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Xin Tian
Department of KWR, Water Research Institute, 3433 PE Nieuwegein, The Netherlands

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Journal article
Published: 27 August 2020 in Water
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Recently, a continuous reinforcement learning model called fuzzy SARSA (state, action, reward, state, action) learning (FSL) was proposed for irrigation canals. The main problem related to FSL is its convergence and generalization in environments with many variables such as large irrigation canals and situations beyond training. Furthermore, due to its architecture, FSL may require high computation demands during its learning process. To deal with these issues, this work proposes a computationally lighter generalizing learned Q-function (GLQ) model, which benefits from the FSL-learned Q-function, to provide operators with a faster and simpler mechanism to obtain operational instructions. The proposed approach is tested for different water requests in the East Aghili Canal, located in the southwest of Iran. Several performance indicators are used for evaluating the GLQ model results, showing convergence in all the investigated cases and the ability to estimate operational instructions (actions) in situations beyond training, delivering water with high accuracy regarding several performance indicators. Hence, the use of the GLQ model is recommended for determining the operational patterns in irrigation canals.

ACS Style

Kazem Shahverdi; J. M. Maestre; Farinaz Alamiyan-Harandi; Xin Tian. Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals. Water 2020, 12, 2407 .

AMA Style

Kazem Shahverdi, J. M. Maestre, Farinaz Alamiyan-Harandi, Xin Tian. Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals. Water. 2020; 12 (9):2407.

Chicago/Turabian Style

Kazem Shahverdi; J. M. Maestre; Farinaz Alamiyan-Harandi; Xin Tian. 2020. "Generalizing Fuzzy SARSA Learning for Real-Time Operation of Irrigation Canals." Water 12, no. 9: 2407.

Journal article
Published: 07 August 2020 in Advances in Water Resources
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Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time and duration, so that they can further warn inhabitants in risky areas and take emergency actions when forecasting a pluvial flood. Previous studies that dealt with the prediction of urban pluvial flooding are mainly based on hydrological or hydraulic models, requiring a large volume of data for simulation accuracy. These methods are computationally expensive. Using a rainfall threshold to predict flooding based on a data-driven approach can decrease the computational complexity to a great extent. In order to prepare cities for frequent pluvial flood events – especially in the future climate – this paper uses a rainfall threshold for classifying flood vs. non-flood events, based on machine learning (ML) approaches, applied to a case study of Shenzhen city in China. In doing so, ML models can determine several rainfall threshold lines projected in a plane spanned by two principal components, which provides a binary result (flood or no flood). Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, can classify flooding and non-flooding by different combinations of multiple-resolution rainfall intensities, greatly raising the accuracy to 96.5% and lowering the false alert rate to 25%. Compared to the conventional model, the critical indices of accuracy and true positive rate (TPR) were 5%-15% higher in ML models. Such models are applicable to other urban catchments as well. The results are expected to be used to assist early warning systems and provide rational information for contingency and emergency planning.

ACS Style

Qian Ke; Xin Tian; Jeremy Bricker; Zhan Tian; Guanghua Guan; Huayang Cai; Xinxing Huang; Honglong Yang; Junguo Liu. Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China. Advances in Water Resources 2020, 145, 103719 .

AMA Style

Qian Ke, Xin Tian, Jeremy Bricker, Zhan Tian, Guanghua Guan, Huayang Cai, Xinxing Huang, Honglong Yang, Junguo Liu. Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China. Advances in Water Resources. 2020; 145 ():103719.

Chicago/Turabian Style

Qian Ke; Xin Tian; Jeremy Bricker; Zhan Tian; Guanghua Guan; Huayang Cai; Xinxing Huang; Honglong Yang; Junguo Liu. 2020. "Urban pluvial flooding prediction by machine learning approaches – a case study of Shenzhen city, China." Advances in Water Resources 145, no. : 103719.

