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1
Simoes, Nuno Eduardo da Cruz. "Urban pluvial flood forecasting." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10545.
Повний текст джерелаАнотація:
Two main approaches to enhance urban pluvial flood prediction were developed and tested in this research: (1) short-term rainfall forecast based on rain gauge networks, and (2) customisation of urban drainage models to improve hydraulic simulation speed. Rain gauges and level gauges were installed in the Coimbra (Portugal) and Redbridge (UK) catchment areas. The collected data was used to test and validate the approaches developed. When radar data is not available urban pluvial flooding forecasting can be based on networks of rain gauges. Improvements were made in the Support Vector Machine (SVM) technique to extrapolate rainfall time series. These improvements are: enhancing SVM prediction using Singular Spectrum Analysis (SSA) for pre-processing data; combining SSA and SVM with a statistical analysis that gives stochastic results. A method that integrates the SVM and Cascade-based downscaling techniques was also developed to carry out high-resolution (5-min) precipitation forecasting with longer lead time. Tests carried out with historical data showed that the new stochastic approach was useful for estimating the level of confidence of the rainfall forecast. The integration of the cascade method demonstrates the possibility of generating high-resolution rainfall forecasts with longer lead time. Tests carried out with the collected data showed that water level in sewers can be predicted: 30 minutes in advance (in Coimbra), and 45 minutes in advance (in Redbridge). A method for simplifying 1D1D networks is presented that increases computational speed while maintaining good accuracy. A new hybrid model concept was developed which combines 1D1D and 1D2D approaches in the same model to achieve a balance between runtime and accuracy. While the 1D2D model runs in about 45 minutes in Redbridge, the 1D1D and the hybrid models both run in less than 5 minutes, making this new model suitable for flood forecasting.
2
Abdullah, Rozi. "Rainfall forecasting algorithms for real time flood forecasting." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296151.
Повний текст джерелаАнотація:
A fast catchment response usually leads to a shorter lag time, and under these conditions the forecast lead time obtained from a rainfall-runoff model or correlation between upstream and downstream flows may be infeasible for flood warning purposes. Additional lead time can be obtained from short-term quantitative rainfall forecasts that extend the flood warning time and increase the economic viability of a flood forecasting system. For this purpose algorithms which forecasts the quantitative rainfall amounts up to six hours ahead have been developed, based on lumped and distributed approaches. The lumped forecasting algorithm includes the essential features of storm dynamics such as rainband and raincell movements which are represented within the framework of a linear transfer function model. The dynamics of a storm are readily captured by radar data. A space-time rainfall model is used to generate synthetic radar data with known features, e.g. rainband and raincell velocities. This enables the algorithm to be assessed under ideal conditions, as errors are present in observed radar data. The transfer function algorithm can be summarised as follows. The dynamics of the rainbands and raincells are incorporated as inputs into the transfer function model. The algorithm employs simple spatial cross-correlation techniques to estimate the rainband and raincell velocities. The translated rainbands and raincells then form the auxiliary inputs to the transfer function. An optimal predictor based on minimum square error is then derived from the transfer function model, and its parameters are estimated from the auxiliary inputs and observed radar data in real-time using a recursive least squares algorithm. While the transfer-function algorithm forecasts areal rainfalls, a distributed approach which performs rainfall forecasting at a fine spatial resolution (referred to as the advection equation algorithm) is also evaluated in this thesis. The algorithm expresses the space-time rainfall on a Cartesian coordinate system via a partial differential advection equation. A simple explicit finite difference solution scheme is applied to the equation. A comparison of model parameter estimates is undertaken using a square root information filter data processing algorithm, and single-input single-output and multiple-input multiple-output least squares algorithms.
3
Hopson, Thomas Moore. "Operational flood-forecasting for Bangladesh." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/dissertations/fullcit/3165830.
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4
Baird, Laura. "Flood forecasting in ungauged catchments." Thesis, University of Bristol, 1989. http://hdl.handle.net/1983/b07e966f-e5c8-440e-b29c-f8f6324074b7.
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5
Fayegh, A. David. "Flood advisor : an expert system for flood estimation." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25069.
Повний текст джерелаАнотація:
Expert computer programs have recently emerged from research on artificial intelligence as a practical problem-solving tool. An expert system is a knowledge-based program that imitates the problem-solving behaviour of a human expert to solve complex real-world problems. While conventional programs organize knowledge on two levels: data and program, most expert programs organize knowledge on three levels: data, knowledge base, and control. Thus, what distinguishes such a system from conventional programs is that in most expert systems the problem solving model is treated as a separate entity rather than appearing only implicitly as part of the coding of the program.
The purpose of this thesis is twofold. First, it is intended to demonstrate how domain-specific problem-solving knowledge may be represented in computer memory by using the frame representation technique. Secondly, it is intended to simulate a typical flood estimation situation, from the point-of-view of an expert engineer. A frame network was developed to represent, in data structures, the declarative, procedural, and heuristic knowledge necessary for solving a typical flow estimation problem. The control strategy of this computer-based consultant (FLOOD ADVISOR) relies on the concept that reasoning is dominated by a recognition process which is used to compare new instances of a given phenomena to the stereotyped conceptual framework used in understanding that phenomena. The primary purpose of the FLOOD ADVISOR is to provide interactive advice about the flow estimation technique most suitable to one of five generalized real-world situations. These generalizations are based primarily on the type and quantity of the data and resources available to the engineer. They are used to demonstrate how problem solving knowledge may be used to interactively assist the engineer in making difficult decisions. The expertise represented in this prototype system is far from complete and the recommended solution procedures for each generalized case are in their infancy. However, modifications may be easily implemented as the domain-specific expert knowledge becomes available. It is concluded that over the long term, this type of approach for building problem-solving models of the real world are computationally cheaper and easier to develop and maintain than conventional computer programs.Applied Science, Faculty of
Civil Engineering, Department of
Graduate
6
Bagwell, Anne Marina. "A synoptically guided approach to determining suburbanization's impacts on the hydrology of the Red and White Clay Creeks, Pennsylvania and Delaware /." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 169 p, 2008. http://proquest.umi.com/pqdweb?did=1459905411&sid=7&Fmt=2&clientId=8331&RQT=309&VName=PQD.
Повний текст джерела
7
Zachary, A. Glen. "The estimated parameter flood forecasting model." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25148.
Повний текст джерелаАнотація:
Design flood estimates have traditionally been based on records of past events. However, there is a need for a method of estimating peak flows without these records. The Estimated Parameter Flood Forecasting Model (EPFFM) has been developed to provide such a method for small water resource projects based on a 200 year or less design flood. This "user friendly" computer model calculates the expected peak flow and its standard deviation from low, probable, and high estimates of thirteen user supplied parameters. These parameters describe physical characteristics of the drainage basin, infiltration rates, and rainstorm characteristics. The standard deviation provides a measure of reliability and is used to produce an 80% confidence interval on peak flows.
The thesis briefly reviews existing flow estimation techniques and then describes the development of EPFFM. This includes descriptions of the Chicago method of rainfall hyetograph synthesis, Horton's infiltration equation, inflow by time-area method, Muskingum routing equation, and an approximate method of estimating the variance of multivariate equations since these are all used by EPFFM to model the physical and mathematical processes involved. Two examples are included to demonstrate EPFFM's ability to estimate a confidence interval, and compare these with recorded peak flows.Applied Science, Faculty of
Civil Engineering, Department of
Graduate
8
Varoonchotikul, Pichaid. "Flood forecasting using artificial neural networks /." Lisse : Balkema, 2003. http://www.e-streams.com/es0704/es0704_3168.html.
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9
Baymani-Nezhad, Matin. "Real-time flood forecasting and updating." Thesis, University of Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.617587.
