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In this study, we conduct a comprehensive history matching study for the FluidFlower benchmark model. This benchmark was prepared and organized by the University of Bergen, the University of Stuttgart, and Massachusetts Institute of Technology, for promoting understanding of the complex physics of geological carbon storage (GCS) through in-house experiments and numerical simulations. This paper synthesizes the experiences of history matching the benchmark data encountered by the Delft-DARTS and CSIRO participants. History matching is first performed based on a low-dimensional-zonated structured model using a simple Poisson-like solver. The permeability of six facies and two faults is inferred in this stage to match the digitized concentration data. The history matching is then further enhanced to richer spatial and physical models to capture the spatial variation of permeability and buoyancy effects, using an unstructured grid. Efficient adjoint methods are used to evaluate the gradient used in the optimization of data misfits or equivalent Bayesian log-likelihoods. With efficient optimization methods available for both maximum a posteriori model inference and Randomized Maximum Likelihood methods for model uncertainty, we perform history matching using both binary and continuous concentration observations. The results show that the tracer plumes in the enriched model match the experimental plumes more accurately compared with the results from the parsimonious-zonated model. The history matching results based on the concentration observations provide more similar plume shapes compared with the case based on the binary observations. The permeability difference between the model before and after history matching reveals that the tracer plume zone and the high permeable zone are the regions of high sensitivity in terms of data misfit between the model response and observations. Surprisingly, CO 2 concentration plume forecasts based on these history-matche, DOI 10.1007/s11242-024-02065-0 correction: In the original published article, one of the affliation details for corresponding author "Denis Voskov" were incorrectly given as "Department of Energy Resources Engineering, Stanford University, CA, USA" but should have been "Department of Energy Science and Engineering, Stanford University, CA, USA. It is updated in the correction, Reservoir Engineering

In this study, we present an efficient and flexible adjoint-based framework for history matching and forecasting geothermal energy extraction at a large scale. In this framework, we applied the Principal Component Analysis to reduce the parameter space for representing the complex geological model. The adjoint method is implemented for gradient calculation to speed up the history-matching iteration process. Operator-based linearization (OBL) used in this framework makes the calculation of the physical state and its derivatives very efficient and facilitates the matrix assembly in the adjoint method. This study primarily focuses on history matching based on combined observation of well production and in-situ electromagnetic measurements to predict the temperature front. However, different types of misfit terms can be added to the objective function based on practical considerations. For example, our history-matching case studies include model misfit terms applied for regularization purposes. The measurement data is extracted from the true model, and realistic measurement errors are considered. Also, in this work, we propose an optimal weighting strategy for the terms of the objective function to balance their sensitivity with respect to the model control variables. The high efficiency of the framework is demonstrated for the geothermal doublet model implemented at the heterogeneous reservoir with multiple realizations. The framework allows for generating posterior Randomized Maximum Likelihood (RML) estimates of the entire ensemble of the realizations with a reasonable computational cost. Results show that the framework can achieve reliable history-matching results based on the doublets production data and the reservoir electromagnetic measurement., Reservoir Engineering

In this paper, we present an efficient inverse modeling framework for energy transition applications. The key feature of this framework is a combination of adjoint gradients and Operator-based Linearization (OBL) technique to achieve high efficiency in inverse modeling based on forward simulations. This framework allows conducting the history matching of practical industrial applications using the gradient descent method with considerable model control variables in a reasonable time. Generally, the inverse modeling of industrial applications involves large amounts of gradient calculations in algorithms based on gradient descent. In this study, we analytically compute the gradient using the adjoint gradient method as an alternative to the widely used numerical gradient method where many time-consuming forward simulation runs are needed. In the adjoint gradient approach, the objective function is linearly combined with the governing equation by introducing a Lagrange multiplier. That allows for finding the analytical gradient in a backward manner. The developed adjoint gradient method takes full advantage of the OBL efficiency and flexibility when assembling the Jacobian and some relevant derivatives. We demonstrate the applications of the proposed inverse modeling framework to different energy transition applications, including petroleum production, extraction of geothermal energy, and CO2 storage. We demonstrate various treatments of objective function definitions, well controls, and measurement errors for these industrial applications. For petroleum production, the proposed framework is tested on the multiphase multi-component flow problem, which is illustrated by an example of data-driven Discrete Well Affinity model. For this application, only production data is considered. The geothermal problem involves an additional energy balance equation and various property calculations for water and steam. In this application, together with the production dat, Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Reservoir Engineering