Journal article
Published: 03 July 2020 in Water
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This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

ACS Style

Nay Myo Lin; Xin Tian; Martine Rutten; Edo Abraham; José M. Maestre; Nick Van De Giesen. Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System. Water 2020, 12, 1898 .

AMA Style

Nay Myo Lin, Xin Tian, Martine Rutten, Edo Abraham, José M. Maestre, Nick Van De Giesen. Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System. Water. 2020; 12 (7):1898.

Chicago/Turabian Style

Nay Myo Lin; Xin Tian; Martine Rutten; Edo Abraham; José M. Maestre; Nick Van De Giesen. 2020. "Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System." Water 12, no. 7: 1898.

Journal article
Published: 18 March 2020 in Water
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A new Proportional-Integral (PI) tuning method based on Linear Matrix Inequalities (LMIs) is presented. In particular, an LMI-based optimal control problem is solved to obtain a sparse feedback that provides the PI tuning. The ASCE Test Canal 1 is used as a case study. Using a linearised model of the canal, different tunings for the design of the PI controller are developed and tested using the software Sobek. Furthermore, the proposed method is also compared with other tunings proposed for the same canal available in the literature. Our results show that the proposed method reduces by half the maximum errors with respect to other assessed alternatives and minimizes undesired mutual interactions between canal pools. Also, our method improves the optimality degree of the PI tuning by 30%. Therefore, it is concluded that the LMI based PI controllers lead to satisfactory performance in regulating water levels and canal flows/structure outflows, outperforming other tested alternatives, thus becoming a useful tool for irrigation canal control.

ACS Style

Teresa Arauz; José M. Maestre; Xin Tian; Guanghua Guan. Design of PI Controllers for Irrigation Canals Based on Linear Matrix Inequalities. Water 2020, 12, 855 .

AMA Style

Teresa Arauz, José M. Maestre, Xin Tian, Guanghua Guan. Design of PI Controllers for Irrigation Canals Based on Linear Matrix Inequalities. Water. 2020; 12 (3):855.

Chicago/Turabian Style

Teresa Arauz; José M. Maestre; Xin Tian; Guanghua Guan. 2020. "Design of PI Controllers for Irrigation Canals Based on Linear Matrix Inequalities." Water 12, no. 3: 855.

Journal article
Published: 28 December 2019 in Science of The Total Environment
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Climate and land use/cover changes are the main factors altering hydrological regimes. To understand the impacts of climate and land use/cover changes on streamflow within a specific catchment, it is essential to accurately quantify their changes given many possibilities. We propose an integrated framework to assess how individual and combined climate and land use/cover changes impact the streamflow of Xinanjiang Basin, in East China, in the future. Five bias-corrected and downscaled General Circulation Model (GCM) projections are used to indicate the inter-model uncertainties under three Representative Concentration Pathways (RCPs). Additionally, three land use/cover change scenarios representing a range of tradeoffs between ecological protection (EP) and urban development (UD) are projected by Cellular Automata - Markov (CA-Markov). The streamflow in 2021–2050 is then assessed using the calibrated Soil and Water Assessment Tool (SWAT) with 15 scenarios and 75 possibilities. Finally, the uncertainty and attribution of streamflow changes to climate and land use/cover changes at monthly and annual scale are analyzed. Results show that while both land use/cover change alone and combined changes project an increase in streamflow, there is a disagreement on the direction of streamflow change under climate change alone. Future streamflow may undergo a more blurred boundary between the flood and non-flood seasons, potentially easing the operation stress of Xinanjiang Reservoir for water supply or hydropower generation. We find that the impacts of climate and land use/cover changes on monthly mean streamflow are sensitive to the impermeable area (IA). The impacts of climate change are stronger than those induced by land use/cover change under EP (i.e., lower IA); and land use/cover change has a greater impact in case of UD (i.e., higher IA). However, changes in annual mean streamflow are mainly driven by land use/cover change, and climate change may decrease the influence attributed to land use/cover change.

ACS Style

Yuxue Guo; Guohua Fang; Yue-Ping Xu; Xin Tian; Jingkai Xie. Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China. Science of The Total Environment 2019, 710, 136275 .

