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Academic literature on the topic 'Flood forecasting Computer programs'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Flood forecasting Computer programs"

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Manik, Ngarap Im. "Perancangan Program Peramalan Kanal Banjir Barat Jakarta Menggunakan Autoregresi Multivariant." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (June 1, 2012): 186. http://dx.doi.org/10.21512/comtech.v3i1.2402.

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This paper discusses the design of computer programs that is able to discern the characteristics description of water surface elevation data in Manggarai water gate, which variable is the most influential on the water surface elevation model and find a proper flood forecasting model using multivariate autoregressive model. The result of this study is able to assist the water gate officer in delivering early warning, prevention and anticipation of flood countermeasure. The forecast equation model obtained is Yt = 109,.7828 + 0,9291 CHt-6 – 24,484 T t-2 – 0,06245 PM t-2 + 1,4706 KB t-2 in which temperature and water surface elevation is a variable that owns the strongest correlation. This variable owns negative correlation which means that if the temperature falls, the water levels will rise. The coefficient of determination has a value of R2 = 0.4056 and the Durbin Watson statistics for DW = 0.7429.
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Rakhymberdina, M. Ye, E. V. Grokhotov, Zh A. Assylkhanova, and M. M. Toguzova. "USING SPACE SURVEY MATERIALS FOR MODELING HYDRODYNAMIC ACCIDENTS AT MINING ENTERPRISES IN KAZAKHSTAN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-5/W1-2022 (February 3, 2022): 193–98. http://dx.doi.org/10.5194/isprs-archives-xlvi-5-w1-2022-193-2022.

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Abstract. The timeliness of using modern computer programs for modelling flood zones, the consequences of hydraulic accidents, dam breakthroughs, flood and flood forecasting in a complex system of rivers and channels for the prevention of hydro meteorological emergencies is beyond doubt. The use of BIM technologies will make it possible to move from point-based flood risk assessments to areal ones, which will significantly improve the reliability of planned measures to prevent natural and anthropogenic emergencies.The purpose - to perform works on modelling of hydrodynamic accident and forecast of its development by the example of tailings dumps in concentration plant in East Kazakhstan. As the initial data - digital model for the area of work, technical reports on engineering-hydrographical survey, topographic-geodetic works, engineering-geological survey, high-resolution satellite images in a panchromatic survey mode. On the basis of geoinformation modelling methods with use of initial and remote sensing data, final digital terrain model was built in Digital software. The method based on direct hydrodynamic modelling of area flooding was used to calculate hydrodynamic accidents, to model the dynamics of area flooding because of tailings dam break in several levels. The practical result is numerical hydrodynamic modelling of dynamics flooding area because of partial destruction, erosion of embankment dam of tailings concentrator, total area and extent of flooding, as well as the area and depth of partially flooded buildings of residential development was estimated, thematic maps of flooded area were created, as well as maps of water passage with flow velocities during the hydrodynamic accident.Thus, the application of advanced space imagery, GIS technologies in full measure allow for simulating the occurrence, development of hydrodynamic accidents in structures, to determine area, time of flooding.
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Benoit, R., N. Kouwen, W. Yu, S. Chamberland, and P. Pellerin. "Hydrometeorological aspects of the Real-Time Ultrafinescale Forecast Support during the Special Observing Period of the MAP<sup>*</sup>." Hydrology and Earth System Sciences 7, no. 6 (December 31, 2003): 877–89. http://dx.doi.org/10.5194/hess-7-877-2003.

