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Artículos de revistas sobre el tema "Flood forecasting"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Li, Jingyu, Yangbo Chen, Yanzheng Zhu, and Jun Liu. "Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model." Sustainability 15, no. 14 (2023): 11225. http://dx.doi.org/10.3390/su151411225.

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The uneven distribution of meteorological stations in small and medium-sized watersheds in China and the lack of measured hydrological data have led to difficulty in flood simulation and low accuracy in flood forecasting. Traditional hydrological models no longer achieve the forecasting accuracy needed for flood prevention. To improve the simulation accuracy of floods and maximize the use of hydrological information from small and medium-sized watersheds, high-precision hydrological models are needed as a support mechanism. This paper explores the applicability of the Liuxihe model for flood s

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12

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 (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

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13

Arduino, G., P. Reggiani, and E. Todini. "Recent advances in flood forecasting and flood risk assessment." Hydrology and Earth System Sciences 9, no. 4 (2005): 280–84. http://dx.doi.org/10.5194/hess-9-280-2005.

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Abstract. Recent large floods in Europe have led to increased interest in research and development of flood forecasting systems. Some of these events have been provoked by some of the wettest rainfall periods on record which has led to speculation that such extremes are attributable in some measure to anthropogenic global warming and represent the beginning of a period of higher flood frequency. Whilst current trends in extreme event statistics will be difficult to discern, conclusively, there has been a substantial increase in the frequency of high floods in the 20th century for basins greate

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14

Mistry, Shivangi, and Falguni Parekh. "Flood Forecasting Using Artificial Neural Network." IOP Conference Series: Earth and Environmental Science 1086, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1755-1315/1086/1/012036.

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Abstract The process of assessing the timing, amount, and period of flood events based on observed features of a river basin is known as flood forecasting. Floods cause lots of damage to properties and create a risk to human life. Flood forecasting is critical for developing appropriate flood risk management strategies, reducing flood hazards, evacuating people from flood-prone areas. The main objective of this study is to apply artificial neural networks for forecasting of river flow in the Deo River, located in Gujarat. Rainfall and discharge are the parameters considered for model developme

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15

Wu, Heng Qing, Qiang Huang, Wei Xu, and Shu Feng Xi. "Application of K-Means Cluster and Rough Set in Classified Real-Time Flood Forecasting." Advanced Materials Research 1092-1093 (March 2015): 734–41. http://dx.doi.org/10.4028/www.scientific.net/amr.1092-1093.734.

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A new classified real-time flood forecasting framework was presented. Firstly, the historical floods were classified by K-means cluster, according to the hydrological factors. Then rough set was used to extract operation rules for flood forecasting. Following, the conceptual hydrological model was constructed and Genetic Algorithm (GA) was used to calibrate the hydrological model parameters. In simulation, River A is taken as study example. The categories of parameters are selected in operation according to flood information and rules. The result is compared with traditional flood forecasting.

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16

Zhen, Yiwei, Ming Guo, Penghui Li, Jianzheng Chen, and Yucheng Liu. "Study on flood level forecasting of tidal reach in Puyang River basin." Journal of Physics: Conference Series 2865, no. 1 (2024): 012008. http://dx.doi.org/10.1088/1742-6596/2865/1/012008.

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Abstract The downstream section of the Puyang River is a tidal river reach subject to both upstream floods and downstream tides. This combined impact results in an unstable relationship between water level and discharge at the forecast station, making flood level forecasting challenging. This paper took the Linpu station on the Puyang River in Zhejiang Province as the research object. Based on the historical flood data, the cause of high water levels at Linpu station was analyzed. The flood levels at Linpu station were decomposed into flood increments and basic tidal levels. Using the multiple

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17

Pandey, Rajendra P., Meena Desai, and Rajnesh Panwar. "Hybrid deep learning model for flood frequency assessment and flood forecasting." Multidisciplinary Science Journal 5 (August 18, 2023): 2023ss0204. http://dx.doi.org/10.31893/multiscience.2023ss0204.

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The most common and persistent natural hazard to people across the globe is flooding. The frequency of floods in a given place is defined as the likelihood and intensity of floods occurring there within a certain period. Examining historical flood data and using techniques are often used to determine the likelihood that a flood of a certain size would occur in a specific location. The method of flood prediction involves making forecasts on the frequency and severity of flooding. It may be influenced by a number of factors, including the topography, river flow, soil moisture content, and the pe

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18

Zhang, Yue, Juanhui Ren, Rui Wang, Feiteng Fang, and Wen Zheng. "Multi-Step Sequence Flood Forecasting Based on MSBP Model." Water 13, no. 15 (2021): 2095. http://dx.doi.org/10.3390/w13152095.