AMA Style

Yuxue Guo, Guohua Fang, Yue-Ping Xu, Xin Tian, Jingkai Xie. Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China. Science of The Total Environment. 2019; 710 ():136275.

Chicago/Turabian Style

Yuxue Guo; Guohua Fang; Yue-Ping Xu; Xin Tian; Jingkai Xie. 2019. "Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China." Science of The Total Environment 710, no. : 136275.

Journal article
Published: 18 December 2019 in Environmental Modelling & Software
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Rainfall-triggered shallow landslides are widespread natural hazards around the world, causing many damages to human lives and property. In this study, we focused on predicting landslides in a large region by coupling a 1 km-resolution hydrological model and a 90 m-resolution slope stability model, where a downscaling method for soil moisture via topographic wetness index was applied. The modeled hydrological processes show generally good agreements with the observed discharges: relative biases and correlation coefficients at three validation stations are all 0.60, respectively. The derived scaling law for soil moisture allows for near-conservative downscaling of the original 1-km soil moisture to 90-m resolution for slope stability assessment. For landslide prediction, the global accuracy and true positive rate are 97.2% and 66.9%, respectively. This study provides an effective and computationally efficient coupling method to predict landslides over large regions in which fine-scale topographical information is incorporated.

ACS Style

Sheng Wang; Ke Zhang; Ludovicus P.H. van Beek; Xin Tian; Thom Bogaard. Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment. Environmental Modelling & Software 2019, 124, 104607 .

AMA Style

Sheng Wang, Ke Zhang, Ludovicus P.H. van Beek, Xin Tian, Thom Bogaard. Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment. Environmental Modelling & Software. 2019; 124 ():104607.

Chicago/Turabian Style

Sheng Wang; Ke Zhang; Ludovicus P.H. van Beek; Xin Tian; Thom Bogaard. 2019. "Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment." Environmental Modelling & Software 124, no. : 104607.

Journal article
Published: 29 June 2019 in Science of The Total Environment
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Urban pluvial flooding is one of the most costly natural hazards worldwide. Risks of flooding are expected to increase in the future due to global warming and urbanization. The complexity of the involved processes and the lack of long-term field observations means that many crucial aspects related to urban flood risks still remain poorly understood. In this paper, the possibility to gain new insight into urban pluvial flooding using citizen flood observations is explored. Using a ten-year dataset of radar rainfall maps and 70,000 citizen flood reports for the city of Rotterdam, we derive critical thresholds beyond which urban pluvial flooding is likely to occur. Three binary decision trees are trained for predicting flood occurrences based on peak rainfall intensities across different temporal scales. Results show that the decision trees correctly predict 37%–52% of all flood occurrences and 95%–97% of all non-flood occurrences, which is a fair performance given the uncertainties associated with citizen data. More importantly, all models agree on which rainfall features are the most important for predicting flooding, reaching optimal performance whenever short- and long-duration rainfall peak intensities are combined together to make a prediction. Additional feature selection using principal component analysis shows that further improvement is possible when critical rainfall thresholds are calculated using a linear combination of peak rainfall intensities across multiple temporal scales. The encouraging results suggest that citizen observatories, although prone to larger errors and uncertainties, constitute a valuable alternative source of information for gaining insight into urban pluvial flooding.

ACS Style

Xin Tian; Marie-Claire Ten Veldhuis; Marc Schleiss; Christian Bouwens; Nick van de Giesen. Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations. Science of The Total Environment 2019, 689, 258 -268.

AMA Style

Xin Tian, Marie-Claire Ten Veldhuis, Marc Schleiss, Christian Bouwens, Nick van de Giesen. Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations. Science of The Total Environment. 2019; 689 ():258-268.

Chicago/Turabian Style

Xin Tian; Marie-Claire Ten Veldhuis; Marc Schleiss; Christian Bouwens; Nick van de Giesen. 2019. "Critical rainfall thresholds for urban pluvial flooding inferred from citizen observations." Science of The Total Environment 689, no. : 258-268.

