Bibliografías temáticas / Flood forecasting

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Literatura académica sobre el tema "Flood forecasting"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Flood forecasting"

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Kechkhoshvili, Erekle, and Irina Khutsishvili. "For Flood Forecasting Issues." Works of Georgian Technical University, no. 2(532) (June 10, 2024): 265–72. http://dx.doi.org/10.36073/1512-0996-2024-2-265-272.

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. Global climate change has caused sharp increasing of natural calamities, including floods. In the course of recent period, over the entire world, every year there are occurring tens of cases of disastrous floods and waterflows characterized by damages worth of several millions and human losses. The issue of forecasting waterflows and floods, in general, is discussed in the article. There are given basic differentiating features-characteristics existing between spring floods and rain-caused waterflows. The methodology of forecasting related decision-making based on the Statistical Fuzzy Analy
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Xu, Wei, and Yong Peng. "Research on classified real-time flood forecasting framework based on K-means cluster and rough set." Water Science and Technology 71, no. 10 (2015): 1507–15. http://dx.doi.org/10.2166/wst.2015.128.

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This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category
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Ren, Juanhui, Bo Ren, Qiuwen Zhang, and Xiuqing Zheng. "A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region." Water 11, no. 9 (2019): 1848. http://dx.doi.org/10.3390/w11091848.

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Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP1, HSP
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Mustamin, Muhammad Rifaldi, Farouk Maricar, Rita Tahir Lopa, and Riswal Karamma. "Integration of UH SUH, HEC-RAS, and GIS in Flood Mitigation with Flood Forecasting and Early Warning System for Gilireng Watershed, Indonesia." Earth 5, no. 3 (2024): 274–93. http://dx.doi.org/10.3390/earth5030015.

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A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia. This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference for flood forecasting and early warning systems in anticipating the timing and occurrence of floods, as well as GIS in the spatial modeling of flood-prone areas. Broadly speaking, this research is divided into four stages, namely, a flood hydrological analysis us
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Anafi, Nurin Fadhlina Mohd, Norzailawati Mohd Noor, and Hasti Widyasamratri. "A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge." Jurnal Planologi 20, no. 2 (2023): 150. http://dx.doi.org/10.30659/jpsa.v20i2.30765.

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Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental re
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Langdon, M. "Forecasting flood." Engineering & Technology 4, no. 7 (2009): 40–42. http://dx.doi.org/10.1049/et.2009.0706.

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Thiemig, V., B. Bisselink, F. Pappenberger, and J. Thielen. "A pan-African Flood Forecasting System." Hydrology and Earth System Sciences Discussions 11, no. 5 (2014): 5559–97. http://dx.doi.org/10.5194/hessd-11-5559-2014.

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Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this paper the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 where important floods were observed. Results were verified with ground measurements of 36 subcatchments as well as
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Brilly, M., and M. Polic. "Public perception of flood risks, flood forecasting and mitigation." Natural Hazards and Earth System Sciences 5, no. 3 (2005): 345–55. http://dx.doi.org/10.5194/nhess-5-345-2005.

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Abstract. A multidisciplinary and integrated approach to the flood mitigation decision making process should provide the best response of society in a flood hazard situation including preparation works and post hazard mitigation. In Slovenia, there is a great lack of data on social aspects and public response to flood mitigation measures and information management. In this paper, two studies of flood perception in the Slovenian town Celje are represented. During its history, Celje was often exposed to floods, the most recent serious floods being in 1990 and in 1998, with a hundred and fifty re
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V.E.S.Mahendra, Kumar, and Khasim Sayyad. "Development of a Flood Forecasting System using Artificial Neural Networks." International Journal for Modern Trends in Science and Technology 11, no. 05 (2025): 1192–95. https://doi.org/10.5281/zenodo.15511223.

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<em>Floods pose significant risks to communities, infrastructure, and ecosystems worldwide, making early warning systems essential for mitigating their impacts. Traditional flood forecasting models rely on complex mathematical equations and meteorological data, but they often fail to capture the nonlinear relationships between various factors influencing floods. This paper proposes the development of a flood forecasting system using Artificial Neural Networks (ANNs), which can model these nonlinear relationships more effectively. The proposed system uses real-time data from weather stations, r
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Mohammed Salman, Mohammed Wasim, Mohammed Adnan Ahmed, and Ms. Sumrana Tabassum. "Flood Forecasting Model Using Federated Learning." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 328–35. https://doi.org/10.62647/ijitce2025v13i2spp328-335.

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Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred
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Tesis sobre el tema "Flood forecasting"

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Simoes, Nuno Eduardo da Cruz. "Urban pluvial flood forecasting." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10545.

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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 (S
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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.

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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
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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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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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Fayegh, A. David. "Flood advisor : an expert system for flood estimation." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25069.

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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 ra
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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.