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Establishing a model predicting river flow can effectively reduce huge losses caused by floods. This paper proposes a multi-step time series forecasting model based on multiple input and multiple output strategies, and this model is applied to the flood forecasting process of a river basin in Shanxi, which effectively improves the engineering application value of the flood forecasting model based on deep learning. The experimental results show that after considering the seasonal characteristics of the river channel and screening the influencing factors, a simple neural network model can accura

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19

Gao, Yuan. "Research on the Application of Machine Learning Technology in Hydrological Flood Prediction." Journal of Computer, Signal, and System Research 2, no. 2 (2025): 28–34. https://doi.org/10.71222/se6cyv71.

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Urban flooding disasters frequently occur in our country, severely affecting the national development process, anticipating the probability and severity of floods can effectively reduce the negative impacts caused by floods, the rapid progress of hydrology has accelerated the development of flood prediction research. Currently, a lot of machine learning methods are widely applied in the field of flood forecasting based on hydrology, which holds great significance for social development. First, the hydrological models currently used for flood forecasting are introduced. Then, the application of

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20

Chitwatkulsiri, Detchphol, and Hitoshi Miyamoto. "Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects." Water 15, no. 1 (2023): 178. http://dx.doi.org/10.3390/w15010178.

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Many urban areas in tropical Southeast Asia, e.g., Bangkok in Thailand, have recently been experiencing unprecedentedly intense flash floods due to climate change. The rapid flood inundation has caused extremely severe damage to urban residents and social infrastructures. In addition, urban Southeast Asia usually has inadequate capacities in drainage systems, complicated land use patterns, and a large vulnerable population in limited urban areas. To reduce the urban flood risk and enhance the resilience of vulnerable urban communities, it has been of essential importance to develop real-time u

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21

Xu, Yiyuan, Jianhui Zhao, Biao Wan, Jinhua Cai, and Jun Wan. "Flood Forecasting Method and Application Based on Informer Model." Water 16, no. 5 (2024): 765. http://dx.doi.org/10.3390/w16050765.

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Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Info

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22

Tsegaw, Aynalem Tassachew, Thomas Skaugen, Knut Alfredsen, and Tone M. Muthanna. "A dynamic river network method for the prediction of floods using a parsimonious rainfall-runoff model." Hydrology Research 51, no. 2 (2019): 146–68. http://dx.doi.org/10.2166/nh.2019.003.

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Abstract Floods are one of the major climate-related hazards and cause casualties and substantial damage. Accurate and timely flood forecasting and design flood estimation are important to protect lives and property. The Distance Distribution Dynamic (DDD) is a parsimonious rainfall-runoff model which is being used for flood forecasting at the Norwegian flood forecasting service. The model, like many other models, underestimates floods in many cases. To improve the flood peak prediction, we propose a dynamic river network method into the model. The method is applied for 15 catchments in Norway

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23

Chen, Y., J. Li, S. Huang, and Y. Dong. "Study of Beijiang catchment flash-flood forecasting model." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 150–55. http://dx.doi.org/10.5194/piahs-368-150-2015.

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Abstract. Beijiang catchment is a small catchment in southern China locating in the centre of the storm areas of the Pearl River Basin. Flash flooding in Beijiang catchment is a frequently observed disaster that caused direct damages to human beings and their properties. Flood forecasting is the most effective method for mitigating flash floods, the goal of this paper is to develop the flash flood forecasting model for Beijiang catchment. The catchment property data, including DEM, land cover types and soil types, which will be used for model construction and parameter determination, are downl

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24

Riedel, Lukas, Thomas Röösli, Thomas Vogt, and David N. Bresch. "Fluvial flood inundation and socio-economic impact model based on open data." Geoscientific Model Development 17, no. 13 (2024): 5291–308. http://dx.doi.org/10.5194/gmd-17-5291-2024.

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Abstract. Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art risk modeling framework. It uses openly available data to rapidly compute flood inundation footprin

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25

lakshmi, Divya. "Flood Forecasting Model Using KNN Algorithm." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48223.