Повний текст джерелаАнотація:
Floods have potential destructive effects on socioeconomic facilities and cause serious risks for people. During the last decades lots of efforts have been carried out 10 overcome the difficulties caused by this natural phenomenon. In the past, most of the studies have been focused on developing mathematical models to forecast flood events in real -time to provide precautionary activities. The models are various from simple structures to models with high complexity and according to the climate conditions of the catchment under study, most appropriate model must be selected to predict flood events by using the existing recorded data from the catchment Rainfall-runoff model is the main component of a real-time flood forecasting model and transforms rainfall to runoff. The model commonly consists of a number of mathematical equations and parameters which are interconnected together for simulating runoff over a catchment. Since a model is a simplification of the real hydrological system, errors In simulation are unavoidable and influence on the simulation accuracy. Hence. the model should be selected properly and requires to be updated continuously to cope with probable hydrological changes which could create errors on model simulations. The current research focus on real-time flood forecasting by improving and developing rainfall-runoff models and indicating solutions to update the model to cope with frequent hydrological changes which can reduce the model performance. The research was started by evaluating optimisation schemes to derive the model parameters and an optimisation method was proposed based on Genetic algorithm concept. On the second stage, a new rainfall -runoff model called ERM, was introduced and suggested as a reliable model to use In rainfall -runoff modeling. Moreover, the adaptability of the ERM model parameters to cope with different errors occurred in terms of modeling was considered. Finally, in the last part of the thesis, the ERM model was coupled with a well-known numerical filter called the Kalman Filter and a real-time flood forecasting model was introduced.
10
Cerda-Villafana, Gustavo. "Artificial intelligence techniques in flood forecasting." Thesis, University of Bristol, 2005. http://hdl.handle.net/1983/09d0faea-8622-4609-a33c-e4baefa304f5.
Повний текст джерелаАнотація:
The need for reliable, easy to set up and operate, hydrological forecasting systems is an appealing challenge to researchers working in the area of flood risk management. Currently, advancements in computing technology have provided water engineering with powerful tools in modelling hydrological processes, among them, Artificial Neural Networks (ANN) and genetic algorithms (GA). These have been applied in many case studies with different level of success. Despite the large amount of work published in this field so far, it is still a challenge to use ANN models reliably in a real-time operational situation. This thesis is set to explore new ways in improving the accuracy and reliability of ANN in hydrological modelling. The study is divided into four areas: signal preprocessing, integrated GA, schematic application of weather radar data, and multiple input in flow routing. In signal preprocessing, digital filters were adopted to process the raw rainfall data before they are fed into ANN models. This novel technique demonstrated that significant improvement in modelling could be achieved. A GA, besides finding the best parameters of the ANN architecture, defined the moving average values for previous rainfall and flow data used as one of the inputs to the model. A distributed scheme was implemented to construct the model exploiting radar rainfall data. The results from weather radar rainfall were not as good as the results from raingauge estimations which were used for comparison. Multiple input has been carried out modelling a river junction with excellent results and an extraction pump with results not so promising. Two conceptual models for flow routing modelling and a transfer function model for rainfall-runoff modelling have been used to compare the ANN model's performance, which was close to the estimations generated by the conceptual models and better than the transfer function model. The flood forecasting system implemented in East Anglia by the Environment Agency, and the NERC HYREX project have been the main data sources to test the model.
11
Damle, Chaitanya. "Flood forecasting using time series data mining." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001038.
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12
Tomlin, Christopher Michael. "Adaptive flood forecasting using weather radar data." Thesis, Lancaster University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322340.
Повний текст джерела
13
Kozyniak, Kathleen. "Integrated mesoscale-hydrometeorological modelling for flood forecasting." Thesis, University of Bristol, 2001. http://hdl.handle.net/1983/f54ba862-fc88-4ae1-9f6a-fe955dc5e581.
Повний текст джерелаАнотація:
In an effort to improve upon rainfall forecasts produced by simple storm advection methods (nowcasts) and to broach the gap between them and the forecasts of complex Numerical Weather Prediction (NWP) models, in terms of the spatial detail and length of lead-time each provides, the research presented explores the possibility of combining elements of each into a physically-based algorithm for rainfall forecasting. It is an algorithm that uses as its foundation the rainfall prediction model of Mark French and Witold Krajewski, developed in 1994. Their model was designed to take advantage of the high resolution rainfall observations and tracking abilities provided by weather radar and to achieve a rainfall forecast by augmenting extrapolation techniques with a representation of storm dynamics in the form of "rising parcel" theory. The new algorithm/model retains those features but incorporates NWP data to assist with forecasting, using it as a means to enable an informed choice of algorithm pathways and, more specifically, to identify the ingredients of precipitation, namely ascending air of high moisture content. A case study application of the new rainfall forecasting model to storms in Northern England shows its performance, at a lead-time of one hour, compares favourably with respect to extrapolation and persistence techniques and also NWP forecasts, and that it is able to provide more assured forecasts than persistence and nowcasts at longer lead-times. The robustness of the model is tested and confirmed by way of another case study, this time using Mediterranean storms and with predictions made in the context of urban hydrology. The case studies help to identify aspects of the model that need improvement, with representation of orographic forcing being a key one. Both the model's encouraging performance and its pinpointed weaknesses provide impetus for further research in the area of integrated mesoscale-hydrometeorological modelling for flood forecasting.
14
Zevin, Susan Faye 1949. "A probabilistic approach to flash flood forecasting." Diss., The University of Arizona, 1986. http://hdl.handle.net/10150/191119.
Повний текст джерелаАнотація:
A major area targeted for hydrometeorological forecast service improvements is in flash flood forecasting. Verification data show that general public service products of flash flood forecasts do not provide enough lead time in order for the public to make effective response. Sophisticated users of flash flood forecasts could use forecast probabilities of flash flooding in order to make decisions in preparation for the predicted event. To this end, a systematic probabilistic approach to flash flood forecasting is presented. The work first describes a deterministic system which serves as a conceptual basis for the probability system. The approach uses accumulated rainfall plus potential rainfall over a specified area and time period, and assesses this amount against the water holding capacity of the affected basin. These parameters are modeled as random variables in the probabilistic approach. The effects of uncertain measurements of rainfall and forecasts of precipitation from multiple information sources within a time period and moving forward in time are resolved through the use of Bayes' Theorem. The effect of uncertain inflows and outflows of atmospheric moisture on the states of the system, the transformation of variables, is resolved by use of convolution. Requirements for probability distributions to satisfy Bayes' Theorem are discussed in terms of the types and physical basis of meteorological data needed. The feasibility of obtaining the data is evaluated. Two alternatives for calculating the soil moisture deficit are presented--one, an online automatic rainfall/runoff model, the other an approximation. Using the soil moisture approximation, a software program was developed to test the probabilistic approach. A storm event was simulated and compared against an actual flash flood event. Results of the simulation improved forecast lead time by 3-5 hours over the actual forecasts issued at the time of the event.
15
Krauße, Thomas. "Development of a Class Framework for Flood Forecasting." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-103439.
Повний текст джерелаАнотація:
Aus der Einleitung:
The calculation and prediction of river flow is a very old problem. Especially extremely high values of the runoff can cause enormous economic damage. A system which precisely predicts the runoff and warns in case of a flood event can prevent a high amount of the damages.
On the basis of a good flood forecast, one can take action by preventive methods and warnings. An efficient constructional flood retention can reduce the effects of a flood event enormously.With a precise runoff prediction with longer lead times (>48h), the dam administration is enabled to give order to their gatekeepers to empty dams and reservoirs very fast, following a smart strategy. With a good timing, that enables the dams later to store and retain the peak of the flood and to reduce all effects of damage in the downstream. A warning of people in possible flooded areas with greater lead time, enables them to evacuate not fixed things like cars, computers, important documents and so on. Additionally it is possible to use the underlying rainfall-runoff model to perform runoff simulations to find out which areas are threatened at which precipitation events and associated runoff in the river. Altogether these methods can avoid a huge amount of economic damage.
16
Krauße, Thomas. "Development of a Class Framework for Flood Forecasting." Technische Universität Dresden, 2007. https://tud.qucosa.de/id/qucosa%3A26441.
Повний текст джерелаАнотація:
Aus der Einleitung:
The calculation and prediction of river flow is a very old problem. Especially extremely high values of the runoff can cause enormous economic damage. A system which precisely predicts the runoff and warns in case of a flood event can prevent a high amount of the damages.