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Zachary, A. Glen. "The estimated parameter flood forecasting model." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25148.

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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, infiltrati
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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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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.

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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 eve
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Cerda-Villafana, Gustavo. "Artificial intelligence techniques in flood forecasting." Thesis, University of Bristol, 2005. http://hdl.handle.net/1983/09d0faea-8622-4609-a33c-e4baefa304f5.

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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 operation
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Libros sobre el tema "Flood forecasting"

1

Romanowicz, Renata J., and Marzena Osuch, eds. Stochastic Flood Forecasting System. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6.

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Muthusi, F. M. Somalia flood forecasting system. Somalia Water and Land Information Management, 2009.

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Seneka, Michael. Paddle River Dam probable maximum flood. Alberta Environment, Environmental Assurance, Environmental Operations Division, Hydrology Branch, Surface Water Section, 2002.

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Ontoyin, Yakubu. Regional flood estimation in Ghanaian rivers. Water Resources Research Institute (CSIR), 1985.

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Mastin, M. C. Real-time flood alert and simulation of river flood discharges in the Puyallup River Basin, Washington. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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Dinicola, Karen. The "100-year flood". USGS Washington Water Science Center, 1997.

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Dinicola, Karen. The " 100-year flood". USGS Washington Water Science Center, 1997.

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Dinicola, Karen. The " 100-year flood". USGS Washington Water Science Center, 1997.

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Dinicola, Karen. The " 100-year flood.". U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Dinicola, Karen. The " 100-year flood". USGS Washington Water Science Center, 1997.

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Capítulos de libros sobre el tema "Flood forecasting"

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Sharma, Priyanka, Pravin Patil, and Saeid Eslamian. "Flood Forecasting." In Flood Handbook. CRC Press, 2022. http://dx.doi.org/10.1201/9780429463938-9.

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Sene, Kevin. "Flood Forecasting." In Flash Floods. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5164-4_5.

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Sene, Kevin. "Flood Forecasting." In Hydrometeorology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58269-1_8.

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Cunge, J. A., M. Erlich, J. L. Negre, and J. L. Rahuel. "Construction and Assessment of Flood Forecasting Scenarios in the Hydrological Forecasting System HFS/SPH." In Floods and Flood Management. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-1630-5_20.

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Vuillaume, Jean-François, and Akinola Adesuji Komolafe. "Flood Modeling and Forecasting Uncertainty." In Flood Handbook. CRC Press, 2022. http://dx.doi.org/10.1201/9780429463938-6.

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Mutreja, Kedar N., Yin Au-Yeung, and Ir Martono. "Flood Forecasting Model for Citanduy River Basin." In Flood Hydrology. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3957-8_17.

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Todini, Ezio. "From Real-Time Flood Forecasting to Comprehensive Flood Risk Management Decision Support Systems." In Floods and Flood Management. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-1630-5_21.

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Gutry-Korycka, M., A. Mirończuk, and A. Hościło. "Land Cover Change in the Middle River Vistula Catchment." In Stochastic Flood Forecasting System. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6_1.

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Romanowicz, Renata J., and Marzena Osuch. "Stochastic Transfer Function Based Emulator for the On-line Flood Forecasting." In Stochastic Flood Forecasting System. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6_10.

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Kiczko, Adam, Renata J. Romanowicz, Marzena Osuch, and Florian Pappenberger. "Adaptation of the Integrated Catchment System to On-line Assimilation of ECMWF Forecasts." In Stochastic Flood Forecasting System. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18854-6_11.

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Actas de conferencias sobre el tema "Flood forecasting"

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Boughale, Rahma, Amira Zrelli, and Tahar Ezzedine. "Enhancing Flood Forecasting with BiLSTM Networks." In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA). IEEE, 2024. https://doi.org/10.1109/aiccsa63423.2024.10912620.

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Gunanandhini, S., Livyashree M, Maheshwaran R. R, Palanisamy K. S, and Niranjan Kumar V. "Forecasting and Real Time Flood Monitoring System using IoT." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716883.

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Priyadharshini, P., R. Parvathi, Lijetha C. Jaffrin, R. Santhana Krishnan, J. Relin Francis Raj, and T. Saranya. "ConvLSTM for Flood Forecasting: A Deep Neural Network Model for Integrated Temporal and Spatial Flood Analysis." In 2025 3rd International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2025. https://doi.org/10.1109/icici65870.2025.11069957.

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K, Jaraline Kirubavathy, and Thulasi Bai V. "An Intelligent ResNet-152 CNN framework for Anticipatory Flood Forecasting." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923325.

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Yu, Xue-jun, Jian Meng, Lei Sha, and Lin-hai Liu. "Research on Flood Forecasting Model Based on Artificial Intelligence Technology." In 2025 10th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 2025. https://doi.org/10.1109/icccbda64898.2025.11030434.