Journal article
Published: 23 May 2019 in Water
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In the open channel control algorithm, good feed-forward controllers will reduce the transition time of the canal and improve performance. Feedforward control algorithms based on active storage compensation are greatly affected by delay time. However, there is no literature comparing the three most commonly used algorithms, namely volume step compensation, dynamic wave principle and water balance models, under the operation mode of constant water level downstream. In order to compare the existing three algorithms, and to avoid storage calculation by calculating the constant non-uniform water surface line or identification of relevant parameters, combined with the open channel constant gradient flow theory with the storage compensation algorithm, a delay time explicit algorithm is proposed in this study. Tested on the first canal pool of the American Society of Civil Engineers (ASCE) Test Canal 2, the performance of the delay time explicit algorithm is assessed and compared to that of the three conventional algorithms. In the current water intake plan, i.e. in the second hour, the intake begins to take 1.2 m3/s, and the upstream flow of the canal pool changes from 6 m3/s to 7.2 m3/s, among the three existing algorithms, the volume step compensation algorithm has better performance in terms of time to achieve stability, i.e., 1.25 h. The actual adjusted storage accounts for 99.6% of the target adjusted storage, which can basically meet the requirement of compensated storage of the canal pool. The delay time explicit algorithm only needs 1.47 h to stabilize the regulation system. The fluctuation of water level and discharge in the regulation process is small. The actual adjusted storage accounts for 99.6% of the target adjusted storage, which can basically meet the requirement of compensated storage for the canal pool. The delay time calculated by explicit algorithm can provide references for the determination of delay time in feedforward control.

ACS Style

Wenjun Liao; Guanghua Guan; Xin Tian; Liao; Guan; Tian. Exploring Explicit Delay Time for Volume Compensation in Feedforward Control of Canal Systems. Water 2019, 11, 1080 .

AMA Style

Wenjun Liao, Guanghua Guan, Xin Tian, Liao, Guan, Tian. Exploring Explicit Delay Time for Volume Compensation in Feedforward Control of Canal Systems. Water. 2019; 11 (5):1080.

Chicago/Turabian Style

Wenjun Liao; Guanghua Guan; Xin Tian; Liao; Guan; Tian. 2019. "Exploring Explicit Delay Time for Volume Compensation in Feedforward Control of Canal Systems." Water 11, no. 5: 1080.

Journal article
Published: 20 November 2018 in Environmental Modelling & Software
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Worldwide, delta areas are under stress due to climate change. With rising sea levels and decreasing freshwater availability, surface water salinization due to groundwater exfiltration is expected to increase in these low-lying areas. To counteract surface water salinization, freshwater diverted from rivers is used to flush agricultural ditches. In this paper, we demonstrate a Model Predictive Control (MPC) scheme to control salinity and water levels in a water course while minimizing freshwater usage. A state space description of the discretized De Saint Venant and advection-dispersion equations for water and salt transport, respectively, is used as the internal model of the controller. The developed MPC scheme is tested using groundwater exfiltration data from two different representative Dutch polders. The tests demonstrate that water levels and salinity concentrations can successfully be controlled within set limits while minimizing the freshwater used.

ACS Style

Boran Ekin Aydin; Xin Tian; Joost Delsman; Gualbert H.P. Oude Essink; Martine Rutten; Edo Abraham. Optimal salinity and water level control of water courses using Model Predictive Control. Environmental Modelling & Software 2018, 112, 36 -45.

AMA Style

Boran Ekin Aydin, Xin Tian, Joost Delsman, Gualbert H.P. Oude Essink, Martine Rutten, Edo Abraham. Optimal salinity and water level control of water courses using Model Predictive Control. Environmental Modelling & Software. 2018; 112 ():36-45.