On the basis of a good flood forecast, one can take action by preventive methods and warnings. An efficient constructional flood retention can reduce the effects of a flood event enormously.With a precise runoff prediction with longer lead times (>48h), the dam administration is enabled to give order to their gatekeepers to empty dams and reservoirs very fast, following a smart strategy. With a good timing, that enables the dams later to store and retain the peak of the flood and to reduce all effects of damage in the downstream. A warning of people in possible flooded areas with greater lead time, enables them to evacuate not fixed things like cars, computers, important documents and so on. Additionally it is possible to use the underlying rainfall-runoff model to perform runoff simulations to find out which areas are threatened at which precipitation events and associated runoff in the river. Altogether these methods can avoid a huge amount of economic damage.:List of Symbols and Abbreviations S. III
1 Introduction S. 1
2 Process based Rainfall-Runoff Modelling S. 5
2.1 Basics of runoff processes S. 5
2.2 Physically based rainfall-runoff and hydrodynamic river models S. 15
3 Portraying Rainfall-Runoff Processes with Neural Networks S. 21
3.1 The Challenge in General S. 22
3.2 State-of-the-art Approaches S. 24
3.3 Architectures of neural networks for time series prediction S. 26
4 Requirements specification S. 33
5 The PAI-OFF approach as the base of the system S. 35
5.1 Pre-Processing of the Input Data S. 37
5.2 Operating and training the PoNN S. 47
5.3 The PAI-OFF approach - an Intelligent System S. 52
6 Design and Implementation S. 55
6.1 Design S. 55
6.2 Implementation S. 58
6.3 Exported interface definition S. 62
6.4 Displaying output data with involvement of uncertainty S. 64
7 Results and Discussion S. 69
7.1 Evaluation of the Results S. 69
7.2 Discussion of the achieved state S. 75
8 Conclusion and FutureWork S. 77
8.1 Access to real-time meteorological input data S. 77
8.2 Using further developed prediction methods S. 79
8.3 Development of a graphical user interface S. 80
Bibliography S. 83
17
Hatter, Elizabeth. "Using radar and hydrologic data to improve forecasts of flash floods in Missouri /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1422929.
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18
Sun, Hongyong. "Investigation of flood probability and regionalization." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1173275342.
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19
Han, D. "Weather radar information processing and real-time flood forecasting." Thesis, University of Salford, 1991. http://usir.salford.ac.uk/2089/.
Повний текст джерелаАнотація:
This thesis describes research into remotely sensed weather radar information systems and specifically addresses three problems; 1) Weather radar data processing; 2) Real-time flood forecasting models and 3) Computer system design for the realisation of the real-time flood forecasting system using radar data. Quantitative rainfall measurements utilising weather radar is of high temporal and spatial resolution when compared with traditional rainfall measurements. Analysis was carried out to assessth e type of radar datap roductsr equired for operational use in flood forecastings ystem. This includes issues of data processing such as quantisation, temporal sampling and spatial sampling. The influence of the data process on hydrological applications is also addressed. Theoretical analysis was carried out to probe the characteristics of Transfer Function Models and robust flood forecasting modelling procedure is proposed. The proposed model is always stable and physical realisable and is described as PRTF (Physical Realisable Transfer Function model). Algorithms and software for the identification of PRTF are presented. It was found that such a model is easy to identify and more importantly it can be updated robustly in real time. By changing the impulse response of the PRTF, it has been found that significant improvements can be observed in river flow simulation. A RST (Rainfall S eparation Tank) model was developed and incorporated into the PRTF model. The adaptivity of the PRTF also has the potential to make use of high spatial resolution radar rainfall data and could be further incorporated into an Expert System suitable for real-time application. Finally, the thesis includes the development of the WRIP system (Weather Radar Information Processor). Such a system can process weather radar information and use it for the real-time flood forecasting. The system design consists of database design, user interface design and program design. An object-oriented computing concept is used in the program design. The final system is currently in test operation within the N. R. A Wessex Region, including the man machine interface (MMI) incorporating a portable computer based data acquisition and display system known by the acronym `STORM' (System To Obtain Radar Rainfall Measurements).
20
Keefer, Timothy Orrin, and Timothy Orrin Keefer. "Likelihood development for a probabilistic flash flood forecasting model." Thesis, The University of Arizona, 1993. http://hdl.handle.net/10150/192077.
Повний текст джерелаАнотація:
An empirical method is developed for constructing likelihood functions required in a Bayesian probabilistic flash flood forecasting model using data on objective quantitative precipitation forecasts and their verification. Likelihoods based on categorical and probabilistic forecast information for several forecast periods, seasons, and locations are shown and compared. Data record length, forecast information type and magnitude, grid area, and discretized interval size are shown to affect probabilistic differentiation of amounts of potential rainfall. Use of these likelihoods in Bayes' Theorem to update prior probability distributions of potential rainfall, based on preliminary data, to posterior probability distributions, reflecting the latest forecast information, demonstrates that an abbreviated version of the flash flood forecasting methodology is currently practicable. For this application, likelihoods based on the categorical forecast are indicated. Apart from flash flood forecasting, it is shown that likelihoods can provide detailed insight into the value of information contained in particular forecast products.
21
Smith, Paul James. "Probabilistic flood forecasting using a distributed rainfall-runoff model." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/143966.
Повний текст джерелаАнотація:
Kyoto University (京都大学)0048
新制・課程博士
博士(工学)
甲第12267号
工博第2596号
新制||工||1366(附属図書館)
24103
UT51-2006-J260
京都大学大学院工学研究科都市社会工学専攻
(主査)教授 小尻 利治, 教授 池淵 周一, 教授 中北 英一
学位規則第4条第1項該当
22
Haddad, Khaled. "Design flood estimation for ungauged catchments in Victoria : ordinary and generalised least squares methods compared." Thesis, View thesis, 2008. http://handle.uws.edu.au:8081/1959.7/30369.
Повний текст джерелаАнотація:
Design flood estimation in small to medium sized ungauged catchments is frequently required in hydrologic analysis and design and is of notable economic significance. For this task Australian Rainfall and Runoff (ARR) 1987, the National Guideline for Design Flow Estimation, recommends the Probabilistic Rational Method (PRM) for general use in South- East Australia. However, there have been recent developments that indicated significant potential to provide more meaningful and accurate design flood estimation in small to medium sized ungauged catchments. These include the L moments based index flood method and a range of quantile regression techniques. This thesis focuses on the quantile regression techniques and compares two methods: ordinary least squares (OLS) and generalised least squares (GLS) based regression techniques. It also makes comparison with the currently recommended Probabilistic Rational Method. The OLS model is used by hydrologists to estimate the parameters of regional hydrological models. However, more recent studies have indicated that the parameter estimates are usually unstable and that the OLS procedure often violates the assumption of homoskedasticity. The GLS based regression procedure accounts for the varying sampling error, correlation between concurrent flows, correlations between the residuals and the fitted quantiles and model error in the regional model, thus one would expect more accurate flood quantile estimation by this method. This thesis uses data from 133 catchments in the state of Victoria to develop prediction equations involving readily obtainable catchment characteristics data. The GLS regression procedure is explored further by carrying out a 4-stage generalised least squares analysis where the development of the prediction equations is based on relating hydrological statistics such as mean flows, standard deviations, skewness and flow quantiles to catchment characteristics. This study also presents the validation of the two techniques by carrying out a split-sample validation on a set of independent test catchments. The PRM is also tested by deriving an updated PRM technique with the new data set and carrying out a split sample validation on the test catchments. The results show that GLS based regression provides more accurate design flood estimates than the OLS regression procedure and the PRM. Based on the average variance of prediction, standard error of estimate, traditional statistics and new statistics, rankings and the median relative error values, the GLS method provided more accurate flood frequency estimates especially for the smaller catchments in the range of 1-300 km2. The predictive ability of the GLS model is also evident in the regression coefficient values when comparing with the OLS method. However, the performance of the PRM method, particularly for the larger catchments appears to be satisfactory as well.