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Abstract - Floods are among the most devastating and frequent natural disasters, responsible for significant loss of life, widespread property damage, and serious disruption to transportation, communication, and essential infrastructure. With climate change, rapid urbanization, and deforestation contributing to the increasing occurrence and intensity of floods, the need for advanced, data-driven solutions has become more urgent than ever. In response to this growing challenge, a machine learning-based application called FloodCare has been developed to provide real-time flood prediction and ris

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26

Zhang, Yue, Daiwei Pan, Jesse Van Griensven, Simon X. Yang, and Bahram Gharabaghi. "Intelligent flood forecasting and warning: a survey." Intelligence & Robotics 3, no. 2 (2023): 190–212. http://dx.doi.org/10.20517/ir.2023.12.

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Accurately predicting the magnitude and timing of floods is an extremely challenging problem for watershed management, as it aims to provide early warning and save lives. Artificial intelligence for forecasting has become an emerging research field over the past two decades, as computer technology and related areas have been developed in depth. In this paper, three typical machine learning algorithms for flood forecasting are reviewed: supervised learning, unsupervised learning, and semi-supervised learning. Special attention is given to deep learning approaches due to their better performance

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27

Pham Thanh Long, Nguyen Thao Hien, Nguyen Thu Huong, and Le Thanh Trang. "BUILDING INTEGRATED TOOLS FOR FLOOD WARNING AND INUNDATION FORECAST OF RIVER BASINS IN KHANH HOA PROVINCE." Tạp chí Khoa học Biến đổi khí hậu, no. 27 (September 30, 2023): 65–74. http://dx.doi.org/10.55659/2525-2496/27.85972.

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In order to strengthen the flood warning and inundation forecasting system in the South Central region, the authors aim to study an integrated tool for flood disaster prevention, pilot application in two main river basins in Khanh Hoa Province. This area often suffers great damage due to the influence of storms and tropical depressions, which can be mentioned as floods in 1980, 1986, 1993, 1998, 1999, 2003, 2009, 2013, and 2016. The tool is the connection of scientific and technical products in automatic monitoring equipment and transmits real-time rainfall, water level data; as input for fore

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28

Zhan, Xiaoyan, Hui Qin, Yongqi Liu, et al. "Variational Bayesian Neural Network for Ensemble Flood Forecasting." Water 12, no. 10 (2020): 2740. http://dx.doi.org/10.3390/w12102740.

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Disastrous floods are destructive and likely to cause widespread economic losses. An understanding of flood forecasting and its potential forecast uncertainty is essential for water resource managers. Reliable forecasting may provide future streamflow information to assist in an assessment of the benefits of reservoirs and the risk of flood disasters. However, deterministic forecasting models are not able to provide forecast uncertainty information. To quantify the forecast uncertainty, a variational Bayesian neural network (VBNN) model for ensemble flood forecasting is proposed in this study.

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Cluckie, I. D., and D. Han. "Fluvial Flood Forecasting." Water and Environment Journal 14, no. 4 (2000): 270–76. http://dx.doi.org/10.1111/j.1747-6593.2000.tb00260.x.

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Yao, Yi, Zhongmin Liang, Weimin Zhao, Xiaolei Jiang, and Binquan Li. "Performance assessment of hydrologic uncertainty processor through integration of the principal components analysis." Journal of Water and Climate Change 10, no. 2 (2017): 373–90. http://dx.doi.org/10.2166/wcc.2017.137.

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Abstract Uncertainty analysis is important and should be always considered when using models for flood forecasting. In this paper, the ‘Principal Components Analysis-Hydrologic Uncertainty Processor’ (PCA-HUP) was developed for probabilistic flood forecasting (PFF) and further evaluated in the middle Yellow River, China. Due to the severe sediment erosion, small and medium floods drain in the main channel (normal floods) while large floods would spill over the bank and drain in river floodplains (overbank floods). Thus, the practical routing methods were used to provide the deterministic flood

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31

Ushiyama, Tomoki, Takahiro Sayama, and Yoichi Iwami. "Ensemble Flood Forecasting of Typhoons Talas and Roke at Hiyoshi Dam Basin." Journal of Disaster Research 11, no. 6 (2016): 1032–39. http://dx.doi.org/10.20965/jdr.2016.p1032.