This study investigates the application of inverse modeling in numerical geo-energy scenarios such as petroleum, geothermal, and CCS projects. The study aims to enhance model accuracy and predictive capabilities for real-world applications. The focus lies on the implementation of the inverse modeling framework within the open-source simulator called Delft Advanced Research Terra Simulator (DARTS), developed using the adjoint method and Operator-Based Linearization (OBL) to assemble derivatives efficiently. The adjoint method's efficiency and analytical gradient solution make it a preferred choice for gradient evaluation in inverse modeling. The work transitions from forward simulation to inverse modeling, elucidating the objective function definition, optimization theory, and the adjoint method's process. Prototype development in MATLAB and its translation to C++ are presented, showcasing the method's superiority. Application examples, like the data-driven proxy model and energy transition projects, demonstrate the framework's versatility and effectiveness in handling diverse observations and solving complex energy transition challenges., Reservoir Engineering

In this work, we describe our decisions made to perform the FluidFlower simulation study and discuss various aspects of the benchmark that are different from our usual subsurface simulation practice. We will discuss the impact of various modeling choices on the outcomes of the simulation models, such as gridding, discretization, and solver strategies, and the lessons learned, taking into account the different conditions of the FluidFlower study compared to conditions commonly dealt with in subsurface simulation. We will start with a brief description of the DARTS framework utilized for compositional simulation, the thermodynamic and physical modeling related to the atmospheric CO2 -brine system, and the modeling workflow used in our benchmark submission. Additionally, we describe a custom nonlinear solver developed for the atmospheric benchmark conditions to improve convergence including the linear solver strategy since our default two-stage preconditioner does not perform effectively. To make meaningful comparisons between each of the modeling choices, we define a baseline model which is a simplified version of our setup in the main FluidFlower benchmark. The baseline model is then used to study the effect of Cartesian and unstructured meshes and a two-point flux approximation compared with a multi-point flux approximation for capturing the physics at play. We conclude our work with lessons learned and future recommendations., Reservoir Engineering

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the “FluidFlower”), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations., Applied Geology, Reservoir Engineering, Numerical Analysis

In this study, we present a history matching framework for oil production forecast based on synthetic and real production data developed using the stochastic Discrete Well Affinity (DiWA) model. With the increase in the complexity of the geological model and the uncertainty in the geological data, it becomes more difficult (sometimes infeasible) to conduct model inversion and production based on conventional technique. To address this problem, we proposed a stochastic DiWA model with unstructured low-resolution mesh to represent the location of wells and reservoir fluid dynamics. With this method, we can efficiently train the forward model based on production data and a stochastic ensemble of property realization. The performance of forward evaluation benefits from the Operator-Based Linearization (OBL) technique and the adjoint method for gradient calculation. Before the model training, a large ensemble size of stochastic DiWA models is generated based on the permeability statistics of the real reservoir, and those models are then filtered using the misfit between the true production data and the DiWA model response. The filtered models have the best fit with the production history of the real reservoir, while they also contain the basic geological information of the real field. The proposed method is tested first on a synthetic data ensemble for production forecast and then applied to a real field. Based on real observations, we use the DiWA model for data quality diagnostic and identify certain flaws in the collected data and model assumptions. Based on these findings, the original assumptions and data observations have been adjusted and the resulting DiWA model was successfully trained. The prediction quality of the trained DiWA model is comparable to conventional simulation techniques based on detailed geological models and has the advantage of a much more efficient and faster ability to update and maintain the subsurface model when continuous updates in produc, Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Reservoir Engineering