Chicago/Turabian Style

Boran Ekin Aydin; Xin Tian; Joost Delsman; Gualbert H.P. Oude Essink; Martine Rutten; Edo Abraham. 2018. "Optimal salinity and water level control of water courses using Model Predictive Control." Environmental Modelling & Software 112, no. : 36-45.

Article
Published: 08 November 2018 in Water Resources Management
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A real-time control scheme informed by a streamflow forecast is presented for the optimal operation of water resources systems composed of multiple and spatially distributed systems, affected by hydroclimatic disturbances. The approach uses a two-layer scenario-based hierarchical and distributed model predictive controller (HD-MPC) to deal with the operational water management problem under dynamical uncertainty. The higher layer collects and coordinates forecast information, which is rendered into possible realizations of the uncertainties and sent to the local agents. The lower layer solves a distributed optimization problem related to the actual management objectives. The HD-MPC method is demonstrated through a simulation of the North Sea Canal system as a real-world case study. The results show the benefits of the proposed compared to over other types of MPC controllers.

ACS Style

P. Velarde; Xin Tian; A. D. Sadowska; J. M. Maestre. Scenario-Based Hierarchical and Distributed MPC for Water Resources Management with Dynamical Uncertainty. Water Resources Management 2018, 33, 677 -696.

AMA Style

P. Velarde, Xin Tian, A. D. Sadowska, J. M. Maestre. Scenario-Based Hierarchical and Distributed MPC for Water Resources Management with Dynamical Uncertainty. Water Resources Management. 2018; 33 (2):677-696.

Chicago/Turabian Style

P. Velarde; Xin Tian; A. D. Sadowska; J. M. Maestre. 2018. "Scenario-Based Hierarchical and Distributed MPC for Water Resources Management with Dynamical Uncertainty." Water Resources Management 33, no. 2: 677-696.

Journal article
Published: 01 October 2018 in Water
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Managing a multi-reservoir system is complicated, due to conflicting interests among various objectives. This study proposes an optimization-based approach for the operations of a multi-reservoir system. An advanced real-time control technique, Model Predictive Control (MPC), is adopted to control a multi-reservoir system with two control objectives, i.e., flood mitigation and water conservation. The case study area is the Sittaung River basin in Myanmar, where the current reservoir operating rule needs to be improved for a more effective operation. A comparison between an MPC-based operation and the current operation is presented by using performance indicators. The result shows a reduction of the system’s vulnerability by 0.9 percent using MPC. Due to the physical constraint of the reservoirs, it is impossible to completely eliminate the flood risk at Taungoo City during high inflow events. However, the results indicate that the potential flood risk can be mitigated by improving the current operating rule.

ACS Style

Nay Myo Lin; Martine Rutten; Xin Tian. Flood Mitigation through Optimal Operation of a Multi-Reservoir System by Using Model Predictive Control: A Case Study in Myanmar. Water 2018, 10, 1371 .

AMA Style

Nay Myo Lin, Martine Rutten, Xin Tian. Flood Mitigation through Optimal Operation of a Multi-Reservoir System by Using Model Predictive Control: A Case Study in Myanmar. Water. 2018; 10 (10):1371.

Chicago/Turabian Style

Nay Myo Lin; Martine Rutten; Xin Tian. 2018. "Flood Mitigation through Optimal Operation of a Multi-Reservoir System by Using Model Predictive Control: A Case Study in Myanmar." Water 10, no. 10: 1371.

Articles
Published: 09 August 2018 in Urban Water Journal
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Water utilities often rely on industrial water supply (e.g. desalination) to complement natural resources. These climate-independent sources of supply allow operators to respond quickly to varying operating conditions, but require them to choose operating strategies, or rules. How does such operational flexibility impact the performance of water supply systems? How might it affect long-term plans for capacity expansion? Possibly significantly, as demonstrated by the analysis of a water supply system based on Singapore. First, we simulate the dynamics of the system under multiple rainfall and operating scenarios to understand the extent to which the operators’ behavior affect system performance. Results show that different operating rules can have comparable impact on the variability in system performance as hydrological conditions. Then, we show that small changes in the operating rules can lead to substantial changes in the capacity expansions, such as the size of a new desalination plant.

