1,169 research outputs found
Predicting Storm Surges: Chaos, Computational Intelligence, Data Assimilation, Ensembles
No full textAccurate predictions of storm surge are of importance in many coastal areas. This book focuses on data-driven modelling using methods of nonlinear dynamics and chaos theory for predicting storm surges. A number of new enhancements are presented: phase space dimensionality reduction, incomplete time series, phase error correction, finding true neighbours, optimization of chaotic model, data assimilation and multi-model ensembles. These were tested on the case studies in the North Sea and Caribbean Sea. Chaotic models appear to be are accurate and reliable short and mid-term predictors of storm surges aimed at supporting decision-makers for flood prediction and ship navigation.Water ManagementCivil Engineering and Geoscience
Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling
No full textDue to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of models. One of multi modelling approaches called "committee modelling" is one of the topics in part of this study. Special attention is given to the so-called “fuzzy committee” approach to hydrological modelling. The comparative interpretation of the resulting uncertainty statistics from different sampling schemes (MCS, GLUE, MCMC, SCEMUA, DREAM, PSO, and ACCO) for uncertainty estimations of hydrological model is presented. The uncertainty statistics are considerably depending on the sampling method used. Another aspect of uncertainty analysis relates to predicting uncertainty (rather than its analysis). Machine learning techniques were proposed to build model of probability distribution function as predictive uncertainty models, which allows adequate uncertainty estimation for hydrological models. In flood modelling hydrological models are typically used in combination with hydraulic models forming a cascade, often supported by geospatial processing. SWAT hydrological and SOBEK hydrodynamic models are integrated for uncertainty analysis of flood inundation modelling of the Nzoia catchment (Kenya), and the parametric uncertainty of the hydrological model is allowed to propagate through the model cascade using Monte Carlo simulations, leading to the generation of the probabilistic flood maps. Due to the high computational complexity of these experiments, the high performance (cluster) computing framework is designed and used. Overall, this thesis presents research efforts in: (i) committee modelling of hydrological models, (ii) hybrid committee hydrological models, (iii) influence of sampling strategies on prediction uncertainty of hydrological models, (iv) uncertainty prediction using machine learning techniques, (v) committee of predictive uncertainty models and (vi) uncertainty in flood inundation extent. This study refined a number of hydroinformatics techniques, thus enhancing uncertainty-based hydrological and integrated modelling.Water ManagementCivil Engineering and Geoscience
Framework for Dynamic Modelling of Urban Floods at Different Topographical Resolutions
No full textFloods are among the most frequent and costly natural disasters in terms of human hardship and economic loss. The impacts of flooding are especially devastating in urban areas as these areas are densely populated and contain vital infrastructures. Urban flood risks and their impacts are expected to increase as urban development in flood prone areas continues and as rain intensity increases as a result of climate change while aging drainage infrastructures limit the drainage capacity in existing urban areas. The increased risk and severe consequence of flooding drives the need for the development of costeffective flood mitigation strategies as part of sound urban flood management plans. Efficient prediction of characteristics of flood propagation in urban areas is paramount in developing flood mitigation measures. Urban flood modelling attempts to quantitatively describe the characteristics and evolution of flood flows that occur when a large amount of water moves along drainage systems and urban flood plains.Water ManagementCivil Engineering and Geoscience
Hybrid models for hydrological forecasting: Integration of data-driven and conceptual modelling techniques
No full textThis book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following topics: A classification of different hybrid modelling approaches in the context of flow forecasting. The methodological development and application of modular models based on clustering and baseflow empirical formulations. The integration of hydrological conceptual models with neural network error corrector models and the use of committee models for daily streamflow forecasting. The application of modular modelling and fuzzy committee models to the problem of downscaling weather information for hydrological forecasting.WatermanagementCivil Engineering and Geoscience
Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model
No full textThis study aims at proposing novel approaches for integrating qualitative flow observations in a lumped hydrologic routing model and assessing their usefulness for improving flood estimation. Routing is based on a three-parameter Muskingum model used to propagate streamflow in five different rivers in the United States. Qualitative flow observations, synthetically generated from observed flow, are converted into fuzzy observations using flow characteristic for defining fuzzy classes. A model states updating method and a model output correction technique are implemented. An innovative application of Interacting Multiple Models, which use was previously demonstrated on tracking in ballistic missile applications, is proposed as state updating method, together with the traditional Kalman filter. The output corrector approach is based on the fuzzy error corrector, which was previously used for robots navigation. This study demonstrates the usefulness of integrating qualitative flow observations for improving flood estimation. In particular, state updating methods outperform the output correction approach in terms of average improvement of model performances, while the latter is found to be less sensitive to biased observations and to the definition of fuzzy sets used to represent qualitative observations.Water Resource
Uncertainty analysis in rainfall-runoff modelling: Application of machine learning techniques
No full textThis thesis presents powerful machine learning (ML) techniques to build predictive models of uncertainty with application to hydrological models. Two different methods are developed and tested. First one focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulations of hydrological models using efficient ML techniques. Second method aims at modelling uncertainty by building an ensemble of specialised ML models on the basis of past hydrological model’s performance. Methods employed include artificial neural networks, model trees, locally weighted regression and fuzzy logic. The application of the methods to several real-world case studies demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.WatermanagementCivil Engineering and Geoscience
Networked environments for stakeholder participation in water resources and flood management