In this paper, we describe adjoint gradient formulation for the Operator-Based Linearization modeling approach. Adjoint gradients are implemented in Delft Advanced Research Terra Simulator (DARTS) framework and applied for history matching using a proxy methodology. Due to the application of adjoint gradients, the computational efficiency of the discrete well affinity (DiWA) proxy model for production forecast is significantly improved. That allows us to derive several important extensions. The proxy methodology is further extended and validated for 3D three-phase black-oil problems. The results show that the gradient-based regression can provide good history matching and reconstruct a true petrophysical characterization when the initial guess is generated based on highly reliable geological information. For cases with a limited or not sufficient geological characterization, an efficient stochastic application of DiWA proxy model is proposed. This approach consists of massive sampling procedures for collecting different realizations based on high-fidelity statistics with filtering. These realizations are generated stochastically because they are not conditioned to any production information but the basic geological statistics of the reservoir. The trained DiWA proxy model demonstrates a small deviation between the model response and the observation data. When applying the refined DiWA model for the training, the error between the model response and observation data can be further reduced. The forecast based on the trained model has slightly larger variability but the deviation is still reasonable. The enhanced DiWA methodology presents an efficient and robust technique for creating an ensemble of stochastic proxy models that can be used in production forecast, flow diagnostic, and optimization., Reservoir Engineering

Health risk assessment of environmental exposure to pathogens requires complete and up to date knowledge. With the rapid growth of scientific publications and the protocolization of literature reviews, an automated approach based on Artificial Intelligence (AI) techniques could help extract meaningful information from the literature and make literature reviews more efficient. The objective of this research was to determine whether it is feasible to extract both qualitative and quantitative information from scientific publications about the waterborne pathogen Legionella on PubMed, using Deep Learning and Natural Language Processing techniques. The model effectively extracted the qualitative and quantitative characteristics with high precision, recall and F-score of 0.91, 0.80, and 0.85 respectively. The AI extraction yielded results that were comparable to manual information extraction. Overall, AI could reliably extract both qualitative and quantitative information about Legionella from scientific literature. Our study paved the way for a better understanding of the information extraction processes and is a first step towards harnessing AI to collect meaningful information on pathogen characteristics from environmental microbiology publications., Sanitary Engineering, Water Resources

A physics-based data-driven model is proposed for forecasting of subsurface energy production. The model fully relies on production data and does not require any in-depth knowledge of reservoir geology or governing physics. In the proposed approach, we use the Delft Advanced Reservoir Terra Simulator (DARTS) as a workhorse for data-driven simulation. DARTS uses an operator-based linearization technique that exploits an abstract interpretation of physics benefiting computational performance. The physics-based data-driven model is trained to fit data increasing the fidelity of the model forecast and reflecting significant changes in reservoir dynamics or physics over its history. The model is examined and validated for both synthetic and real field production data. We demonstrate that the developed approach is capable of providing accurate and reliable production forecast on a daily basis, even if the exact geological information is not available., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Petroleum Engineering

A physics-based data-driven model is proposed for forecasting of subsurface energy production. The model fully relies on production data and does not require any in-depth knowledge of reservoir geology or governing physics. In the proposed approach, we use the Delft Advanced Reservoir Terra Simulator (DARTS) as a workhorse for data-driven simulation. DARTS uses an operator-based linearization technique that exploits an abstract interpretation of physics benefiting computational performance. The physics-based data-driven model is trained to fit data increasing the fidelity of the model forecast and reflecting significant changes in reservoir dynamics or physics over its history. The model is examined and validated for both synthetic and real field production data. We demonstrate that the developed approach is capable of providing accurate and reliable production forecast on a daily basis, even if the exact geological information is not available., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Reservoir Engineering

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 influen, Accepted Author Manuscript, Water Resources