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Abstract. During the Special Observation Period (SOP, 7 September–15 November, 1999) of the Mesoscale Alpine Programme (MAP), the Canadian Mesoscale Compressible Community Model (MC2) was run in real time at a horizontal resolution of 3 km on a computational domain of 350☓300☓50 grid points, covering the whole of the Alpine region. The WATFLOOD model was passively coupled to the MC2; the former is an integrated set of computer programs to forecast flood flows, using all available data, for catchments with response times ranging from one hour to several weeks. The unique aspect of this contribution is the operational application of numerical weather prediction data to forecast flows over a very large, multinational domain. An overview of the system performance from the hydrometeorological aspect is presented, mostly for the real-time results, but also from subsequent analyses. A streamflow validation of the precipitation is included for large basins covering upper parts of the Rhine and the Rhone, and parts of the Po and of the Danube. In general, the MC2/WATFLOOD model underestimated the total runoff because of the under-prediction of precipitation by MC2 during the MAP SOP. After the field experiment, a coding error in the cloud microphysics scheme of MC2 explains this underestimation to a large extent. A sensitivity study revealed that the simulated flows reproduce the major features of the observed flow record for most of the flow stations. The experiment was considered successful because two out of three possible flood events in the Swiss-Italian border region were predicted correctly by data from the numerical weather models linked to the hydrological model and no flow events were missed. This study has demonstrated that a flow forecast from a coupled atmospheric-hydrological model can serve as a useful first alert and quantitative forecast. Keywords: mesoscale atmospheric model, hydrological model, flood forecasting, Alps
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Thirumalaiah, Konda, and M. C. Deo. "Real-Time Flood Forecasting Using Neural Networks." Computer-Aided Civil and Infrastructure Engineering 13, no. 2 (March 1998): 101–11. http://dx.doi.org/10.1111/0885-9507.00090.

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Bhola, Punit Kumar, Bhavana B. Nair, Jorge Leandro, Sethuraman N. Rao, and Markus Disse. "Flood inundation forecasts using validation data generated with the assistance of computer vision." Journal of Hydroinformatics 21, no. 2 (December 7, 2018): 240–56. http://dx.doi.org/10.2166/hydro.2018.044.

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Abstract Forecasting flood inundation in urban areas is challenging due to the lack of validation data. Recent developments have led to new genres of data sources, such as images and videos from smartphones and CCTV cameras. If the reference dimensions of objects, such as bridges or buildings, in images are known, the images can be used to estimate water levels using computer vision algorithms. Such algorithms employ deep learning and edge detection techniques to identify the water surface in an image, which can be used as additional validation data for forecasting inundation. In this study, a methodology is presented for flood inundation forecasting that integrates validation data generated with the assistance of computer vision. Six equifinal models are run simultaneously, one of which is selected for forecasting based on a goodness-of-fit (least error), estimated using the validation data. Collection and processing of images is done offline on a regular basis or following a flood event. The results show that the accuracy of inundation forecasting can be improved significantly using additional validation data.
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Puttinaovarat, Supattra, and Paramate Horkaew. "Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 11 (November 15, 2019): 137. http://dx.doi.org/10.3991/ijim.v13i11.11237.

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Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.
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Supatmi, Sri, Rongtao Hou, and Irfan Dwiguna Sumitra. "Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia." Computational Intelligence and Neuroscience 2019 (February 25, 2019): 1–13. http://dx.doi.org/10.1155/2019/6203510.

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An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The study aims finding which approach gives the best performance for forecasting flood vulnerability. Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs). The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia. Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.
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Leedal, D., A. H. Weerts, P. J. Smith, and K. J. Beven. "A data based mechanistic real-time flood forecasting module for NFFS FEWS." Hydrology and Earth System Sciences Discussions 9, no. 6 (June 8, 2012): 7271–96. http://dx.doi.org/10.5194/hessd-9-7271-2012.

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Abstract. The data based mechanistic (DBM) approach for identifying and estimating rainfall to level, and level to level models has been shown to perform well for flood forecasting in several studies. The DELFT-FEWS open shell operational flood forecasting system provides a framework linking hydrological/meteorological real-time data, real-time forecast models, and a human/computer interaction interface. This infrastructure is used by the UK National Flood Forecasting System (NFFS) and the European Flood Alert System (EFAS) among others. The open shell nature of the FEWS framework has been specifically designed to make it easy to add new forecasting models written as FEWS modules. This paper shows the development of the DBM forecast model as a FEWS module and presents results for the Eden catchment (Cumbria UK) as a case study.
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Johnson, Lynn E., and John L. Dallmann. "Flood Flow Forecasting Using Microcomputer Graphics and Radar Imagery." Computer-Aided Civil and Infrastructure Engineering 2, no. 2 (November 6, 2008): 85–99. http://dx.doi.org/10.1111/j.1467-8667.1987.tb00136.x.