No full textStakeholders’ awareness and participation is important in the planning and management of water resources and floods. Stakeholders’ spatial distribution and diverse stakeholders’ interest (even opposed) are some of the hindrances in stakeholder participation. This research developed and implemented three frameworks for developing “Networked Environments for Stakeholder Participation” (NESPs) to address these challenges and hindrances in participation. NESPs are envisioned to enable stakeholder participation by providing sharing of information, planning, negotiating and decision support through a web-based computer-aided or mobile environment. The overall results of the research show that networked environments can address the challenges and hindrances and enhance stakeholder participation.Water ManagementCivil Engineering and Geoscience
Multi-Objective Optimization for Urban Drainage Rehabilitation
No full textFlooding in urbanized areas has become a very important issue around the world. The level of service (or performance) of urban drainage systems (UDS) degrades in time for a number of reasons. In order to maintain an acceptable performance of UDS, early rehabilitation plans must be developed and implemented. In developing countries the situation is serious, little investment is done and there are smaller funds each year for rehabilitation. The allocation of such funds must be “optimal” in providing value for money. However this task is not easy to achieve due to the multicriteria nature of the rehabilitation process, taking into account technical, environmental and social interests. Most of the time these are conflicting, which make it a highly demanding task. The present book introduce a framework to deal with multicriteria decision making for the rehabilitation of urban drainage systems, and focuses on several aspects such as the improvement of the performance of the multicriteria optimization through the inclusion of new features in the algorithms and the proper selection of performance criteria. The use of Genetic Algorithms, parallelization and application in countries like Brazil, Colombia y Venezuela are treated in this book.Water ManagementCivil Engineering and Geoscience
Low-cost Space-borne Data for Inundation Modelling: Topography, Flood Extent and Water Level
No full textFloods are among the most damaging natural hazards and their impacts have been dramatically increasing worldwide over the past decades. As most basins of the world are ungauged or poorly gauged and some measurement networks are continuously under decline, the spatial distribution of flood hazard is often difficult to estimate because the input data needed for flood inundation modelling (e.g. topographies, flood extents, water levels) are often not available. A unique opportunity is nowadays provided by the ongoing development of remote sensing data, such as the low-cost, space-borne data. In particular, the development of new remotely sensed data sources has not only shifted flood modelling from a datapoor to a data-rich environment, but also provided a paradigm shift in flood modelling: from developing more sophisticated flood models to evaluating potential of remote sensing data. There is a general consensus that the increased availability and quality of those low-cost remote sensing data will be valuable for improving prediction in ungauged basins. However, their value and potential in supporting hydraulic modelling of floods are still not sufficiently explored in view of the unavoidable, intrinsic uncertainty affecting any modeling exercise. In this context, this thesis aims to explore the potential and limitations of low-cost, space-borne data in flood inundation modelling under uncertainty. In our research work, we analyze the potential in supporting hydraulic modelling of floods of: NASA’s SRTM (Shuttle Radar Topographic Mission) topographic data, SAR (Synthetic Aperture Radar) satellite imagery and radar altimetry. The characteristics of those data, and their pros and cons for inundation modelling are discussed. For example, SRTM`s global coverage and relatively low vertical error on low-slope areas are in favour of floodplain modelling, while its absence of in-channel geometry information would hamper its application in flood studies. Low-cost SAR imagery`s day-night, all-weather, cloud-free acquisition are particularly useful for flood extent monitoring, while its low resolution could induce equifinality in inundation model conditioning. Radar altimetry`s reliable water level measurements over large rivers provides opportunities for flood model calibration and evaluation, while its low space-time frequency limits the application in areas such as flood forecasting. To this end, research work has been carried out by either following a model calibration-evaluation approaches or by explicitly considers major sources of uncertainty within a Monte Carlo framework. To generalize our findings, three river reaches with various scales (from medium to large) and topographic characteristics (e.g. valley-filling, two-level embankments, large and flat floodplain) are used as test sites. Thus, specific modelling exercises are implemented with slight, tailor-made modifications to deal with practical issues, such as the actual data availability, the characteristics of flood events etc. The usefulness of the low-cost space-borne data is quantitatively analyzed. Lastly, an application of SRTM-based flood modelling of a large river is conducted to highlight the challenges of predictions in ungauged basins. The outcomes of the study provide indications on the potential and limitations of low-cost, space-borne data in supporting flood inundation modelling under uncertainty. Specifically, DEM resolution is often less of an issue than its vertical accuracy, as long as the coarse resolution allows the representation of flood patterncontrolling topographic features for the flood modelling issue, which is often not the case in urban flood studies. Thus, the thesis includes and discusses the usefulness of these data according to specific modelling purpose (e.g. re-insurance, planning, design). Moreover, topographic uncertainty could be compensated by other sources of uncertainties in hydraulic modelling if they are explicitly taken into account. The model prediction based on SRTM can be very close to that based on high-resolution, high-accuracy topographic data under other sources of uncertainty. However, besides modelling purpose and uncertainty considered, their actual usefulness could be affected by several other factors, such as the scale of the river under study, flood frequency, and the choice of modelling tools. Furthermore, the issue of in-channel information absent in SAR-derived DEMs are also discussed. It could be partially resolved by using either the global river depth dataset, or depth estimating from hydraulic geometry theory or model parameterization. Lastly, we discuss the upcoming satellite missions, which could potentially impact the way we model flood inundation patters.Water ManagementCivil Engineering and Geoscience
Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrologic and Hydraulic Models
No full textMonitoring stations have been used for decades to measure hydrological variables,and mathematical water models used to predict floods can be enhanced by theincorporation of these observations, i.e. by data assimilation. The assimilation ofremotely sensed water level observations in hydrological and hydraulic modellinghas become more attractive due to their availability and spatially distributed nature
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