23
Akter, Shirin. "Regional flood estimation method for the Mt. Lofty Ranges /." Title page, abstract and contents only, 1992. http://web4.library.adelaide.edu.au/theses/09ENS/09ensa315.pdf.
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24
Viner, David. "The hydrological utilisation of the FRONTIERS system." Thesis, University of Salford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315519.
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25
Haddad, Khaled. "Design flood estimation for ungauged catchments in Victoria ordinary & generalised least squares methods compared /." View thesis, 2008. http://handle.uws.edu.au:8081/1959.7/30369.
Повний текст джерелаАнотація:
Thesis (M.Eng. (Hons.)) -- University of Western Sydney, 2008.A thesis submitted towards the degree of Master of Engineering (Honours) in the University of Western Sydney, College of Health and Science, School of Engineering. Includes bibliographical references.
26
Haggett, Christopher Milne. "An integrated approach to flood warning in England and Wales." Thesis, Middlesex University, 2000. http://eprints.mdx.ac.uk/13632/.
Повний текст джерелаАнотація:
Flood warning systems have been researched and discussed for several decades and there is a high degree of consensus in the literature that the most effective structure for a flood warning system is that of an integrated system. Experience suggests however, that few, if any, operational systems are designed in an integrated way and that few practitioners fully appreciate the benefits of integration. Through an analysis of arrangements in the Thames Basin, this research addresses this issue by identifying the necessary criteria and actions required to introduce an integrated system. The limited number of models that attempt to conceptualise flood warning systems in an integrated way have been critically examined and have found to focus too narrowly on selective integrative criteria. It is concluded that there is a need for a wider and multidimensional perspective. This study rectifies this deficiency by presenting a conceptual model that is derived from a more comprehensive assessment of the most relevant integrative factors. A two-staged process is adopted with an initial identification of a wide range of issues and variables, leading to a more focused set of factors presented under four main headings that are used to structure the substantive chapters of this thesis. These integrative factors can be conceptualised as crosscutting strands running through and drawing together the main components of a flood warning system (detection, forecasting, dissemination and response) that help ensure that these components work together collaboratively towards a common aim. Few of the integrative factors identified in this research were found in operational flood warning practices in England and Wales prior to 1996. A number of improvements were made with the establishment of the Environment Agency as the lead authority in both flood forecasting and flood warning dissemination, but a number of weakness still prevail. Through the use of case studies the plausibility of introducing a fully integrated approach to future arrangements has been tested and found to be both practical and feasible.
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Karlsson, Magnus Sven. "NEAREST NEIGHBOR REGRESSION ESTIMATORS IN RAINFALL-RUNOFF FORECASTING." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/282088.
Повний текст джерелаАнотація:
The subject of this study is rainfall-runoff forecasting and flood warning. Denote by (X(t),Y(t)) a sequence of equally spaced bivariate random variables representing rainfall and runoff, respectively. A flood is said to occur at time period (n + 1) if Y(n + 1) > T where T is a fixed number. The main task of flood warning is that of deciding whether or not to issue a flood alarm for the time period n + 1 on the basis of the past observations of rainfall and runoff up to and including time n. With each decision, warning or no warning, there is a certain probability of an error (false alarm or no alarm). Using notions from classical decision theory, the optimal solution is the decision that minimizes Bayes risk. In Chapter 1 a more precise definition of flood warning will be given. A critical review (Chapter 2) of classical methods for forecasting used in hydrology reveals that these methods are not adequate for flood warning and similar types of decision problems unless certain Gaussian assumptions are satisfied. The purpose of this study is to investigate the application of a nonparametric technique referred to as the k-nearest neighbor (k-NN) methods to flood warning and least squares forecasting. The motivation of this method stems from recent results in statistics which extends nonparametric methods for inferring regression functions in a time series setting. Assuming that the rainfall-runoff process can be cast in the framework of Markov processes then, with some additional assumptions, the k-NN technique will provide estimates that converge with an optimal rate to the correct decision function. With this in mind, and assuming that our assumptions are valid, then we can claim that this method will, as the historical record grows, provide the best possible estimate in the sense that no other method can do better. A detailed description of the k-NN estmator is provided along with a scheme for calibration. In the final chapters, the forecasts of this new method are compared with the forecasts of several other methods commonly used in hydrology, on both real and simulated data.
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Nilsson, Andreas. "FloodViewer : Web-based visual interface to a flood forecasting system." Thesis, Linköping University, Department of Science and Technology, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1394.
Повний текст джерелаАнотація:
This diploma work has been done as a part of the EC funded projects, MUSIC VK1- CT-2000-00058 and SmartDoc IST-2000-28137. The objective was to create an intuitive and easy to use visualization of flood forecasting data provided in the MUSIC project. This visualization is focused on the Visual User Interface and is built on small, reusable components. The visualization, FloodViewer, is small enough to ensure the possibility of distribution via the Internet, yet capable of enabling collaboration possibilities and embedment in electronic documents of the entire visualization. Thus, FloodViewer has been developed in three versions for different purposes.
Analysis and report generation (FloodViewer ) Collaborative analysis (FloodViewerNet ) Presentation and documentation (FloodViewerX).
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Wang, Li-Pen. "Improved rainfall downscaling for real-time urban pluvial flood forecasting." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10127.
Повний текст джерелаАнотація:
Traditionally, hydrologists had a relatively minor role in rainfall data processing; they usually simply took data from meteorologists. However, meteorological organisations usually provide weather service over a larger area and scale (i.e. country level); the applicability of this large-scale information to urban hydrological applications is therefore questionable. This work tries to provide a local view on rainfall processing, aiming to improve the suitability (in terms of accuracy and resolution) of operational rainfall data for urban hydrological uses. This work explores advanced downscaling and adjustment techniques to address the identified issues in urban hydrology: accuracy and resolution. On the basis of a a review and the testing of state of the art techniques, the Bayesian-based adjustment technique and the newly-developed cascade-based downscaling techniques are found to be suitable tools to improve respectively the accuracy, and the resolution of operational radar (and raingauge) rainfall estimates. In addition, a combined application of these two techniques is tested; the results suggested that, although extra uncertainty may appear, this combination demonstrates a clear potential for providing accurate and high-resolution (street-scale and 5-min) rainfall estimates.
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Zhang, Limin. "Intelligent algorithms applied to weather radar based flood forecasting system." Thesis, University of Salford, 1999. http://usir.salford.ac.uk/42998/.
Повний текст джерелаАнотація:
The UK weather radar network and telemetry system for the raingauges and river level gauges provided the solid physical base which produce the large amount of data in real time and a large variety of operational flood forecasting models were supplied from SW Region of the Environment Agency. Data processing, the selection of a suitable model, model calibration and parameters updating have played a more and more important role in real time forecasting and this thesis focuses on many of the key issues involved in the emerging area. Within this context, surface fitting, interpolation and cluster analysis were used for adjustment of the weather radar data and comparison between the raingauge data and radar data. As the core of the forecasting system the rainfall runoff model and river routing model were investigated in a wide-ranging manner, the key model utilised is the Transfer Function model. Potential misinterpretation of the TF model was explained by distinguishing between the "Black Box" model and the "White Box" model. The physically based Genetic Cascade Transfer Function (GCTF) model was introduced and shown to be consistent with the Gamma function and Muskingum model which were based upon the three common assumptions: linear, time-invariant and Single Input Single Output (SISO) system. The calculation formula for the moment parameters and the geometry coefficients (t-peak time, volume parameter) create the initial model and a genetic algorithm provides the basic tool to global search for the parameters. An expert system plus the genetic algorithm are combined to provide a real time updating capability. A dentritic model composed of the SISO rainfall runoff model at several tributaries and the Multi-Input Single Output (MISO) routing model in the mainstream were developed and applied to the Bristol Avon catchment. As a Weather Radar Information Processor, WRIP(II) was extended and implemented on a SPARC 10 workstation and communions at Environment Agency South West Region (Exeter) with a graphical user interface based on X/Motif.
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Napolitano, Giulia. "An exploration of neural networks for real-time flood forecasting." Thesis, University of Leeds, 2011. http://etheses.whiterose.ac.uk/2178/.
