiwan. The satellite rainfall estimates over Taiwan are generated through PERSIANN CCS at grid of 4 km and temporal resolution of half hour. Validation of satellite estimates with gauge measurements shows the PERSIANN CCS captures the heavy rainfall in terms of trend and peak volume but slightly underestimates the light rainfall, in particular the initial stage of the storm events. The major goal of this study is to investigate the impact of rainfall forcing data from different sources on flood forecasting. First, an ANN flood prediction model was calibrated by using datasets of 13 historical gauge-streamflow events. Second, 6 latest flood events were used to investigate the flood prediction results driven by satellite-derived rainfall and gauge observations, respectively. Finally, the realization of uncertainty quantification was examined by propagating satellite-derived precipitation ensemble into the flood prediction model. Our results exemplify the need for a better representation of satellite-derived precipitation error structure and a detailed investigation of the techniques that propagate the input error into hydrological models.
Summary Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid... more Summary Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.
Agriculture is extremely vulnerable to climate change. Greenhouse farming is recognized as a prom... more Agriculture is extremely vulnerable to climate change. Greenhouse farming is recognized as a promising measure against climate change. Nevertheless, greenhouse farming frequently encounters environmental adversity, especially greenhouses built to protect against typhoons. Short-term microclimate prediction is challenging because meteorological variables are strongly interconnected and change rapidly. Therefore, this study proposes a water-centric smart microclimate-control system (SMCS) that fuses system dynamics and machine-learning techniques in consideration of the internal hydro-meteorological process to regulate the greenhouse micro-environment within the canopy for environmental cooling with improved resource-use efficiency. SMCS was assessed by in situ data collected from a tomato greenhouse in Taiwan. The results demonstrate that the proposed SMCS could save 66.8% of water and energy (electricity) used for early spraying during the entire cultivation period compared to the t...
Taiwan is located in themonsoon zone of the North Pacific Ocean and experiences an average of 4-5... more Taiwan is located in themonsoon zone of the North Pacific Ocean and experiences an average of 4-5 typhoons annually. The particular topography of Taiwan makes rivers short and steep, and thus rivers flow rapidly from catchments to reservoirs within a few hours during typhoon events. This study aims to construct realtime multi-step-ahead reservoir inflow forecast models by using Artificial Neural Networks (ANNs) based on radar rainfall data and reservoir inflow data. The Back PropagationNeural Network (BPNN) and the Recurrent Neural Network (RNN) are adopted for forecasting. Results indicate that the correlation coefficients in the testing phases of both models exceed 0.86 for one- to three-hour-ahead forecasts and exceed 0.69 for six-hour-head forecasts. The RNN model outperforms the BPNN model, which indicates the recurrent property of the RNN can effectively improve forecast accuracy when making several step-ahead forecasts. Results demonstrates that the constructed multi-step-ahe...
The sustainable management of water cycles is crucial in the context of climate change and global... more The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles—as well as urban, agricultural, and industrial water cycles—to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has made notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, non-linear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT...
The frequency of extreme hydrological events varies highly in Taiwan, and increasing attention ha... more The frequency of extreme hydrological events varies highly in Taiwan, and increasing attention has been paid to the optimal reservoir operations. This study establishes an optimization model for watershed management through reservoir operations subject to human and ecosystem needs. The Shihmen Reservoir in Taiwan is used as a case study. This study adopts the Taiwan Eco-hydrological Indicator System (TEIS) to classify river flow patterns. We combine the non-dominated sorting genetic algorithm II (NSGA-II) with the self-organizing radial basis network (SORBN) to develop the optimal model of reservoir operation. The results indicate that it is possible to simultaneously satisfy human and ecosystem needs, where ecosystem diversity can be retained in high SI values (1.7-1.9) while human demands can also be highly satisfied (α higher than 0.85). The proposed approach allows decision makers to easily determine the best compromise in water allocation through the trade-off between human and...
This study proposes a multi-objective optimization model of two cascade reservoirs in the Upper Y... more This study proposes a multi-objective optimization model of two cascade reservoirs in the Upper Yellow River basin for increasing social well-beings in general while simultaneously mitigating ice/flood threats. We first develop a strategy of dimensionality reduction and constraint transformation to largely diminish the complexity of the optimization system and next propose a novel search method that fuses a Feasible Search Space (FSS) into the Particle Swarm Optimization (PSO) algorithm, i.e. FSS-PSO, to effectively solve the optimization problem. To investigate the applicability and effectiveness of the proposed method, this study compares the FSS-PSO model with historical operation. The results indicate that the proposed model produces much better performances in all the objectives than historical operation. To assess the superiority and efficiency of the proposed FSS-PSO, the classical PSO and the Chaos Particle Swarm Optimization (CPSO) are also implemented to compare their comp...
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