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Ding, Yukai, Yuelong Zhu, Jun Feng, Pengcheng Zhang, and Zirun Cheng. "Interpretable spatio-temporal attention LSTM model for flood forecasting." Neurocomputing 403 (August 2020): 348–59. http://dx.doi.org/10.1016/j.neucom.2020.04.110.

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Dissertations / Theses on the topic "Flood forecasting Computer programs"

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Brownell, Dorie Lynn. "Application of a Geographical Information System to Estimate the Magnitude and Frequency of Floods in the Sandy and Clackamas River Basins, Oregon." PDXScholar, 1995. https://pdxscholar.library.pdx.edu/open_access_etds/4877.

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A geographical information system (GIS) was used to develop a regression model designed to predict flood magnitudes in the Sandy and Clackamas river basins in Oregon. Manual methods of data assembly, input, storage, manipulation and analysis traditionally used to estimate basin characteristics were replaced with automated techniques using GIS-based computer hardware and software components. Separate GIS data layers representing (1) stream gage locations, (2) drainage basin boundaries, (3) hydrography, (4) water bodies, (5) precipitation, (6) landuse/land cover, (7) elevation and (8) soils were created and stored in a GIS data base. Several GIS computer programs were written to automate the spatial analysis process needed in the estimation of basin characteristic values using the various GIS data layers. Twelve basin characteristic data parameters were computed and used as independent variables in the regression model. Streamflow data from 19 gaged sites in the Sandy and Clackamas basins were used in a log Pearson Type III analysis to define flood magnitudes at 2-, 5-, 10-, 25-, 50- and 100-year recurrence intervals. Flood magnitudes were used as dependent variables and regressed against different sets of basin characteristics (independent variables) to determine the most significant independent variables used to explain peak discharge. Drainage area, average annual precipitation and percent area above 5000 feet proved to be the most significant explanatory variables for defining peak discharge characteristics in the Sandy and Clackamas river basins. The study demonstrated that a GIS can be successfully applied in the development of basin characteristics for a flood frequency analysis and can achieve the same level of accuracy as manual methods. Use of GIS technology reduced the time and cost associated with manual methods and allowed for more in-depth development and calibration of the regression model. With the development of GIS data layers and the use of GIS-based computer programs to automate the calculation of explanatory variables, regression equations can be developed and applied more quickly and easily. GIS proved to be ideally suited for flood frequency modeling applications by providing advanced computerized techniques for spatial analysis and data base management.
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Varoonchotikul, Pichaid. "Flood forecasting using artificial neural networks /." Lisse : Balkema, 2003. http://www.e-streams.com/es0704/es0704_3168.html.

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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.

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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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Zarekarizi, Mahkameh. "Ensemble Data Assimilation for Flood Forecasting in Operational Settings: from Noah-MP to WRF-Hydro and the National Water Model." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4651.