Повний текст джерелаАнотація:
This thesis examines Artificial Neural Networks (ANNs) for rainfall-runoff modelling. A simple ANN was first developed to predict floods in the city of Rome, located in the Tiber River basin. A rigorous comparison of the ensemble ANN and the conceptual TEVERE model were undertaken for two recent flood events in 2005 and 2008. Both models performed well but the conceptual model was better at overall hydrograph prediction while the ANN performed better for the initial part of the event at longer lead times. Further experimentation with the ANN model was then undertaken to try to improve the model performance. Additional upstream stations and rainfall inputs were added including hourly totals, effective rainfall and cumulative rainfall. Different methods of normalisation and different ANN training algorithms were also implemented along with four alternative methods for combining the ensemble ANN predictions. The results showed that the ANN was able to extrapolate to the 2008 event. Finally, Empirical Mode Decomposition was applied to the ANN to examine whether this method has value for ANN rainfall-runoff modelling. At the same time the impact of the random initialisation of the weights of the ANN was investigated for the Potomac River and Clark Fork River catchments in the USA. The EMD was shown to be a valuable tool in detecting signal properties but application to ANN rainfall-runoff modelling was dependent on the nature of the dataset. Overall uncertainty from the random initialisation of weights varied by catchment where uncertainties were shown to be very large at high stream flows. Finally, a suite of redundant and non-redundant model performance measures were applied consistently to all models. The value of applying a range of redundant and non-redundant measures, as well as benchmark-based methods was demonstrated.
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Yatheendradas, Soni. "Flash Flood Forecasting for the Semi-Arid Southwestern United States." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/195244.
Повний текст джерелаАнотація:
Flash flooding in the semi-arid United States poses a significant danger to life and property. One effective way to mitigate flood risk is by implementing a rainfall-runoff model in a real-time forecast and warning system. This study investigated the feasibility of using the mechanistic, distributed semi-arid rainfall-runoff model KINEROS2 driven by high resolution radar rainfall input estimates obtained from the NEXRAD WSR-88D DHR reflectivity measurements in such a system. The original procedural paradigm-based KINEROS2 Fortran 77 code with space-time looping was recoded into an object-oriented Fortran 90 code with time-space looping for this purpose. The recoded form is now applicable to large basins, is easily future-extensible, and individual modules can be incorporated into other models.Sources of operational uncertainty in the above system were investigated for their influence over several events within a sub-basin of the USDA-ARS Walnut Gulch Experimental Watershed. Uncertainties considered were in the rainfall estimates, the model parameters, and the initial conditions. The variance-based Sobol' method of global sensitivity analysis conditioned on the observed streamflow showed that the uncertainty in the modeled response was heavily dominated by the operational variability of biases in the radar rainfall depth estimates. Sensitivities to KINEROS2 parameters indicates the need for improved representation of semi-arid hillslope hydrology in small basins, while pointing to specific influential, but poorly identified model parameters towards which field investigations should be directed. The significant influence of initial hillslope soil moisture showed the requirement of a sophisticated inter-storm model component for a continuous forecasting model.A synthetic study data was used to further explore the phenomena seen in the above real data study, of behavioral modifier set inconsistency across all events and of irreducibility in the spatial modifier ranges. The former was found to be attributable to wide uncertainty ranges in the sources of uncertainty, and the latter to the high distributed model non-linearity with associated interactions. These contribute towards a high predictive uncertainty in operational forecasting.Overall, the GLUE-based predictive uncertainty method with behavioral classification and accommodation of wide operational source uncertainty ranges is recommended as a simple and effective setup for operational flash flood forecasting.
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Yapo, Patrice Ogou 1967. "A Markov chain flow model with application to flood forecasting." Thesis, The University of Arizona, 1992. http://hdl.handle.net/10150/278135.
Повний текст джерелаАнотація:
This thesis presents a new approach to streamflow forecasting. The approach is based on specifying the probabilities that the next flow of a stream will occur within different ranges of values. Hence, this method is different from the time series models where point estimates are given as forecasts. With this approach flood forecasting is possible by focusing on a preselected range of streamflows. A double criteria objective function is developed to assess the model performance in flood prediction. Three case studies are examined based on data from the Salt River in Phoenix, Arizona and Bird Creek near Sperry, Oklahoma. The models presented are: a first order Markov chain (FOMC), a second order Markov chain (SOMC), and a first order Markov chain with rainfall as an exogenous input (FOMCX). Three forecasts methodologies are compared among each other and against time series models. It is shown that the SOMC is better than the FOMC while the FOMCX is better than the time series models.
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Krewson, Corey Nicholas. "Near Real-Time Flood Forecasts from Global Hydrologic Forecasting Models." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7476.
Повний текст джерелаАнотація:
This research assesses possible methods for extending the Streamflow Prediction Tool from a streamflow forecasting model to a flood extent forecasting model. This new flood extent forecasting model would allow valuable and easy to understand information be disseminated in a timely manner for flood preparation and flood response. The Height Above Nearest Drainage (HAND) method and AutoRoute method were considered for flood extent models but the HAND was the better option for its simple and quick computation as well as its viability on a global scale. Due to the importance of Digital Elevation Models (DEMs) in these flood extent models, an analysis was performed on the sensitivity and response of different DEMs with the HAND method. The HAND method with the differing DEMs was also analyzed using the Streamflow Prediction Tool for model boundary conditions against Sentinel-1 SAR generated flood extent images from August 24, 2017. The MERIT DEM performed the best in this analysis and is recommended for future research in creating a global forecasting flood extent model. The HAND method covered about 25% of the generated flood extent images and more complex flood extent models may need to be considered in areas where HAND underperforms. Finally, a proof of concept flood extent model was created and deployed as a web application for easy accessibility and distribution of flood information. Additional research to consider is flood impact based on affected population or an economic analysis, as well as optimizing model parameters for increased accuracy and performance. Additional research is also needed for HAND DEM analysis in other parts of the world.
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Kamwi, Innocent Silibelo. "Fitting extreme value distributions to the Zambezi river flood water levels recorded at Katima Mulilo in Namibia." Thesis, University of the Western Cape, 2005. http://etd.uwc.ac.za/index.php?module=etd&.
Повний текст джерелаАнотація:
The aim of this research project was to estimate parameters for the distribution of annual maximum flood levels for the Zambezi River at Katima Mulilo. The estimation of parameters was done by using the maximum likelihood method. The study aimed to explore data of the Zambezi's annual maximum flood heights at Katima Mulilo by means of fitting the Gumbel, Weibull and the generalized extreme value distributions and evaluated their goodness of fit.
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Persio, Andrew Franklin. "Assessment of changes in the water-surface profile of the lower canyon of the Little Colorado River, Arizona." Thesis, The University of Arizona, 2004. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_etd_hy0124_sip1_w.pdf&type=application/pdf.
Повний текст джерела
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Storck, Pascal. "Trees, snow, and flooding : an investigation of forest canopy effects on snow accumulation and melt at the plot and watershed scales in the Pacific Northwest /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/10103.
Повний текст джерела
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Neal, Jeffrey. "Flood forecasting and adaptive sampling with spatially distributed dynamic depth sensors." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485291.
Повний текст джерелаАнотація:
The movement of computational power and communications capabilities onto networks of sensors in the environment through the concept of pervasive or ubiquitous computing has initiated opportunities for the delivery of ground-based data in real-time and the development of adaptive monitoring systems. Measurements of water level taken by a network ofwireless sensors called 'FloodNet' were assimilated into a one-dimensional hydrodynamic model using an ensemble Kalman filter, to create a forecasting model. The ensemble Kalman filter led to an increase in forecast accuracy of between 50% and 70% depending on location for forecast lead times of less than 4 hours. This research then focused on methods for targeting measurements in real-time, such that the power limited but flexible resources deployed by the FloodNet project could be used optimally. Two targeting methods were developed. The first targeted measurements systematically over space and time until the forecasting model predicted that the probability of the water level exceeding a pre-defined threshold was less than 5%. The second method targeted measurements based on the expected decrease in forecasted water level error variance at a validation time and location, quickly calculated for various sets of measurements by an ensemble transform Kalman filter. Estimates of forecast error covariance from the ensemble Kalman filter and ensemble transform Kalman filter were significantly correlated, with correlations ranging between 0.979 and 0.292. Targeting measurements based on the decrease in forecast error variance was found to be more efficient than the systematic sampling method. The ensemble transform Kalman filter based targeting method was also used to estimate the 'signal variance' oftheoretical measurements at any computational node in the hydrodynamic model. Furthermore, time series data, different sensors types and measurements of floodplain stage could all be taken into account either as part of the targeting process or prior to measurement targeting.