ACS Style

Xin Tian; Stefano Galelli; Richard De Neufville. Impact of operating rules on planning capacity expansion of urban water supply systems. Urban Water Journal 2018, 15, 654 -661.

AMA Style

Xin Tian, Stefano Galelli, Richard De Neufville. Impact of operating rules on planning capacity expansion of urban water supply systems. Urban Water Journal. 2018; 15 (7):654-661.

Chicago/Turabian Style

Xin Tian; Stefano Galelli; Richard De Neufville. 2018. "Impact of operating rules on planning capacity expansion of urban water supply systems." Urban Water Journal 15, no. 7: 654-661.

Journal article
Published: 01 November 2017 in Advances in Water Resources
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ACS Style

Xin Tian; Rudy R. Negenborn; Peter-Jules van Overloop; José María Maestre; Anna Sadowska; Nick van de Giesen. Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts. Advances in Water Resources 2017, 109, 58 -68.

AMA Style

Xin Tian, Rudy R. Negenborn, Peter-Jules van Overloop, José María Maestre, Anna Sadowska, Nick van de Giesen. Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts. Advances in Water Resources. 2017; 109 ():58-68.

Chicago/Turabian Style

Xin Tian; Rudy R. Negenborn; Peter-Jules van Overloop; José María Maestre; Anna Sadowska; Nick van de Giesen. 2017. "Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts." Advances in Water Resources 109, no. : 58-68.

Journal article
Published: 01 March 2017 in Journal of Irrigation and Drainage Engineering
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Open water systems such as irrigation canals are used to transport and deliver water from the source to the user. Water loss in these systems by seepage, leakage, evaporation, or unknown water offtakes can be large. If this loss is unknown to the model used, it will not be considered by the controller and create a real system model mismatch. This mismatch will affect the water level directly and create an offset from the reference set point of the water level. A control configuration for open water canals, model predictive control (MPC) based on moving horizon estimation (MHE-MPC), to deal with offset problems resulting from real system-model mismatch is described in this paper. MHE uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and systematically removes the offset in the controlled water level. This control configuration is numerically tested on an accurate hydrodynamic model of the Control Algorithms Test Canal of the Technical University of Catalonia (UPC-PAC). The results presented in this paper show that MHE-MPC can realize offset-free control and the results are better than those of the well-known disturbance modelling offset-free control scheme.

ACS Style

Boran Ekin Aydin; P. J. Van Overloop; Martine Rutten; Xin Tian. Offset-Free Model Predictive Control of an Open Water Channel Based on Moving Horizon Estimation. Journal of Irrigation and Drainage Engineering 2017, 143, 1 .

AMA Style

Boran Ekin Aydin, P. J. Van Overloop, Martine Rutten, Xin Tian. Offset-Free Model Predictive Control of an Open Water Channel Based on Moving Horizon Estimation. Journal of Irrigation and Drainage Engineering. 2017; 143 (3):1.

Chicago/Turabian Style

Boran Ekin Aydin; P. J. Van Overloop; Martine Rutten; Xin Tian. 2017. "Offset-Free Model Predictive Control of an Open Water Channel Based on Moving Horizon Estimation." Journal of Irrigation and Drainage Engineering 143, no. 3: 1.

Journal article
Published: 01 March 2017 in Journal of Irrigation and Drainage Engineering
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A variety of methods are in use for the design of controllers for adjusting canal gate positions to maintain a constant water level immediately upstream from check gates. These methods generally rely on a series of tests on the water level’s response to changes in canal gate position or flow, either by simulation or on the canal itself. This paper presents a method for tuning these controllers based on wave celerity through use of the integrator delay zero (IDZ) model. These equations can be used to determine the resonance peak height and resonance frequency. Unsteady-flow canal simulation models are used to show the response of controller design using these theoretical equations with a test case for ASCE Test Canal 1. A novel method is presented for avoiding disturbance amplification by considering the delay times in all canal pools downstream.