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The National Water Center (NWC) started using the National Water Model (NWM) in 2016. The NWM delivers state-of-the-science hydrologic forecasts in the nation. The NWM aims at operationally forecasting streamflow in more than 2,000,000 river reaches while currently river forecasts are issued for 4,000. The NWM is a specific configuration of the community WRF-Hydro Land Surface Model (LSM) which has recently been introduced to the hydrologic community. The WRF-Hydro model, itself, uses another newly-developed LSM called Noah-MP as the core hydrologic model. In WRF-Hydro, Noah-MP results (such as soil moisture and runoff) are passed to routing modules. Riverine water level and discharge, among other variables, are outputted by WRF-Hydro. The NWM, WRF-Hydro, and Noah-MP have recently been developed and more research for operational accuracy is required on these models. The overarching goal in this dissertation is improving the ability of these three models in simulating and forecasting hydrological variables such as streamflow and soil moisture. Therefore, data assimilation (DA) is implemented on these models throughout this dissertation. State-of-the art DA is a procedure to integrate observations obtained from in situ gages or remotely sensed products with model output in order to improve the model forecast. In the first chapter, remotely sensed satellite soil moisture data are assimilated into the Noah-MP model in order to improve the model simulations. The performances of two DA techniques are evaluated and compared in this chapter. To tackle the computational burden of DA, Massage Passing Interface protocols are used to augment the computational power. Successful implementation of this algorithm is demonstrated to simulate soil moisture during the Colorado flood of 2013. In the second chapter, the focus is on the WRF-Hydro model. Similarly, the ability of DA techniques in improving the performance of WRF-Hydro in simulating soil moisture and streamflow is investigated. The results of chapter 2 show that the assimilation of soil moisture can significantly improve the performance of WRF-Hydro. The improvement can reach 58% depending on the study location. Also, assimilation of USGS streamflow observations can improve the performance up to 25%. It was also observed that soil moisture assimilation does not affect streamflow. Similarly, streamflow assimilation does not improve soil moisture. Therefore, joint assimilation of soil moisture and streamflow using multivariate DA is suggested. Finally, in chapter 3, the uncertainties associated with flood forecasting are studied. Currently, the only uncertainty source that is taken into account is the meteorological forcings uncertainty. However, the results of the third chapter show that the initial condition uncertainty associated with the land state at the time of forecast is an important factor that has been overlooked in practice. The initial condition uncertainty is quantified using the DA. USGS streamflow observations are assimilated into the WRF-Hydro model for the past ten days before the forecasting date. The results show that short-range forecasts are significantly sensitive to the initial condition and its associated uncertainty. It is shown that quantification of this uncertainty can improve the forecasts by approximately 80%. The findings of this dissertation highlight the importance of DA to extract the information content from the observations and then incorporate this information into the land surface models. The findings could be beneficial for flood forecasting in research and operation.
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Dunn, Adam. "A model of wildfire propagation using the interacting spatial automata formalism." University of Western Australia. School of Computer Science and Software Engineering, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0071.

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[Truncated abstract] In this thesis, I address the modelling and computer simulation of spatial, eventdriven systems from a computer science perspective. Spatially explicit models of wildland fire (wildfire) behaviour are addressed as the specific application domain. Wildfire behaviour is expressed as a formal model and the associated simulations are compared to existing models and implementations. It is shown that the in- teracting spatial automata formalism provides a general framework for modelling spatial event-driven systems and is appropriate to wildfire systems. The challenge adressed is that of physically realistic modelling of wildfire behaviour in heterogeneous environments . . . Many current models do not incorporate the influence of a neighbourhood (the geometry of the fire front local to an unburnt volume of fuel, for example), but rather determine the propagation of fire using only point information. Whilst neighbourhood-based influence of behaviour is common to cellular automata theory, its use is very rare in existing models of wildfire models. In this thesis, I present the modelling technique and demonstrate its applicability to wildfire systems via a series of simulation experiments, where I reproduce known spatial wildfire dynamics. I conclude that the interacting spatial automata formalism is appropriate as a basis for constructing new computer simulations of wildfire spread behaviour. Simulation results are compared to existing implementations, highlighting the limitations of current models and demonstrating that the new models are capable of greater physical realism.
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Cwalinski, Tim A. "Simulated forecasting of yellow perch (Perca flavescens) relative population density for Indiana waters of Lake Michigan : responses to varying harvest and alewife density." Virtual Press, 1996. http://liblink.bsu.edu/uhtbin/catkey/1036196.