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Tsang, Fan Cheong. "Advances in flood forecasting using radar rainfalls and time-series analysis." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.481184.
Повний текст джерелаАнотація:
This thesis reports the use of a time-series analysis approach to study the catchment hydrological system of the River Ribble. Rain gauge records, radar rainfall estimates and flow data are used in the analysis. The preliminary study consists of the flow forecasting at Reedyford, Pendle Water (82 km2). Flow forecasts generated from the rain gauge records are better than the radar rainfall estimates over this small catchment. However, the catchment response to rainfall is quick and no clear advantages in extending the lead-time of the forecast can be introduced by using an artificial time delayed rainfall input. A non-linear rainfall-flow relationship has been studied using the rain gauge rainfall and flow records at the River Hodder catchment (261 km2). A calibration scheme is used to identify the non-linear function of the catchment as well as the rainfall-flow system model. Although a better time-invariant system model can be identified, the non-linear rainfall-flow process cannot be fully explained by a power law function of effective rainfall. Assuming the dynamic, nonlinear system characteristics of the catchment can be reflected by a time-varying model gain parameter, relationships between the parameter and the flow, and between the parameter and the rainfall can be evaluated. These relationships have been used to improve the flow forecast during storm events. The results indicate, however, that the approach failed to improve the flow forecast near the peak flow condition. Radar data have been incorporated to forecast the flow at Jumbles Rock (1053 km2) and Samlesbury (1140 km2), River Ribble. The radar data calibrated by the Lancaster University Adaptive Radar Calibration System appears to produce better flow forecasts than the standard radar data product calibrated by the Meteorological Office. The proposed flow forecasting scheme generates better forecasts than the current system operated by the National Rivers Authority, North West Region.
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Terti, Galateia. "Forecasting of flash-flood human impacts integrating the social vulnerability dynamics." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAU004/document.
Повний текст джерелаАнотація:
Au XXIe siècle, la prévision de l'aléa hydrométéorologique et des impacts associés aux crues rapides demeurent un défi pour les prévisionnistes et les services de secours. Les mesures structurelles et / ou les avancées des systèmes de prévision hydrologique ne garantissent pas, à elles seules, la réduction des décès lors de ces phénomènes d'inondation rapide. La littérature souligne la nécessité d'intégrer d'autres facteurs, liés aux processus de vulnérabilité sociaux et comportementaux, afin de mieux prendre en compte les risques encourus par les populations lors de ces épisodes extrêmes. Cette dissertation conduit une analyse théorique couplés à ceux de une analyse des accidents historiques mortels afin d'expliquer les interactions qui existent entre les processus hydrométéorologiques et sociaux responsables de l'apparition de vulnérabilités humaines lors de crues rapides aux États-Unis. Des données d'enquêtes liées aux crues rapides sont examinées afin d'élaborer un système de classification des circonstances du décès (en voiture, à l'extérieur, à proximité d'un cours d'eau, dans un camping, dans un bâtiment ou en mobile-home). L'objectif est d'établir un lien entre la conception des vulnérabilités et l'estimation des pertes humaines liées à ces catastrophes naturelles. "Random forest" est utilisé et est basé sur un arbre de décision, qui permet d'évaluer la probabilité d'occurrence de décès pour une circonstance donnée en fonction d'indicateurs spatio-temporels. Un système de prévision des décès liés à l'usage de la voiture lors des crues rapides, circonstance la plus répandue, est donc proposé en s'appuyant sur les indicateurs initialement identifiés lors de l'étude théorique. Les résultats confirment que la vulnérabilité humaine et le risque associé varient de façon dynamique et infra journalière, et en fonction de la résonance spatio-temporelle entre la dynamique sociale et la dynamique d'exposition aux dangers. Par exemple, on constate que les jeunes et les personnes d'âge moyen sont plus susceptibles de se retrouver pris au piège des crues rapides particulièrement soudaines(par exemple, une durée de près de 5 heures) pendant les horaires de travail ou de loisirs en extérieur. Les personnes âgées sont quant à elles plus susceptibles de périr à l'intérieur des bâtiments, lors d'inondations plus longues, et surtout pendant la nuit lorsque les opérations de sauvetage et / ou d'évacuation sont rendues difficiles. Ces résultats mettent en évidence l'importance d'examiner la situation d'exposition aux risques en tenant compte de la vulnérabilité dynamique, plutôt que de se concentrer sur les conceptualisations génériques et statiques. Ce concept de vulnérabilité dynamique est l'objectif de modélisation développée dans cette thèse pour des vulnérabilités liés aux véhicules. À partir de l'étude de cas sur les crues rapides survenues en mai 2015, et en analysant principalement les états du Texas et de l'Oklahoma, principaux états infectés par ces évènements,le modèle montre des résultats prometteurs en termes d'identification spatio-temporelle des circonstances dangereuses. Cependant, des seuils critiques pour la prédiction des incidents liés aux véhicules doivent être étudiés plus en profondeur en intégrant des sensibilités locales non encore résolues par le modèle. Le modèle établi peut être appliqué, à une résolution journalière ou horaire, pour chaque comté du continent américain. Nous envisageons cette approche comme une première étape afin de fournir un système de prévision des crues rapides et des risques associés sur le continent américain. Il est important que la communauté scientifique spécialisée dans l'étude des crues éclairs récoltent des données à plus haute résolution lorsque ces épisodes entrainement des risques mortels, et ce afin d'appuyer la modélisation des complexités temporelles et spatiales associées aux pertes humaines causées par les futures inondations soudainesIn the 21st century the prediction of and subsequent response to impacts due to sudden onset and localized flash flooding events remain a challenge for forecasters and emergency managers. Structural measures and/or advances in hydrological forecasting systems alone do not guarantee reduction of fatalities during short-fuse flood events. The literature highlights the need for the integration of additional factors related to social and behavioral vulnerability processes to better capture risk of people during flash floods. This dissertation conducts a theoretical analysis as well as an analysis of flash flood-specific historic fatalities to explain complex and dynamic interactions between hydrometeorological, spatial and social processes responsible for the occurrence of human life-threatening situations during the "event" phase of flash floods in the United States (U.S.). Individual-by-individual fatality records are examined in order to develop a classification system of circumstances (i.e., vehicle-related, outside/close to streams, campsite, permanent buildings, and mobile homes). The ultimate goal is to link human vulnerability conceptualizations with realistic forecasts of prominent human losses from flash flood hazards. Random forest, a well-known decision-tree based ensemble machine learning algorithm for classification is adopted to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators at the county-level and daily or hourly time steps. Starting from the most prevalent circumstance of fatalities raised from both the literature review and the impact-based analysis, flash flood events with lethal vehicle-related accidents are the subject to predict. The findings confirm that human vulnerability and the subsequent risk to flash flooding, vary dynamically depending on the space-time resonance between that social and hazard dynamics. For example, it is found that younger and middle-aged people are more probable to get trapped from very fast flash floods (e.g., duration close to 5 hours) while participating in daytime outdoor activities (e.g., vehicle-related, recreational). In contrary, older people are more likely to perish from longer flooding inside buildings, and especially in twilight and darkness hours when rescue and/or evacuation operations are hindered. This reasoning places the importance of situational examination of dynamic vulnerability over generic and static conceptualizations, and guides the development of flash flood-specific modeling of vehicle-related human risk in this thesis. Based on the case study of May 2015 flash floods with a focus in Texas and Oklahoma, the model shows promising results in terms of identifying dangerous circumstances in space and time. Though, critical thresholds for the prediction of vehicle-related incidents need to be further investigated integrating local sensitivities, not yet captured by the model. The developed model can be applied on a daily or hourly basis for every U.S. county. We vision this approach as a first effort to provide a prediction system to support emergency preparedness and response to flash flood disasters over the conterminous U.S. It is recommended that the flash flood disaster science community and practitioners conduct data collection with more details for the life-threatening scene, and at finer resolutions to support modeling of local temporal and spatial complexities associated with human losses from flash flooding in the future
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Silva, Mark Daniel Basco. "Probabilistic monthly flood forecasting models using statistical and machine learning approaches." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20934.
