14. A Model for Flood Risk Management: Tisza (2025)

13. A Model for Flood Risk Management: Bac Hung Hai

Deliberation, Representation, Equity: Research Approaches, Tools and Algorithms for Participatory Processes, 2017

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Modeling for Flood Control and Management

Natural and Anthropogenic Disasters, 2010

Structural methods to safely dispose floods include: (a) construction of reservoirs for storage of flood water that can be utilized for other purposes once flood recedes, (b) embankments to retain the flood water far away from the flood prone areas, and (c) construction and improvements of channels to adequately discharge the flood waters. Structural measures can be supplemented with non-structural measures such as floodplain zoning as well as flood forecasting as a cost-effective strategy. Both structural and nonstructural methods are to be taken into consideration while planning flood mitigation measures. Salient factors that affect data collection are cost, speed with which the data can be collected and their accuracy. Data is required about the existing vegetation/soils, level of urbanization, land use, watershed characteristics, rainfall intensity and its duration, runoff, degree of flood protection/severity of flood damage, stage-discharge relationships, climatic records, geology and geomorphology, depth of 8 *Corresponding Author 148 Natural and Anthropogenic Disasters: Vulnerability, Preparedness and Mitigation 2nd Proof groundwater, etc. Database of as many flood events as possible for different locations is essential to get a comprehensive overview of flood events over the entire area. This will avoid any ambiguous inferences due to incomplete data. Additional data can also be collected from secondary sources such as by interviews and earlier reports. Data thus collected are to be processed and analyzed in such a manner as to minimize errors which otherwise affect the accuracy of general analyses as well as calibration and validation of hydrologic simulation models. Statistical analysis is very helpful in this regard. The processed data can be utilized for various aspects including development of maximum probable flood, standard project flood, design flood, flood frequency analysis, flood sediment, flood envelope curve, stage-discharge curves and floodplain zoning. The data can also be used for analyzing past flood situations, managing present flood situation or for forecasting future floods of different magnitudes. In India, many agencies are involved in water resources planning, development and disaster management such as Central Water Commission (CWC), Central Ground Water Board (CGWB), Indian Meteorological Department (IMD), National Disaster Management Authority (NDMA), National Water Development Agency (NWDA), National Institute of Hydrology (NIH), Water and Land Management Institutes (WALMI) of various State Governments, National Remote Sensing Centre (NRSC) and State Remote Sensing Application Centers, Geological Survey of India (GSI), etc. Various mathematical modeling approaches based on soft computing and related fields are available for flood control and management (e.g., ASCE, 2000b; Chau et al., 2005; Jain and Singh, 2006). 3. OVERVIEW OF TOOLS AND TECHNIQUES FOR FLOOD MODELING Modeling for flood control and management has attracted considerable attention from number of researchers and various tools are developed to combat floods. A brief overview of some of the tools developed for modeling floods is presented below. 3.1 Artificial Neural Network (ANN) and Fuzzy Logic The Back Propagation Algorithm (Rumelhart et al., 1986) is a procedure to train feed forward ANN models, in which the outputs can be sent only to the immediate next layers. The selection of a suitable architecture for the problem on hand can be done in three steps: fixing the architecture, training the network and testing the network. Main parameters concerned are network architecture, learning rate, type of activation function, definition of error, number of epochs etc. The procedures for training the ANN network are described in ASCE (2000a). Radial Basis Function network (ASCE, 2000a) is a three-layer network in which the hidden layer performs a fixed non-linear transformation with no adjustable parameters. This layer consists of a number of nodes and a parameter vector called a center which can be considered as its weight vector. For each node, the Euclidean distance between the center and the input vector of the network input is computed and transformed by a non-linear function that determines the output of the nodes in the hidden layer. The output layer then combines these results in a linear fashion. The common activation functions used in Radial Basis Function are Sigmoidal and the Gaussian Kernel functions. More details about Radial Basis Functions are available in ASCE (2000a, b). Applications of ANN with reference to flood modeling are discussed in ASCE (2000b). Moreover, there are often many situations with components that are intrinsically vague, called uncertain. Different situations that lead to uncertainty are unquantifiable information, incomplete information, non-obtainable information and partial ignorance (Bojadziev and Bojadziev, 1997). In such cases, fuzzy approach is the most suitable to tackle the vagueness of data. The advantage of fuzzy