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The yellow perch, (Perca flavescens), is an important commercial and sport fish in Indiana waters of Lake Michigan. The population is currently managed by temporary restrictions of commercial harvest. A computer simulation model was developed to examine the effects of various constant harvest quotas and alewife densities on yellow perch relative numbers.Model design is based on the SLAM II simulation language incorporating a FORTRAN biological subroutine. The age-structured population model includes measured or predicted biological characteristics of the dynamic pool model. Recruitment is based on a preestablished three-dimensional Ricker stock-recruitment function including alewife (Alosa pseudoharengus) species interaction as a constant or stochastic factor. Sex-specific natural mortality rates were established through life history parameter analysis and the von Bertalanffy growth factors. Density-dependent growth is incorporated into each year of a model run and fluctuates with the simultaneous density of fish. Constant levels of commercial harvest ranging from 0 to 700,000 kg were used in 20-year forecasts. Initial conditions for model runs were 1984 and 1994 trawl CPUE levels when yellow perch were at high and low levels, respectively according to standardized sampling. Response variables were examined as mean catches over each forecast length and included: age 2 fish, spawning stock (z 190 mm), and total catch > age 1.Alewife densities had a tremendous impact on mean catches of the response variables. Highest catches under any forecast period occurred when alewife was considered absent from the system. Catches declined as alewife density was increased as a 20-year constant under each harvest regimen.Catches of spawning size fish were maintained at highest levels for all forecast periods when harvest was set to zero. Catches of young fish were moderate with this harvest regimen if initial catch conditions were high such as in 1984. Catches of young fish were always higher in the absence of a commercial fishery if initial catch conditions were low such as in 1994. Low to moderate harvest quotas could maintain moderate levels of young fish for the forecast length if initial model conditions were high. However, these quota levels for the 1984-2004 forecast length resulted in lower mean catches of spawning size fish as compared to the no commercial fishery regimen. The best case scenario for all response variables when initial catch conditions were low was under a no commercial harvest regimen.
Department of Biology
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Nigrini, Lucas Bernardo. "Developing a neural network model to predict the electrical load demand in the Mangaung municipal area." Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2012. http://hdl.handle.net/11462/176.

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Thesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012
Because power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.
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Pearcy, Charles M. "The impact of background resolution on Target Acquisitions Weapons Software (TAWS) sensor performance." Thesis, Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/2232.

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Approved for public release, distribution is unlimited
This study evaluated the sensitivity of TAWS detection range calculations to the spatial resolution of scenario backgrounds. Sixteen independent sites were analyzed to determine TAWS background. Multispectral satellite data were processed to different spatial resolutions from 1m to 8km. The resultant imagery was further processed to determine TAWS background type. The TAWS background type was refined to include soil moisture characteristics. Soil moisture analyses were obtained using in situ measurements, the Air Force's Agricultural-Meteorological (AGRMET) model and the Army's Fast All-seasons Soil Strength (FASST) model. The analyzed imagery was compared to the current default 1o latitude by 1o of longitude database in TAWS. The use of the current default TAWS background database was shown to result in TAWS ranges differing from the 1m standard range by 18-23%. The uncertainty was reduced to 5% when background resolution was improved to 8km in rural areas. By contrast, in urban regions the uncertainty was reduced to 14% when spatial resolution was reduced to 30m. These results suggest that the rural and urban designations are important to the definition of a background database.
First Lieutenant, United States Air Force
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Lumsden, Trevor Graeme. "Development and evaluation of a sugarcane yield forecasting system." Thesis, 2000. http://hdl.handle.net/10413/4955.