Inter-basin water transfers (IBWT) are implemented to re-allocate unevenly distributed water resources. However, many conflicting objectives associated with society, economy, and environment have made the water resources allocation problem in IBWT more complicated than ever before. Thus, there is a continuous need for in-depth research with the latest optimization techniques to secure many-objective allocation of water resources for IBWT. In addition, being troubled of easily falling into local minima and premature convergence in some multi-objective optimization algorithms, it is necessary to explore new alternatives to improve their search quality. Here we propose a many-objective optimization methodology for IBWT, which includes three modules: (1) formulating a many-objective optimization problem based on realistic controls; (2) developing a new multi-objective real-coded quantum inspired shuffled frog leaping algorithm (r-MQSFLA) to solve the optimization problem; (3) utilizing the Analytic Hierarchy Process (AHP)-Entropy method to filter the Pareto solutions. In r-MQSFLA, the real-coded quantum computer and the external archive with dynamic updating mechanism are applied to SFLA. The performance of r-MQSFLA is first compared to that of other multi-objective evolutionary algorithms (MOEAs) in solving mathematical benchmark problems. A case study of the Eastern Route of South-to-North Water Transfer Project in Jiangsu Province, China varying from a normal to an extremely dry year, demonstrates that r-MQSFLA displays approximate performance on some compared algorithms and is improved significantly than MOSFLA in terms of convergence, diversity and reasonable solutions. This study can update the understanding of quantum theory to MOEAs and will provide a reference for better water resources allocation in IBWT under uncertainty., Water Resources

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., Water Resources

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., Hydraulic Structures and Flood Risk, Water Resources

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., Water Resources

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., Water Resources

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 <20% and >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., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

Inter-basin water transfers (IBWT) are implemented to re-allocate unevenly distributed water resources. However, many conflicting objectives associated with society, economy, and environment have made the water resources allocation problem in IBWT more complicated than ever before. Thus, there is a continuous need for in-depth research with the latest optimization techniques to secure many-objective allocation of water resources for IBWT. In addition, being troubled of easily falling into local minima and premature convergence in some multi-objective optimization algorithms, it is necessary to explore new alternatives to improve their search quality. Here we propose a many-objective optimization methodology for IBWT, which includes three modules: (1) formulating a many-objective optimization problem based on realistic controls; (2) developing a new multi-objective real-coded quantum inspired shuffled frog leaping algorithm (r-MQSFLA) to solve the optimization problem; (3) utilizing the Analytic Hierarchy Process (AHP)-Entropy method to filter the Pareto solutions. In r-MQSFLA, the real-coded quantum computer and the external archive with dynamic updating mechanism are applied to SFLA. The performance of r-MQSFLA is first compared to that of other multi-objective evolutionary algorithms (MOEAs) in solving mathematical benchmark problems. A case study of the Eastern Route of South-to-North Water Transfer Project in Jiangsu Province, China varying from a normal to an extremely dry year, demonstrates that r-MQSFLA displays approximate performance on some compared algorithms and is improved significantly than MOSFLA in terms of convergence, diversity and reasonable solutions. This study can update the understanding of quantum theory to MOEAs and will provide a reference for better water resources allocation in IBWT under uncertainty., Water Resources

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 influen, Accepted Author Manuscript, Water Resources

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., Water Resources

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., Hydraulic Structures and Flood Risk, Water Resources

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., Water Resources

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., Water Resources

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 <20% and >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., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

FloodCitiSense aims at developing an urban pluvial flood early warning service for, but also by citizens and city authorities, building upon the state-of-the-art knowledge, methodologies and smart technologies provided by research units and private companies. FloodCitiSense targets the co-creation of this innovative public service in an urban living lab context with all local actors. This service will reduce the vulnerability of urban areas and citizens to pluvial floods, which occur when heavy rainfall exceeds the capacity of the urban drainage system. Due to their fast onset and localized nature, they cause significant damage to the urban environment and are challenging to manage. Monitoring and management of peak events in cities is typically in the hands of local governmental agencies. Citizens most often just play a passive role as people negatively affected by the flooding, despite the fact that they are often the ‘first responders’ and should therefore be actively involved. The FloodCitiSense project aims at integrating crowdsourced hydrological data, collaboratively monitored by local stakeholders, including citizens, making use of low-cost sensors and web-based technologies, into a flood early warning system. This will enable ‘citizens and cities’ to be better prepared for and better respond to urban pluvial floods. Three European pilot cities are targeted: Brussels – Belgium, Rotterdam – The Netherlands and Birmingham – UK., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