ACS Style

A. J. Clemmens; Xinmin Tian; P.-J. Van Overloop; Xavier Litrico. Integrator Delay Zero Model for Design of Upstream Water-Level Controllers. Journal of Irrigation and Drainage Engineering 2017, 143, 1 .

AMA Style

A. J. Clemmens, Xinmin Tian, P.-J. Van Overloop, Xavier Litrico. Integrator Delay Zero Model for Design of Upstream Water-Level Controllers. Journal of Irrigation and Drainage Engineering. 2017; 143 (3):1.

Chicago/Turabian Style

A. J. Clemmens; Xinmin Tian; P.-J. Van Overloop; Xavier Litrico. 2017. "Integrator Delay Zero Model for Design of Upstream Water-Level Controllers." Journal of Irrigation and Drainage Engineering 143, no. 3: 1.

Journal article
Published: 01 March 2017 in Journal of Irrigation and Drainage Engineering
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Model predictive control (MPC) is one of the most popular control techniques that has been widely used in many fields of water resources management, such as canal control for drainage, irrigation, and navigation. MPC uses an internal mathematical model to describe system dynamics over a given prediction horizon and then minimizes a hard-constrained optimization problem based on actual objectives. Due to the use of hard constraints, the optimization problem may occasionally be infeasible. A compromise is sometimes made to look for a feasible solution by softening the hard constraints, which means that the limit on water levels or flows is allowed to be violated to a certain extent. For example, water in a canal may go above the top of a dike during a high-discharge event, resulting in a spill. This amount of spilling water leaves the water system and does not flow back, which therefore should be deducted in the mathematical model of the water system. To deal with this spill, past studies often utilized a hybrid model and an integer optimization. However, the system in the hybrid model is usually nonlinear and nonsmooth, especially when it transits from one discrete state to another. In this paper, an alternative way is proposed to link the spill with the softened constraint, still maintaining the linearity of the water system. Results show that the proposed way to tackle the spilling water is easy to implement and the water level is more accurately regulated around the setpoint in a canal control problem.

ACS Style

Xin Tian; Boran Ekin Aydin; Rudy R. Negenborn; Nick Van De Giesen; José María Maestre. Model Predictive Control for Water Level Control in the Case of Spills. Journal of Irrigation and Drainage Engineering 2017, 143, 1 .

AMA Style

Xin Tian, Boran Ekin Aydin, Rudy R. Negenborn, Nick Van De Giesen, José María Maestre. Model Predictive Control for Water Level Control in the Case of Spills. Journal of Irrigation and Drainage Engineering. 2017; 143 (3):1.

Chicago/Turabian Style

Xin Tian; Boran Ekin Aydin; Rudy R. Negenborn; Nick Van De Giesen; José María Maestre. 2017. "Model Predictive Control for Water Level Control in the Case of Spills." Journal of Irrigation and Drainage Engineering 143, no. 3: 1.

Book chapter
Published: 01 January 2015 in Operations Research/Computer Science Interfaces Series
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The Netherlands lies in the delta area, which is formed by the Rivers Rhine, Meuse and Scheldt. Being a low-lying country, dikes and other water-retaining structures have been constructed for the purposes of flood protection (transport of water), water supply (transport of water), and navigation (transport over water). All of these objectives are important within the total operational water management. In order to achieve these objectives and make them explicit, we propose a water management approach in which each goal is addressed specifically by a term in a cost function. We assume one centralized Model Predictive Controller, which can determine the balance among the different objectives, as the control strategy for determining which actions to take when controlling the Dutch water system, especially in droughts. Simulation experiments are used to illustrate the potential of this approach under different scenarios in the dry season.

ACS Style

X. Tian; R. R. Negenborn; P. J. van Overloop; J. M. Maestre; E. Mostert. Model Predictive Control for Incorporating Transport of Water and Transport over Water in the Dry Season. Operations Research/Computer Science Interfaces Series 2015, 191 -210.