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Dalai, Chitaranjan, Arun Elias, Goutham Kacheru, Purba Das, Ashif Mohammad, and N. Chidambararaj. "Flood Forecasting Model Using LSTM-Neural Network-Application and Challenges." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031939.

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Zeng, Peng, Wei Zhang, Yangjun Zhou, and Guohui Wei. "Bayesian Probability Intelligent Forecasting Model Based on Rainfall during Flood Season." In 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS). IEEE, 2024. http://dx.doi.org/10.1109/ispds62779.2024.10667504.

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Ramya, R., Mohamed Thanseem K, Vimalraj V, and Vinith Kumar S. "Flood Forecasting System based on Water Monitoring using IoT-Machine Learning." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988088.

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Yordanova, Valeriya, Silviya Stoyanova, Snezhanka Balabanova, Georgy Koshinchanov, and Vesela Stoyanova. "FLASH FLOOD FORECASTING USING FLASH FLOOD GUIDANCE SYSTEM PRODUCTS." In 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/3.1/s12.11.

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Flash floods are defined as rapidly developing extreme events caused by heavy or excessive amounts of rainfall. Flash floods usually occur over a relatively small area within six hours or less of the extreme event with quite a rapid streamflow rise and fall. Increased occurrence of flash flood events is expected due to climate change and increase in extreme precipitation events [1]. Flash flood forecasting is still a challenge for hydrologists and water professionals due to the complex nature of the event itself. Besides having sufficient background in hydrological and meteorological forecasti
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Stoyanova, Vesela, Georgy Koshinchanov, and Silviya Stoyanova. "COMPARISON OF NATIONAL, EUROPEAN AND BLACK SEA REGION FLASH FLOOD FORECASTING PRODUCTS FOR THE TERRITORY OF BULGARIA." In 23rd SGEM International Multidisciplinary Scientific GeoConference 2023. STEF92 Technology, 2023. http://dx.doi.org/10.5593/sgem2023/3.1/s12.07.

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Flash floods occur in small areas and in a short time after an extreme precipitation event - generally less than six hours. The intensity of the precipitation, the location and distribution of the precipitation, the land use and topography, soil type, etc. determine just how quickly Flash Flooding can occur. These types of floods are extremely dangerous and it is very important to pay special attention to their forecasting. Due to the complex nature of flash floods, in addition to the qualification and experience of the hydrologist involved in forecasting this type of extreme event, it is impo
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Informes sobre el tema "Flood forecasting"

1

Peters, John C. Application of Rainfall-Runoff Simulation for Flood Forecasting. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada273140.

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Shi, Jimeng, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, and Giri Narasimhan. Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317672.

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Teillet, P. M., R. P. Gauthier, T. J. Pultz, et al. A Soil Moisture Sensorweb for Use in Flood Forecasting Applications. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/220059.

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Melaney, M., and S. Frey. Enhancing flood and drought forecasting tools in the South Nation River Watershed. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2018. http://dx.doi.org/10.4095/306552.

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Hugeback, Kyle. A Comparison of HREF and HRRRE Predictions for Ensemble Flash Flood Forecasting. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-24.

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Charley, William J. The Estimation of Rainfall for Flood Forecasting Using Radar and Rain Gage Data. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada200802.

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Deschamps, A., T. J. Pultz, A. Pietroniro, and K. Best. Temporal soil moisture estimates from Radarsat-1 and Envisat ASAR for flood forecasting. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2004. http://dx.doi.org/10.4095/220092.

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Perera, Duminda, Ousmane Seidou, Jetal Agnihotri, et al. Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects. United Nations University Institute for Water, Environment and Health, 2019. http://dx.doi.org/10.53328/mjfq3791.

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Floods are major water-related disasters that affect millions of people resulting in thousands of mortalities and billiondollar losses globally every year. Flood Early Warning Systems (FEWS) - one of the floods risk management measures - are currently operational in many countries. The UN Office for Disaster Risk Reduction recognises their importance and strongly advocates for an increase in their availability under the targets of the Sendai Framework for Disaster Risk Reduction, and Sustainable Development Goals (SDGs). However, despite widespread recognition of the importance of FEWS for dis
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Messina, Francesca, Ioannis Georgiou, Melissa Baustian, et al. Real-time forecasting model development work plan. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47599.

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The objective of the Lowermost Mississippi River Management Program is to move the nation toward more holistic management of the lower reaches of the Mississippi River through the development and use of a science-based decision-making framework. There has been substantial investment in the last decade to develop multidimensional numerical models to evaluate the Lowermost Mississippi River (LMMR) hydrodynamics, sediment transport, and salinity dynamics. The focus of this work plan is to leverage the existing scientific knowledge and models to improve holistic management of the LMMR. Specificall
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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. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6535.

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