Повний текст джерелаАнотація:
Floods are considered the most damaging of natural hazards, and their frequency and damage is predicted to increase in the future. This research aims to develop an automated methodology using statistical and machine learning approaches that can perform a probabilistic monthly flood forecast. The methodology was tested to handle multiple variables as predictors. The significance of the spatial variability of the predictors was determined through model maps using 222 hydrological reference stations in Australia. Variable screening to forecast the upper 10th percentile of flow was based on the ten best scoring variables using Random Forests (RF), and flexible forecast models were developed using Generalized Additive Models (GAM). Results showed that the methodology can be used to sort through many variables (i.e. past streamflow, rainfall, Southern Oscillation Index (SOI), El Niño/ Southern Oscillation Modoki Index (EMI), and Pacific Sea Surface Temperatures (SST)) as predictors. It can be easily updated and it can vary spatially. The basic conceptual model assumed that Flow was a function of Antecedent conditions (=Lag rainfall), Flow memory (=Seasonality + autocorrelation), Climate effects (=SST indices) and random noise. Lagged flow, lagged rainfall, and lagged NIÑO 1+2 were the most important predictors using a monthly one-out cross-validation (OOCV) process and a forward cross-validation process (FCV). The Gilbert Skill Score indicated that using transformed flow data performed better than using non-transformed flow data or binary data. Model performance was affected by unsupervised variable selection in the RF model; and the employed threshold (10%) which defines a flood event. Overall skill scores based on OOCV process were in the range of 0.2-0.5 indicating reasonable forecast skill.
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Coccia, Gabriele <1983>. "Analysis and developments of uncertainty processors for real time flood forecasting." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amsdottorato.unibo.it/3423/1/Tesi.pdf.
Повний текст джерелаАнотація:
The hydrologic risk (and the hydro-geologic one, closely related to it) is, and has always been, a very relevant issue, due to the severe consequences that may be provoked by a flooding or by waters in general in terms of human and economic losses.
Floods are natural phenomena, often catastrophic, and cannot be avoided, but their damages can be reduced if they are predicted sufficiently in advance. For this reason, the flood forecasting plays an essential role in the hydro-geological and hydrological risk prevention. Thanks to the development of sophisticated meteorological, hydrologic and hydraulic models, in recent decades the flood forecasting has made a significant progress, nonetheless, models are imperfect, which means that we are still left with a residual uncertainty on what will actually happen. In this thesis, this type of uncertainty is what will be discussed and analyzed.
In operational problems, it is possible to affirm that the ultimate aim of forecasting systems is not to reproduce the river behavior, but this is only a means through which reducing the uncertainty associated to what will happen as a consequence of a precipitation event. In other words, the main objective is to assess whether or not preventive interventions should be adopted and which operational strategy may represent the best option.
The main problem for a decision maker is to interpret model results and translate them into an effective intervention strategy. To make this possible, it is necessary to clearly define what is meant by uncertainty, since in the literature confusion is often made on this issue. Therefore, the first objective of this thesis is to clarify this concept, starting with a key question: should be the choice of the intervention strategy to adopt based on the evaluation of the model prediction based on its ability to represent the reality or on the evaluation of what actually will happen on the basis of the information given by the model forecast?
Once the previous idea is made unambiguous, the other main concern of this work is to develope a tool that can provide an effective decision support, making possible doing objective and realistic risk evaluations. In particular, such tool should be able to provide an uncertainty assessment as accurate as possible. This means primarily three things: it must be able to correctly combine all the available deterministic forecasts, it must assess the probability distribution of the predicted quantity and it must quantify the flooding probability. Furthermore, given that the time to implement prevention strategies is often limited, the flooding probability will have to be linked to the time of occurrence. For this reason, it is necessary to quantify the flooding probability within a horizon time related to that required to implement the intervention strategy and it is also necessary to assess the probability of the flooding time.
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Coccia, Gabriele <1983>. "Analysis and developments of uncertainty processors for real time flood forecasting." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amsdottorato.unibo.it/3423/.
Повний текст джерелаАнотація:
The hydrologic risk (and the hydro-geologic one, closely related to it) is, and has always been, a very relevant issue, due to the severe consequences that may be provoked by a flooding or by waters in general in terms of human and economic losses.
Floods are natural phenomena, often catastrophic, and cannot be avoided, but their damages can be reduced if they are predicted sufficiently in advance. For this reason, the flood forecasting plays an essential role in the hydro-geological and hydrological risk prevention. Thanks to the development of sophisticated meteorological, hydrologic and hydraulic models, in recent decades the flood forecasting has made a significant progress, nonetheless, models are imperfect, which means that we are still left with a residual uncertainty on what will actually happen. In this thesis, this type of uncertainty is what will be discussed and analyzed.
In operational problems, it is possible to affirm that the ultimate aim of forecasting systems is not to reproduce the river behavior, but this is only a means through which reducing the uncertainty associated to what will happen as a consequence of a precipitation event. In other words, the main objective is to assess whether or not preventive interventions should be adopted and which operational strategy may represent the best option.
The main problem for a decision maker is to interpret model results and translate them into an effective intervention strategy. To make this possible, it is necessary to clearly define what is meant by uncertainty, since in the literature confusion is often made on this issue. Therefore, the first objective of this thesis is to clarify this concept, starting with a key question: should be the choice of the intervention strategy to adopt based on the evaluation of the model prediction based on its ability to represent the reality or on the evaluation of what actually will happen on the basis of the information given by the model forecast?
Once the previous idea is made unambiguous, the other main concern of this work is to develope a tool that can provide an effective decision support, making possible doing objective and realistic risk evaluations. In particular, such tool should be able to provide an uncertainty assessment as accurate as possible. This means primarily three things: it must be able to correctly combine all the available deterministic forecasts, it must assess the probability distribution of the predicted quantity and it must quantify the flooding probability. Furthermore, given that the time to implement prevention strategies is often limited, the flooding probability will have to be linked to the time of occurrence. For this reason, it is necessary to quantify the flooding probability within a horizon time related to that required to implement the intervention strategy and it is also necessary to assess the probability of the flooding time.
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Brong, Brian S. "A study of flash flood potential in western Nevada and eastern California to enhance flash flood forecasting and awareness." abstract and full text PDF (free order & download UNR users only), 2005. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433282.
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Makakole, Billy T. J. "Revision of the regional maximum flood calculation method for Lesotho." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95935.
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Thesis (MEng) -- Stellenbosch University, 2014.ENGLISH ABSTRACT: The Francou and Rodier (1967) empirical approach uses the original concept of envelope curves for the definition of the regional maximum flood (RMF). Kovacs (1980) adopted the Francou and Rodier empirical flood calculation method and applied it to 355 catchments in South Africa. He revised his study in 1988 to also include the southern portions of the Southern Africa subcontinent. No method other than the Francou and Rodier empirical flood approach in the reviewed literature was found to be suitable for the purpose of this study. Therefore the Francou and Rodier empirical approach, as applied by Kovacs in 1988, was reapplied and used in this study to update the RMF for Lesotho. Maximum recorded flood peaks were derived from annual maximum time series and an up to date catalogue of flood peaks for 29 catchments was compiled for Lesotho. The maximum recorded flood peaks were then plotted on the logarithmic scale against their corresponding catchment areas. There are 3 major river systems that divide Lesotho into hydrologically homogenous basins. Envelope curves were drawn on the upper bound of the cloud of plotted points for these 3 river basins. These envelope curves represent the maximum flood peaks that can reasonably be expected to occur within the respective river basins in Lesotho.