Operational water resources management needs to adopt operational strategies to re-allocate water resources by manipulating hydraulic structures. Model Predictive Control (MPC) has been shown to be a promising technique in this context. However, we still need to advance MPC in the face of hydrological uncertainties. This study makes the first attempt to combine Multi-Scenario MPC (MSMPC) with a Genetic Algorithm (GA) to find Pareto optimal solutions for a multi-scenario operational water resources management problem. Then three performance metrics are adopted to select the solution to be implemented. In order to assess the performance of the proposed approach, a case study of the North Sea Canal in the Netherlands is carried out, in which ensemble discharge forecasts are used. Compared with classic MSMPC approaches that deal with uncertainty by the weighted sum approach, GA-MSMPC can better fulfill management goals although it may also be computationally expensive. With the rapid development of multi-objective evolutionary algorithms, our study suggests the potential of GA-MSMPC to deal with a wide range of operational water management problems in the future., Accepted Author Manuscript, Water Resources, Transport Engineering and Logistics

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., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources, Atmospheric Remote Sensing

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 feedf, Water Resources

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., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

FloodCitiSense aims at developing an urban pluvial flood early warning service for, but also by citizens and city authorities, building upon the state-of-the-art knowledge, methodologies and smart technologies provided by research units and private companies. FloodCitiSense targets the co-creation of this innovative public service in an urban living lab context with all local actors. This service will reduce the vulnerability of urban areas and citizens to pluvial floods, which occur when heavy rainfall exceeds the capacity of the urban drainage system. Due to their fast onset and localized nature, they cause significant damage to the urban environment and are challenging to manage. Monitoring and management of peak events in cities is typically in the hands of local governmental agencies. Citizens most often just play a passive role as people negatively affected by the flooding, despite the fact that they are often the ‘first responders’ and should therefore be actively involved. The FloodCitiSense project aims at integrating crowdsourced hydrological data, collaboratively monitored by local stakeholders, including citizens, making use of low-cost sensors and web-based technologies, into a flood early warning system. This will enable ‘citizens and cities’ to be better prepared for and better respond to urban pluvial floods. Three European pilot cities are targeted: Brussels – Belgium, Rotterdam – The Netherlands and Birmingham – UK., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

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., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources, Atmospheric Remote Sensing

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 feedf, Water Resources

Operational water resources management needs to adopt operational strategies to re-allocate water resources by manipulating hydraulic structures. Model Predictive Control (MPC) has been shown to be a promising technique in this context. However, we still need to advance MPC in the face of hydrological uncertainties. This study makes the first attempt to combine Multi-Scenario MPC (MSMPC) with a Genetic Algorithm (GA) to find Pareto optimal solutions for a multi-scenario operational water resources management problem. Then three performance metrics are adopted to select the solution to be implemented. In order to assess the performance of the proposed approach, a case study of the North Sea Canal in the Netherlands is carried out, in which ensemble discharge forecasts are used. Compared with classic MSMPC approaches that deal with uncertainty by the weighted sum approach, GA-MSMPC can better fulfill management goals although it may also be computationally expensive. With the rapid development of multi-objective evolutionary algorithms, our study suggests the potential of GA-MSMPC to deal with a wide range of operational water management problems in the future., Accepted Author Manuscript, Water Resources, Transport Engineering and Logistics

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., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