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There is a need in the South African sugar industry to investigate improved techniques for forecasting seasonal sugarcane yields. An accurate and timely forecast of seasonal cane yield is of great value to the industry, and could potentially allow for substantial economic savings to be made. Advances by climatologists have resulted in increasingly accurate and timely seasonal climate forecasts. These advances, coupled with the ongoing advances made in the field of crop yield simulation modelling, present the sugar industry with the possibility of obtaining improved cane yield forecasts. In particular, the lead time of these forecasts would be improved relative to traditional techniques. Other factors, such as the flexibility offered by simulation modelling in the representation of a variety of seasonal scenarios, would also contribute to the possibility of obtaining improved cane yield forecasts. The potential of applying crop yield simulation models and seasonal rainfall forecasts in cane yield forecasting was assessed in this research project. The project was conducted in the form of a case study in the Eston Mill Supply Area. Two daily time step cane yield simulation models, namely the ACRU-Thompson and CANEGRO-DSSAT models, were initially evaluated to test their ability to accurately simulate historical yields given an observed rainfall record. The model found to be the more appropriate for yield forecasting at Eston, the ACRU-Thompson model, was then used to generate yield forecasts for a number of seasons, through the application of seasonal rainfall forecasts in the model. These rainfall forecasts had previously been translated into daily rainfall values for input into the model. The sugarcane yield forecasts were then evaluated against observed yields, as well as against forecasts generated by more traditional methods, these methods being represented by a simple rainfall model and Mill Group Board estimates. Although the seasonal rainfall forecasts used in yield forecasting were found not to be particularly accurate, the proposed method provided more reliable cane yield forecasts, on average, than those using the traditional forecasting methods. A simple cost-benefit analysis indicated that the proposed method could potentially give rise to the greatest net economic benefits compared to the other methods. Recommendations are made for the practical implementation of such a method. Future areas of research are also identified.
Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 2000.
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Pillay, Maldean. "Gabor filter parameter optimization for multi-textured images : a case study on water body extraction from satellite imagery." Thesis, 2012. http://hdl.handle.net/10413/11070.

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The analysis and identification of texture is a key area in image processing and computer vision. One of the most prominent texture analysis algorithms is the Gabor Filter. These filters are used by convolving an image with a family of self similar filters or wavelets through the selection of a suitable number of scales and orientations, which are responsible for aiding in the identification of textures of differing coarseness and directions respectively. While extensively used in a variety of applications, including, biometrics such as iris and facial recognition, their effectiveness depend largely on the manual selection of different parameters values, i.e. the centre frequency, the number of scales and orientations, and the standard deviations. Previous studies have been conducted on how to determine optimal values. However the results are sometimes inconsistent and even contradictory. Furthermore, the selection of the mask size and tile size used in the convolution process has received little attention, presumably since they are image set dependent. This research attempts to verify specific claims made in previous studies about the influence of the number of scales and orientations, but also to investigate the variation of the filter mask size and tile size for water body extraction from satellite imagery. Optical satellite imagery may contain texture samples that are conceptually the same (belong to the same class), but are structurally different or differ due to changes in illumination, i.e. a texture may appear completely different when the intensity or position of a light source changes. A systematic testing of the effects of varying the parameter values on optical satellite imagery is conducted. Experiments are designed to verify claims made about the influence of varying the scales and orientations within predetermined ranges, but also to show the considerable changes in classification accuracy when varying the filter mask and tile size. Heuristic techniques such as Genetic Algorithms (GA) can be used to find optimum solutions in application domains where an enumeration approach is not feasible. Hence, the effectiveness of a GA to automate the process of determining optimum Gabor filter parameter values for a given image dataset is also investigated. The results of the research can be used to facilitate the selection of Gabor filter parameters for applications that involve multi-textured image segmentation or classification, and specifically to guide the selection of appropriate filter mask and tile sizes for automated analysis of satellite imagery.
Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2012.
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Books on the topic "Flood forecasting Computer programs"

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Flood forecasting using artificial neural networks. Lisse, Netherlands: Balkema, 2003.

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Prettenthaler, Franz, and Hansjörg Albrecher. Hochwasserrisiko und dessen Versicherung in Österreich: Evaluierung und ökonomische Analyse des von der Versicherungswirtschaft vorgeschlagenen Modells NatKat. Wien: Verlag der österreichischen Akademie der Wissenschaften, 2009.