Flood risk and flood management

Journal of Hydrology, 2002

Risk management has been established as a well defined procedure for handling risks due to natural, environmental or man made hazards, of which floods are representative. Risk management has been discussed in many previous papers giving different meanings to the term-a result of the fact that risk management actually takes place on three different levels of actions: the operational level, which is associated with operating an existing system, a project planning level, which is used when a new, or a revision of an existing project is planned, and a project design level, which is embedded into the second level and describes the process of reaching an optimal solution for the project. The first two levels will be briefly described in the paper. It will be emphasized that the transition from the first to the second level is a dynamic process. As the value system of a nation changes, and as the natural boundary conditions are modified by human actions or global changes, an existing system will be found not meeting the demands of the present society, and actions on the second level are initiated. The decisions for change depend on the changes in options available for handling a flood situation, as well as on the changes in risk perception and attitudes towards risk. On the third level, the actual cost of a design are evaluated and compared with the benefits obtained from the planned project. In particular, on this level the residual risk is considered, i.e. the risk which remains even after a project is completed and fully operational. q

Flood Models: An Exploratory Analysis and Research Trends

Water

Floods can be caused by heavy rainfall and the consequent overflow of rivers, causing low-lying areas to be affected. Populated regions close to riverbeds are the sectors most affected by these disasters, which requires modelling studies to generate different scenarios. The work focuses on the bibliometric analysis of the search for topics such as flood modelling focused on the research, risk, and assessment of these catastrophes, aiming to determine new trends and tools for their application in the prevention of these natural disasters. The methodology consists of: (i) search criteria and database selection, (ii) pre-processing of the selected data and software, and (iii) analysis and interpretation of the results. The results show a wide range of studies for dimensional analysis in different flood scenarios, which greatly benefit the development of flood prevention and risk strategies. In addition, this work provides insight into the different types of software and modelling for f...

Defining flood risk management strategies: A systems approach

International Journal of Disaster Risk Reduction, 2020

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12 Issue 11: Hydraulic modelling methods used in flood risk management

Methods to Assess, Model and Map the …, 2008

Mathematical models in flood management: overview and challenges

Flood Recovery, Innovation and Response II, 2010

One-third of the annual natural disasters and economic losses, and more than half of the respective victims are flood related. These hazards are likely to become more frequent and more relevant in the future, due to the effects of increase in population, urbanization, land subsidence and the impacts of climate change. Knowledge and advanced scientific tools play a role of paramount importance in the strain of coping with flooding problems. In this context, flood modelling represents the basis for effective flood mitigation. The modelling approach aims to provide the best means for assessing and, subsequently, reducing the vulnerability of rural and urban flood prone areas. By using models, an attempt is made to replace trial and error based strategies, as practised in the past, with more physically-based measures of flood management and control. Mathematical models are the best tools, nowadays available, for the design of efficient flood protection strategies and excellent supporters of decision-makers. With reference to these issues, the paper provides a review and a general description of the main features of the models currently used in flood management along with the characteristics of the experimental data required for models' calibration. Moreover, to highlight the effectiveness and the resilience of these tools, some case studies of flood mitigation and hazard assessment are presented.

Flood Risk Management: An Illustrative Approach

10th Annual Conference of the International Institute for Infrastructure Renewal and Reconstruction, 2014

Widespread flooding with significant damage in many countries, such as the Philippines in 2013, highlights the ongoing need for effective flood risk management (FRM). This hinges on comprehensive access to and dissemination of information about the elements and the people at risk. Simulations, real-time graphs, and maps illustrate the spatial distribution of flood risks, spatial allocation and dissemination of flood effects, if flood risk reduction measures are not implemented, as well as the benefits to be derived from the effective implementation and maintenance of flood risk management measures not realized. Using precipitation, river water, and tide levels, a real-time monitoring site was set up for the Shirakawa River, Kumamoto, Japan. The data gathered from the July 2012 flood event is used as a demonstrator, illustrating a flood event as well as how to utilize the information provided on this site to determine the future time and possibility of flooding. Additionally, an electronically generated flood hazard map making process is being developed for distribution across Japan. These illustrative approaches can be utilized in cities and communities around the globe.

Understanding flood-risk intervention

UN75: Sustainable Engineering in Action, 2020

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Flood Characteristics and Mitigation Issues

2003

Among various natural disasters floods make the largest impact in terms of number of casualties, people affected and property lost. Flood data during the past 30 years show that flood events that cause significant impacts have increased at a steady rate. Available data suggest that this increase could be attributed to increasing population and exposure, increasing property values located in vulnerable areas and migration. For flood disaster mitigation, it is useful to consider the different types of floods, i.e., flash floods, river floods or urban floods and strengthen preparedness through risk assessment and appropriate mitigation measures. Several case studies are described to highlight the flood characteristics and mitigation approaches.