AFRIKAANSE OPSOMMING: Francou en Rodier (1967) se empiriese benadering maak gebruik van die oorspronklike konsep van boonste limiet kurwes vir die definisie van die streeks maksimum vloed (SMV). Kovacs (1980) het die Francou en Rodier empiriese vloed berekening metode toegepas op 355 opvanggebiede in Suid-Afrika. Hy hersien sy studie in 1988 om ook die suidelike gedeeltes van die Suider-Afrikaanse subkontinent in te sluit. Geen ander metode as die Francou en Rodier empiriese vloed benadering is in die literatuur gevind wat as geskik aanvaar kan word vir die doel van hierdie studie nie. Daarom is die Francou en Rodier empiriese benadering, soos toegepas deur Kovacs in 1988, weer in hierdie studie toegepas en gebruik om die SMV metode vir Lesotho op te dateer. Maksimum aangetekende vloedpieke is verkry vanuit jaarlikse maksimum tyd-reekse en ʼn opgedateerde katalogus van vloedpieke vir 29 opvanggebiede saamgestel vir Lesotho. Die maksimum aangetekende vloedpieke is grafies aangetoon op logaritmiese skaal teenoor hul opvanggebiede. Daar is 3 groot rivierstelsels wat Lesotho in hidrologiese homogene gebiede verdeel. Boonste limiet kurwes is opgestel om die boonste grens van die gestipte punte vir hierdie 3 gebiede aan te toon. Hierdie krommes verteenwoordig die maksimum vloedpieke wat redelikerwys verwag kan word om binne die onderskeie rivierstelsels in Lesotho voor te kan kom.
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Blackburn, Julia L. "Forecasting open water and ice related flood events using hydraulic modelling techniques." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0002/MQ59780.pdf.
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Liu, Jia. "Rainfall-runoff modelling and numerical weather prediction for real-time flood forecasting." Thesis, University of Bristol, 2011. http://hdl.handle.net/1983/87375e5e-4186-4707-b7c6-465617dc1ac1.
Повний текст джерелаАнотація:
This thesis focuses on integrating rainfall-runoff modelling with a mesoscale numerical weather prediction (NWP) model to make real-time flood forecasts at the catchment scale. Studies carried out are based on catchments in Southwest England with a main focus on the Brue catchment of an area of 135 km2 and covered by a dense network of 49 rain gauges and a C-band weather radar. The studies are composed of three main parts: Firstly, two data mining issues are investigated to enable a better calibrated rainfall-runoff model for flood forecasting. The Probability Distributed Model (PDM) is chosen which is widely used in the UK. One of the issues is the selection of appropriate data for model calibration regarding the data length and duration. It is found that the information quality of the calibration data is more important than the data length in determining the model performance after calibration. An index named the Information Cost Function (ICF) developed on the discrete wavelet decomposition is found to be efficient in identifying the most appropriate calibration data scenario. Another issue is for the impact of the temporal resolution of the model input data when using the rainfall-runoff model for real-time forecasting. Through case studies and spectral analyses, the optimal choice of the data time interval is found to have a positive relation with the forecast lead time, i.e., the longer is the lead time, the larger should the time interval be. This positive relation is also found to be more obvious in the catchment with a longer concentration time. A hypothetical curve is finally concluded to describe the general impact of data time interval in real-time forecasting. The development of the NWP model together with the weather radar allows rainfall forecasts to be made in high resolutions of time and space. In the second part of studies, numerical experiments for improving the NWP rainfall forecasts are carried out based on the newest generation mesoscale NWP model, the Weather Research & Forecasting (WRF) model. The sensitivity of the WRF performance is firstly investigated for different domain configurations and various storm types regarding the evenness of rainfall distribution in time and space. Meanwhile a two-dimensional verification scheme is developed to quantitatively evaluate the WRF performance in the temporal and spatial dimensions. Following that the WRF model is run in the cycling mode in tandem with the three-dimensional variational assimilation technique for continuous assimilation of the radar reflectivity and traditional surface/ upperair observations. The WRF model has shown its best performance in producing both rainfall simulations and improved rainfall forecasts through data assimilation for the storm events with two dimensional evenness of rainfall distribution; while for highly convective storms with rainfall concentrated in a small area and a short time period, the results are not ideal and much work remains to be done in the future. Finally, the rainfall-runoff model PDM and the rainfall forecasting results from WRF are integrated together with a real-time updating scheme, the Auto-Regressive and Moving Average (ARMA) model to constitute a flood forecasting system. The system is tested to be reliable in the small catchment such as Brue and the use of the NWP rainfall products has shown its advantages for long lead-time forecasting beyond the catchment concentration time. Keywords: rainfall-runoff modelling, numerical weather prediction, flood forecasting, real-time updating, spectral analysis, data assimilation, weather radar.
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Russano, Euan [Verfasser], and André [Akademischer Betreuer] Niemann. "Grey-box models for flood forecasting and control / Euan Russano ; Betreuer: André Niemann." Duisburg, 2018. http://d-nb.info/115144670X/34.
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Vivoni, Enrique R. (Enrique Rafael) 1975. "Hydrologic modeling using triangulated irregular networks : terrain representation, flood forecasting and catchment response." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/85757.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.Includes bibliographical references.
Numerical models are modern tools for capturing the spatial and temporal variability in the land-surface hydrologic response to rainfall and understanding the physical relations between internal watershed processes and observed streamflow. This thesis presents the development and application of a distributed hydrologic model distinguished by its representation of topography through a triangulated irregular network (TIN) and its coupling of the surface and subsurface processes leading to the catchment response. As a research tool for hydrologic forecasting and experimentation, the TIN-based Real-time Integrated Basin Simulator (tRIBS) fully incorporates spatial heterogeneities in basin topography, surface descriptors and hydrometeorological forcing to produce dynamic maps of hydrologic states and fluxes. These capabilities allow investigation of theoretical questions and practical problems in hydrologic science and water resources engineering. Three related themes are developed in this thesis. First, a set of methods are developed for constructing TIN topographic models from raster digital elevation models (DEM) for hydrologic and geomorphic applications. A new approach for representing a steady-state estimate of a particular watershed process within the physical mesh is introduced. Hydrologic comparisons utilizing different terrain models are made to investigate the suitable level of detail required for capturing process dynamics accurately. Second, the TIN-based model is utilized in conjunction with a rainfall forecasting algorithm to assess the space-time flood predictability. For two hydrometeorological case studies, the forecast skill is assessed as a function of rainfall forecast lead time, catchment scale and the spatial variability in the quantitative precipitation forecasts (QPF). Third, the surface and subsurface runoff response in a complex basin is investigated with respect to changes in storm properties and the initial water table position.The partitioning of rainfall into runoff production mechanisms is found to be a causative factor in the nonlinearity and scale-dependence observed in the basin hydrograph response. The model applications presented in this thesis highlight the advantages of TIN- based modeling for hydrologic forecasting and process-oriented studies over complex terrain. In particular, the multi-resolution and multi-scale capabilities are encouraging for a range of applied and scientific problems in catchment hydrology.
by Enrique R. Vivoni.
Ph.D.
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Fang, Wei-chun, and 方唯鈞. "Flood discharge forecasting by Genetic Programming." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/40629098653411031835.
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碩士逢甲大學
水利工程所
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Genetic programming is a kind of control theory that spring up in recent years. This deterministic method evolves to adapt itself to the environment of problems. When using deterministic method to infer a hydrology process, the method maybe ignore some of the drastic events. It will increase risk of the flood forecasting. This thesis proposes the classification of typhoon data. The procedure chooses different models of the model bank that according to the structure of data to simulate flooding. After this process, we apply ANFIS to correct the error of GP model base on the rainfall and discharge. The ultimate goal of this research is to reduce the risk of flood forecasting variation. The GP model performed stably and reliably in the validation tests at Wu-Xi watershed.