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Jones, Perry M. Characterization of rainfall-runoff response and estimation of the effect of wetland restoration on runoff, Heron Lake basin, southwestern Minnesota, 1991-97. Mounds View, Minn: U.S. Dept. of the Interior, U.S. Geological Survey, 2000.

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Flynn, Kathleen M. User's manual for Program PeakFQ, annual flood-frequency analysis using Bulletin 17B guidelines. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2006.

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Keiichi, Toda, Miguez Marcelo Gomes, and Inoue Kazuya 1941-, eds. Flood risk simulation. Southampton: WIT, 2005.

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Caroline, Peani, and Peani Pablo, eds. Forecasting software manual. Cincinnati, Ohio: South-Western College Pub., 1999.

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7

Berenbrock, Charles. Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho. Boise, Idaho: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Berenbrock, Charles. Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho. Boise, Idaho: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Berenbrock, Charles. Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho. Boise, Idaho: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Berenbrock, Charles. Simulation of water-surface elevations for a hypothetical 100-year peak flow in Birch Creek at the Idaho National Engineering and Environmental Laboratory, Idaho. Boise, Idaho: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Book chapters on the topic "Flood forecasting Computer programs"

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Freudenthaler, Bernhard, and Reinhard Stumptner. "Adaptive Flood Forecasting for Small Catchment Areas." In Computer Aided Systems Theory – EUROCAST 2015, 211–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27340-2_27.

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Yan, Yaoxing, and Yutao Cao. "A Mode of Storm Flood Forecasting DSS Establish Ion." In Communications in Computer and Information Science, 261–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24282-3_35.

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Gamage, Dhananjali, and Kalani Ilmini. "Flood Forecasting Using Artificial Neural Network for Kalu Ganga." In Communications in Computer and Information Science, 92–102. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9129-3_7.

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Wang, Wangsong, and Yan Tang. "Watershed Flood Forecasting Based on Cluster Analysis and BP Neural Network." In Computer Supported Cooperative Work and Social Computing, 498–506. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3044-5_37.

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Liao, Weihong, and Xiaohui Lei. "Multi-model Combination Techniques for Flood Forecasting from the Distributed Hydrological Model EasyDHM." In Communications in Computer and Information Science, 396–402. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34289-9_44.

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Yan, Le, Jun Feng, Yirui Wu, and Tingting Hang. "Data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 363–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48513-9_30.

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Burkard, Simon, Frank Fuchs-Kittowski, and Anna O’Faolain de Bhroithe. "Mobile Crowd Sensing of Water Level to Improve Flood Forecasting in Small Drainage Areas." In Environmental Software Systems. Computer Science for Environmental Protection, 124–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-89935-0_11.

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Seal, Victor, Arnab Raha, Shovan Maity, Souvik Kr Mitra, Amitava Mukherjee, and Mrinal Kanti Naskar. "A Real Time Multivariate Robust Regression Based Flood Prediction Model Using Polynomial Approximation for Wireless Sensor Network Based Flood Forecasting Systems." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 432–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27317-9_44.

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Solo, Ashu M. G. "The Interdisciplinary Fields of Political Engineering, Public Policy Engineering, Computational Politics, and Computational Public Policy." In Handbook of Research on Politics in the Computer Age, 1–16. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0377-5.ch001.

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This chapter describes four interdisciplinary fields originated and defined by Ashu M. G. Solo in 2011 called political engineering, public policy engineering, computational politics, and computational public policy. Political engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in politics. Computational politics is the application of computer science or mathematics to solving problems in politics. Political engineering and computational politics include, but are not limited to, principles and methods for political decision-making, analysis, modeling, optimization, forecasting, simulation, and expression. Public policy engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in public policy. Computational public policy is the application of computer science or mathematics to solving problems in public policy. Public policy engineering and computational public policy include, but are not limited to, principles and methods for public policy formulation, decision-making, analysis, modeling, optimization, forecasting, and simulation. The chapter describes the scope of research and development in these fields, provides examples of research and development in these fields, and provides possible university curricula for academic programs in these fields.
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Serban, Cristina, and Carmen Maftei. "Using Grid Computing and Satellite Remote Sensing in Evapotranspiration Estimation." In Biometrics, 994–1016. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch039.