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In order to be able to issue flood warnings not hours but days in advance, numerical weather prediction (NWP) is essential to the forecasting of flood-producing rainfall. The regional ensemble prediction system (EPS), advanced NWP on a local scale, has a high potential to improve flood forecasting through the quantitative prediction of precipitation. In this study, the predictability of floods using the ensemble flood forecasting system, which is composed of regional EPS and a distributed hydrological model, was investigated. Two flood events which took place in a small basin in Japan in 2010

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32

Shinde Sanket and Vaibhav Shelar. "Behavior of Flood Resistant Building and Ductile Detailing of G +7 RC Building Using IS 13920-2016." World Journal of Advanced Engineering Technology and Sciences 9, no. 1 (2023): 182–92. http://dx.doi.org/10.30574/wjaets.2023.9.1.0158.

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Floods are one of the most widespread and destructive natural disasters occurring in the world and with the increase in constructions along river courses and concentration of population around floodplain areas, flood-induced damages have been continuously increasing. The annual disaster record reveals that flood occurrence increased about ten folds over the past five decades. Thus, floods are posing a great threat and challenge to planers, design engineers, insurance industries, policymakers, and to the governments. Structural and non-structural measures can be used to deal with floods. Struct

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Song, Tianyu, Wei Ding, Jian Wu, Haixing Liu, Huicheng Zhou, and Jinggang Chu. "Flash Flood Forecasting Based on Long Short-Term Memory Networks." Water 12, no. 1 (2019): 109. http://dx.doi.org/10.3390/w12010109.

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Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory

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34

Chang, Fi-John, Yen-Chang Chen, and Jin-Ming Liang. "Fuzzy Clustering Neural Network as Flood Forecasting Model." Hydrology Research 33, no. 4 (2002): 275–90. http://dx.doi.org/10.2166/nh.2002.0008.

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Flood forecasting is always a challenge in Taiwan, which has a subtropical climate and high mountains. This paper develops a fuzzy clustering neural network (FCNN), and implements this novel structure and reasoning process for flood forecasting. The FCNN has a hybrid learning scheme; the unsupervised learning scheme employs fuzzy min-max clustering to extract information from the input data. The supervised learning scheme uses linear regression to determine the weights of FCNN. The network, which learns from examples, is a hydrological processes theory-free estimator. Most of the parameters, w

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35

Zhang, Yue, Zhaohui Gu, Jesse Van Griensven Thé, Simon X. Yang, and Bahram Gharabaghi. "The Discharge Forecasting of Multiple Monitoring Station for Humber River by Hybrid LSTM Models." Water 14, no. 11 (2022): 1794. http://dx.doi.org/10.3390/w14111794.

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An early warning flood forecasting system that uses machine-learning models can be utilized for saving lives from floods, which are now exacerbated due to climate change. Flood forecasting is carried out by determining the river discharge and water level using hydrologic models at the target sites. If the water level and discharge are forecasted to reach dangerous levels, the flood forecasting system sends warning messages to residents in flood-prone areas. In the past, hybrid Long Short-Term Memory (LSTM) models have been successfully used for the time series forecasting. However, the predict

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Nguyen, Dinh Ty, and Shien-Tsung Chen. "Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods." Water 12, no. 3 (2020): 787. http://dx.doi.org/10.3390/w12030787.

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Probabilistic flood forecasting, which provides uncertain information in the forecasting of floods, is practical and informative for implementing flood-mitigation countermeasures. This study adopted various machine learning methods, including support vector regression (SVR), a fuzzy inference model (FIM), and the k-nearest neighbors (k-NN) method, to establish a probabilistic forecasting model. The probabilistic forecasting method is a combination of a deterministic forecast produced using SVR and a probability distribution of forecast errors determined by the FIM and k-NN method. This study p

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37

Lee, Jung Hwan, Gi Moon Yuk, Hyeon Tae Moon, and Young-Il Moon. "Integrated Flood Forecasting and Warning System against Flash Rainfall in the Small-Scaled Urban Stream." Atmosphere 11, no. 9 (2020): 971. http://dx.doi.org/10.3390/atmos11090971.