AMA Style

X. Tian, R. R. Negenborn, P. J. van Overloop, J. M. Maestre, E. Mostert. Model Predictive Control for Incorporating Transport of Water and Transport over Water in the Dry Season. Operations Research/Computer Science Interfaces Series. 2015; ():191-210.

Chicago/Turabian Style

X. Tian; R. R. Negenborn; P. J. van Overloop; J. M. Maestre; E. Mostert. 2015. "Model Predictive Control for Incorporating Transport of Water and Transport over Water in the Dry Season." Operations Research/Computer Science Interfaces Series , no. : 191-210.

Journal article
Published: 03 November 2014 in Advances in Water Resources
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The safety of low-lying deltas is threatened not only by riverine flooding but by storm-induced coastal flooding as well. For the purpose of flood control, these deltas are mostly protected in a man-made environment, where dikes, dams and other adjustable infrastructures, such as gates, barriers and pumps are widely constructed. Instead of always reinforcing and heightening these structures, it is worth considering making the most of the existing infrastructure to reduce the damage and manage the delta in an operational and overall way. In this study, an advanced real-time control approach, Model Predictive Control, is proposed to operate these structures in the Dutch delta system (the Rhine–Meuse delta). The application covers non-linearity in the dynamic behavior of the water system and the structures. To deal with the non-linearity, a linearization scheme is applied which directly uses the gate height instead of the structure flow as the control variable. Given the fact that MPC needs to compute control actions in real-time, we address issues regarding computational time. A new large time step scheme is proposed in order to save computation time, in which different control variables can have different control time steps. Simulation experiments demonstrate that Model Predictive Control with the large time step setting is able to control a delta system better and much more efficiently than the conventional operational schemes.

ACS Style

Xin Tian; Peter-Jules Van Overloop; Rudy R. Negenborn; Nick Van De Giesen. Operational flood control of a low-lying delta system using large time step Model Predictive Control. Advances in Water Resources 2014, 75, 1 -13.

AMA Style

Xin Tian, Peter-Jules Van Overloop, Rudy R. Negenborn, Nick Van De Giesen. Operational flood control of a low-lying delta system using large time step Model Predictive Control. Advances in Water Resources. 2014; 75 ():1-13.

Chicago/Turabian Style

Xin Tian; Peter-Jules Van Overloop; Rudy R. Negenborn; Nick Van De Giesen. 2014. "Operational flood control of a low-lying delta system using large time step Model Predictive Control." Advances in Water Resources 75, no. : 1-13.

Conference paper
Published: 01 April 2013 in 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC)
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Safety and navigation are two important elements in the multi-objective water management of the Lake IJssel area in The Netherlands including Lake IJssel, Lake Marker and North Sea Canal. In order to maintain these important stakes and to make them explicit, transport over water is treated as individual agent that has to negotiate its objectives within the total operational water management. In this paper, dual decomposition is used in a distributed model predictive controller applied to the main control structures in this area. The controller is evaluated on a realistic scenario simulated on a detailed model of the water system.

ACS Style

Xin Tian; Peter-Jules Van Overloop; Rudy Negenborn; Pepe Maestre Torreblanca. Incorporating transport over water in the multi-objective water management of the Lake IJssel area in The Netherlands. 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC) 2013, 649 -654.

AMA Style

Xin Tian, Peter-Jules Van Overloop, Rudy Negenborn, Pepe Maestre Torreblanca. Incorporating transport over water in the multi-objective water management of the Lake IJssel area in The Netherlands. 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC). 2013; ():649-654.

Chicago/Turabian Style

Xin Tian; Peter-Jules Van Overloop; Rudy Negenborn; Pepe Maestre Torreblanca. 2013. "Incorporating transport over water in the multi-objective water management of the Lake IJssel area in The Netherlands." 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC) , no. : 649-654.