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The most advanced and applicable approach today in the development of environmental monitoring programs is the integration of remote sensing and Grid computing services into a monitoring and forecasting system that helps the analyst to understand the problem without being a remote sensing or computer expert. In this chapter we present the main features of Grid computing and how we can use it in conjunction with remote sensing to develop several applications that will estimate ET (Evapotranspiration), LST (Land Surface Temperature) and some vegetation indices (VI's) directly from a satellite image, these parameters playing an essential role in all activities related to water resources management.
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Conference papers on the topic "Flood forecasting Computer programs"

1

Abbassi, Kamel, Hamadi Lirathni, Mohamed Hechmi Jeridi, and Tahar Ezzedine. "Flood Forecasting with Bayesian Approach." In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2021. http://dx.doi.org/10.23919/softcom52868.2021.9559122.

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Antony Sylvia, J. Michael, M. Pushpa Rani, and Bashiru Aremu. "Analysis of IoT Big Weather Data For Early Flood Forecasting System." In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2021. http://dx.doi.org/10.1109/icecct52121.2021.9616941.

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Soomlek, Chitsutha, Nattawadee Kaewchainam, Thawat Simano, and Chakchai So-In. "Using backpropagation neural networks for flood forecasting in PhraNakhon Si Ayutthaya, Thailand." In 2015 International Computer Science and Engineering Conference (ICSEC). IEEE, 2015. http://dx.doi.org/10.1109/icsec.2015.7401424.

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Finger, Alice, and Aline Loreto. "Applications of Numerical Methods with Linear Complexity in Flood Forecasting in Rivers." In 2011 Workshop-School on Theoretical Computer Science (WEIT). IEEE, 2011. http://dx.doi.org/10.1109/weit.2011.32.

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Gomes, João L., Gonçalo Jesus, João Rogeiro, Anabela Oliveira, Ricardo da Costa, and André B. Fortunato. "Molines – towards a responsive web platform for flood forecasting and risk mitigation." In 2015 Federated Conference on Computer Science and Information Systems. IEEE, 2015. http://dx.doi.org/10.15439/2015f265.

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Ullah, Kaleem, Zahid Ullah, Irfanullah Khan, Fazale Wahab, Waqar Uddin, Athar Waseem, Aun Haider, Ghulam Hafeez, Sahibzada Muhammad Ali, and Khadim Ullah Jan. "Load Forecasting Schemes and Demand Response Programs within Smart Grid." In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, 2020. http://dx.doi.org/10.1109/icecce49384.2020.9179280.

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Yin, Zhiyuan, Fang Yang, and Tieyuan Shen. "Application of Set Pair Analysis on QPE and Rain Gauge in Flood Forecasting." In 2017 2nd International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/isaeece-17.2017.10.

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Zainorzuli, Siti Maisarah, Syahrul Afzal Che Abdullah, Ramli Adnan, and Fazlina Ahmat Ruslan. "Comparative Study of Elman Neural Network (ENN) and Neural Network Autoregressive With Exogenous Input (NARX) For Flood Forecasting." In 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2019. http://dx.doi.org/10.1109/iscaie.2019.8743796.

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Deshmukh, Rahul P., and A. A. Ghatol. "Notice of Retraction: Comparative study of Jorden and Elman model of neural network for short term flood forecasting." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5564917.

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Wang, Kai, Mingkai Qian, Shunfeng Peng, Shijin Xu, Fengsheng Li, and Min Xu. "The Deft-FEWS flood forecasting system and its application in the middle-upper parts of the Huaihe River Basin, China." In 2013 International Conference on Software Engineering and Computer Science. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icsecs-13.2013.43.

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