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The flood forecasting and warning system enable an advanced warning of flash floods and inundation depths for disseminating alarms in urban areas. Therefore, in this study, we developed an integrated flood forecasting and warning system combined inland-river that systematized technology to quantify flood risk and flood forecasting in urban areas. LSTM was used to predict the stream depth in the short-term inundation prediction. Moreover, rainfall prediction by radar data, a rainfall-runoff model combined inland-river by coupled SWMM and HEC-RAS, automatic simplification module of drainage netw

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Oleg, Mandryk, Oliynyk Andriy, Mykhailyuk Roman, and Feshanych Lidiia. "Flood Development Process Forecasting Based on Water Resources Statistical Data." Grassroots Journal of Natural Resources 4, no. 2 (2021): 65–76. https://doi.org/10.33002/nr2581.6853.040205.

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The Ukrainian Carpathians is the territory with a great threat of floods. This is due to natural and climatic conditions of this region, which is characterized by mountainous terrain, high density of hydrological network and a significant amount of precipitation. Amount of precipitation here ranges from 600 mm on plains to 1,600 mm on mountain tops. The main factors of floods occurrence are excessive precipitation, low water permeability of soil and a high proportion of low-permeability rocks (flysch layers with a predominance of clay layers). Therefore, catastrophic floods in the region were

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Perumal, Muthiah, Tommaso Moramarco, Silvia Barbetta, Florisa Melone, and Bhabagrahi Sahoo. "Real-time flood stage forecasting by Variable Parameter Muskingum Stage hydrograph routing method." Hydrology Research 42, no. 2-3 (2011): 150–61. http://dx.doi.org/10.2166/nh.2011.063.

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The application of a Variable Parameter Muskingum Stage (VPMS) hydrograph routing method for real-time flood forecasting at a river gauging site is demonstrated in this study. The forecast error is estimated using a two-parameter linear autoregressive model with its parameters updated at every routing time interval of 30 minutes at which the stage observations are made. This hydrometric data-based forecast model is applied for forecasting floods at the downstream end of a 15 km reach of the Tiber River in Central Italy. The study reveals that the proposed approach is able to provide reliable f

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Thiemig, V., B. Bisselink, F. Pappenberger, and J. Thielen. "A pan-African medium-range ensemble flood forecast system." Hydrology and Earth System Sciences 19, no. 8 (2015): 3365–85. http://dx.doi.org/10.5194/hess-19-3365-2015.

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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 by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified b

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Stanley, S. J., and R. Gerard. "Ice jam flood forecasting: Hay River, N.W.T." Canadian Journal of Civil Engineering 19, no. 2 (1992): 212–23. http://dx.doi.org/10.1139/l92-027.

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Much of the town of Hay River, N.W.T., is located on the low-lying land of the Hay River delta, and is subject to severe ice jam floods every decade or so. As a first line of defence against these floods, it was proposed that an ice jam flood forecast procedure be developed. The major components of the study included a review of historical flood data, resident interviews, field surveys, and observations of the delta ice regime. It was found that a 1–2 day forecast of discharge in Hay River can be directly determined from discharges measured at a Water Survey of Canada gauging station upstream.

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Gong, Junchao, Youbing Hu, Cheng Yao, et al. "The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China." Water 16, no. 1 (2023): 103. http://dx.doi.org/10.3390/w16010103.

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The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to dri

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El Khalki, El Mahdi, Yves Tramblay, Arnau Amengual, et al. "Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco." Water 12, no. 2 (2020): 437. http://dx.doi.org/10.3390/w12020437.

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Flash floods are common in small Mediterranean watersheds and the alerts provided by real-time monitoring systems provide too short anticipation times to warn the population. In this context, there is a strong need to develop flood forecasting systems in particular for developing countries such as Morocco where floods have severe socio-economic impacts. In this study, the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) meteorological models are evaluated to forecast flood e

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Chaudhari, Ronak P., Shantanu R. Thorat, Darshan J. Mehta, Sahita I. Waikhom, Vipinkumar G. Yadav, and Vijendra Kumar. "Comparison of soft-computing techniques: Data-driven models for flood forecasting." AIMS Environmental Science 11, no. 5 (2024): 741–58. http://dx.doi.org/10.3934/environsci.2024037.

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<p>Accurate flood forecasting is a crucial process for predicting the timing, occurrence, duration, and magnitude of floods in specific zones. This prediction often involves analyzing various hydrological, meteorological, and environmental parameters. In recent years, several soft computing techniques have been widely used for flood forecasting. In this study, flood forecasting for the Narmada River at the Hoshangabad gauging site in Madhya Pradesh, India, was conducted using an Artificial Neural Network (ANN) model, a Fuzzy Logic (FL) model, and an Adaptive Neuro-Fuzzy Inference System

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Lindenschmidt, Karl-Erich, Robert Briggs, Amir Ali Khan, and Thomas Puestow. "Elements and Processes Required for the Development of a Spring-Breakup Ice-Jam Flood Forecasting System (Churchill River, Atlantic Canada)." Water 16, no. 11 (2024): 1557. http://dx.doi.org/10.3390/w16111557.