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., Water Resources

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., Water Resources

Architecture and The Built Environment, Urbanism

Architecture and The Built Environment, Urbanism

Water is needed everywhere to satisfy domestic, agricultural and industrial water demands, to maintain navigation systems, and to preserve healthy and sustainable ecosystems. In order to protect us from floods and to reallocate water resources in a man-made environment, the 'hardware', water-related structures, is constructed in many water systems. However, the 'software', suitable control approaches to operationally maintain and regulate these structures, is still a challenge. Model Predictive Control, standing out from various control technologies, has shown its powerful ability to address multiple objectives, large-scale systems, distributed systems and long-term prediction problems in the field of operational water resources management. This thesis aims to answer the following main research question: How can we apply generic Model Predictive Control and its extensions to satisfy operational objectives in water resources management? As the first element of Model Predictive Control, an internal model needs to be formulated to calculate system states at present and in the future. The linearized Saint Venant equations can well describe hydrodynamics of open water systems, which fit a linear internal model. Second, a quadratic objective function needs to be formulated based on actual management goals, which usually require the water level or discharge to be kept around a pre-defined setpoint. Last, physical and operational limitations are considered as constraints in the optimization problem, such as the height of the riverbank or the capacity of the pump. By describing a water resources management problem in terms of one of the elements, Model Predictive Control is able to address problems of flood management, water supply, irrigation and so on. Chapter 5 applies the standard formulation of Model Predictive Control to flood management in the Dutch water system. Instead of continued reinforcing and heightening water-related structures, Model Predictive Control shows, Water Management, Civil Engineering and Geosciences

Water is needed everywhere to satisfy domestic, agricultural and industrial water demands, to maintain navigation systems, and to preserve healthy and sustainable ecosystems. In order to protect us from floods and to reallocate water resources in a man-made environment, the 'hardware', water-related structures, is constructed in many water systems. However, the 'software', suitable control approaches to operationally maintain and regulate these structures, is still a challenge. Model Predictive Control, standing out from various control technologies, has shown its powerful ability to address multiple objectives, large-scale systems, distributed systems and long-term prediction problems in the field of operational water resources management. This thesis aims to answer the following main research question: How can we apply generic Model Predictive Control and its extensions to satisfy operational objectives in water resources management? As the first element of Model Predictive Control, an internal model needs to be formulated to calculate system states at present and in the future. The linearized Saint Venant equations can well describe hydrodynamics of open water systems, which fit a linear internal model. Second, a quadratic objective function needs to be formulated based on actual management goals, which usually require the water level or discharge to be kept around a pre-defined setpoint. Last, physical and operational limitations are considered as constraints in the optimization problem, such as the height of the riverbank or the capacity of the pump. By describing a water resources management problem in terms of one of the elements, Model Predictive Control is able to address problems of flood management, water supply, irrigation and so on. Chapter 5 applies the standard formulation of Model Predictive Control to flood management in the Dutch water system. Instead of continued reinforcing and heightening water-related structures, Model Predictive Control shows, Water Management, Civil Engineering and Geosciences

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved., Marine and Transport Technology, Mechanical, Maritime and Materials Engineering

Model predictive control (MPC) is a powerful tool which is used more and more to managing water systems such as reservoirs over a short-term prediction horizon. However, due to unknown disturbances present in the water system and other uncertainties, there is always a mismatch between the model and the actual system. To overcome this mismatch and achieve offset free control of the water system, the internal model of the MPC is updated by adding the disturbance dynamics of the actual system by means of a disturbance model. In this paper, the conditions to achieve offset free control for an open water reach are provided. A disturbance model is designed and used to achieve offset free control in a test canal assessed from simulation results., Water Resources

Water Management, Civil Engineering and Geosciences

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved., Marine and Transport Technology, Mechanical, Maritime and Materials Engineering

Model predictive control (MPC) is a powerful tool which is used more and more to managing water systems such as reservoirs over a short-term prediction horizon. However, due to unknown disturbances present in the water system and other uncertainties, there is always a mismatch between the model and the actual system. To overcome this mismatch and achieve offset free control of the water system, the internal model of the MPC is updated by adding the disturbance dynamics of the actual system by means of a disturbance model. In this paper, the conditions to achieve offset free control for an open water reach are provided. A disturbance model is designed and used to achieve offset free control in a test canal assessed from simulation results., Water Resources