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Spring-breakup ice-jam floods are a major hazard for many rivers in cold regions. They can cause severe damage to infrastructure, property, and ecosystems along riverbanks. To reduce the risk and impact of these events, it is essential to develop reliable and timely forecasting systems that can provide early warning and guidance for mitigation actions. In this paper, we highlight the elements and processes required for the successful development of a spring-breakup ice-jam flood forecasting system, using the lower Churchill River in Labrador, Canada as a case study. We review the existing fore

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Kumar, Sanjeet, Madhusudhan M. Reddy, Meena Isukapatla, and Mara Suneel Kumar Reddy. "Flood frequency and flood forecasting analysis of Krishna basin Andhra Pradesh." Disaster Advances 16, no. 11 (2023): 27–39. http://dx.doi.org/10.25303/1611da027039.

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Floods are occurrences of natural hazards, frequent during the year in many rivers across the globe. Every year, several rivers in India are vulnerable to flooding, causing loss of property and life. Krishna is one of the major rivers in India which is vulnerable to flooding in every monsoon. In this study, flood analysis was conducted for the year 2019. Rainfall data from AWS was used to estimate discharge levels in dams during the monsoon of 2019. It was observed that moderate rainfall occurred in the months of August and September, corresponding to low rainfall and that extreme flooding occ

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Zhu, Yanzheng, Yangbo Chen, Yanjun Zhao, Feng Zhou, and Shichao Xu. "Application and Research of Liuxihe Model in the Simulation of Inflow Flood at Zaoshi Reservoir." Sustainability 15, no. 13 (2023): 9857. http://dx.doi.org/10.3390/su15139857.

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Floods occur frequently in China, and watershed floods are caused mainly by intensive rainfall, but the spatial distribution of this rainfall is often very uneven. Thus, a watershed hydrological model that enables a consideration of a heterogeneous spatial distribution of rainfall is needed. In this study, a flood forecasting scheme based on the Liuxihe model is established for the Zaoshi Reservoir. The particle swarm optimization (PSO) algorithm is used to optimize the model parameters for flood simulation, and the model’s performance is assessed by a comparison with measured flood data. The

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Liu, Li, Yue Ping Xu, Su Li Pan, and Zhi Xu Bai. "Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China." Hydrology and Earth System Sciences 23, no. 8 (2019): 3335–52. http://dx.doi.org/10.5194/hess-23-3335-2019.

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Abstract. In recent year, floods becomes a serious issue in the Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide an early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the Yarlung Zangbo River (YZR) basin, China, has not been investigated. This study adopts the variable infiltration capacity (VIC) model to forecast the annual maximum floods and annual first floods in the YZR based on precipitation and

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Younis, J., S. Anquetin, and J. Thielen. "The benefit of high-resolution operational weather forecasts for flash flood warning." Hydrology and Earth System Sciences Discussions 5, no. 1 (2008): 345–77. http://dx.doi.org/10.5194/hessd-5-345-2008.

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Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of human life loss and infrastructures. Over the last two decades, flash floods brought losses of a billion Euros of damage in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available shortrange numerical weather forecasts together with a rainfall-runoff model can be used as early indication for the occurrence of flash floods. One of the challen

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Otieno, O. M., H. S. Abdillahi, E. M. Wambui, and K. S. Kiprono. "FLOOD IMPACT-BASED FORECASTING FOR EARLY WARNING AND EARLY ACTION IN TANA RIVER BASIN, KENYA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 22, 2019): 293–300. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-293-2019.

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<p><strong>Abstract.</strong> Kenya is mostly affected by floods during the March-April-May (MAM) and October-November-December (OND) rainfall. This often occurs along river basins such as the Tana river basin, leading to disruption of people’s livelihoods, loss of lives, infrastructure destruction and interruption of economic activities. This study used openly available data on flood exposure, vulnerability, lack of coping capacity, flood impacts and observed satellite rainfall to analyse and predict forecast-based impacts in Tana river. Earth observation satellites includin

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