Water Management, Civil Engineering and Geosciences

The Dutch Lower Rhine Delta, a transitional area between the Rivers Rhine, Meuse and the North Sea, is at risk of flooding induced by infrequent events of storm surges or fluvial floods, or the combination of both. To protect the delta from storm surges, it can be closed off from the sea by large dams and controllable storm surge barriers. Also, along the branches of the rivers controllable floodgates are operated to regulate the fluvial discharge. A former study quantified the flood frequency derived from three different sources that potentially may cause a flood and indicated that high water levels was mainly caused by the simultaneous occurrence of storm surges and Rhine floods. In the present water operational management system, the Haringvliet gates and the Maeslant Storm Surge Barrier with the Hartel Storm Surge Barrier should be closed in time when the simultaneous extreme event occurs, and therefore the extreme fluvial flow that accumulates during the closure would result in a very high water level within the delta area. Moreover, this frequency will increase significantly in the context of climate change. As a suggested adaptation measure, a controllable floodgate is proposed in Pannerdensch Canal and the other three floodgates in Merwede, Drechtse Kil and Spui are designed in the East and South of Rotterdam and Dordrecht. These floodgates are expected to decrease the potential extreme water levels which are driven by the simultaneous extreme events. This study will investigate the operational management of these four gates, and further apply a large number of scenarios of the simultaneous extreme event to estimate the effect on the flood frequency in the delta. The results can assist to make better decisions in the adaptation of the present operational water management system., Hydraulic Engineering, Civil Engineering and Geosciences

The Dutch Lower Rhine Delta, a transitional area between the Rivers Rhine, Meuse and the North Sea, is at risk of flooding induced by infrequent events of storm surges or fluvial floods, or the combination of both. To protect the delta from storm surges, it can be closed off from the sea by large dams and controllable storm surge barriers. Also, along the branches of the rivers controllable floodgates are operated to regulate the fluvial discharge. A former study quantified the flood frequency derived from three different sources that potentially may cause a flood and indicated that high water levels was mainly caused by the simultaneous occurrence of storm surges and Rhine floods. In the present water operational management system, the Haringvliet gates and the Maeslant Storm Surge Barrier with the Hartel Storm Surge Barrier should be closed in time when the simultaneous extreme event occurs, and therefore the extreme fluvial flow that accumulates during the closure would result in a very high water level within the delta area. Moreover, this frequency will increase significantly in the context of climate change. As a suggested adaptation measure, a controllable floodgate is proposed in Pannerdensch Canal and the other three floodgates in Merwede, Drechtse Kil and Spui are designed in the East and South of Rotterdam and Dordrecht. These floodgates are expected to decrease the potential extreme water levels which are driven by the simultaneous extreme events. This study will investigate the operational management of these four gates, and further apply a large number of scenarios of the simultaneous extreme event to estimate the effect on the flood frequency in the delta. The results can assist to make better decisions in the adaptation of the present operational water management system., Hydraulic Engineering, Civil Engineering and Geosciences

With the development of projector technology, portable projectors called pico-projectors are emerging on the market. To achieve a high quality projection, multiple pico-projectors can be used to display the same video, where accurate synchronization is required. The target of this thesis is to design and implement a Linux-based application which can synchronize multimedia playback over a WPAN with high accuracy. By analyzing the synchronization requirements, the synchronization problem is decomposed into playback synchronization and clock synchronization. To achieve a synchronized playback based on synchronized clocks, we explore several open source projects and eventually build a synchronized player which can play video with frame-by-frame level synchronization. For clock synchronization, the principles of a typical computer clock and the most popular time synchronization – NTP are studied. Based on that, a new time synchronization protocol with quick-start and anti-interference properties is designed. We also conduct a survey to test if the application meets the users’ requirement of synchronized playback. The final results show that our application provides a synchronized playback with the accuracy which is acceptable for most users., Master Electrical Engineering Track Telecommunications 2008, Telecommunications, Electrical Engineering, Mathematics and Computer Science