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Representing the spatial variability of rainfall for input to the G2G distributed flood forecasting model: operational experience from the Flood Forecasting CentrePRICE, David; PILLING, Charlie; ROBBINS, Gavin et al.IAHS-AISH publication. 2012, Num 351, pp 532-537, issn 0144-7815, isbn 978-1-907161-26-1, 1Vol, 6 p.Conference Paper

From HUP to MCP: Analogies and extended performancesTODINI, Ezio.Journal of hydrology (Amsterdam). 2013, Vol 477, pp 33-42, issn 0022-1694, 10 p.Article

Model identification for hydrological forecasting under uncertaintyWAGENER, Thorsten; GUPTA, Hoshin V.Stochastic environmental research and risk assessment (Print). 2005, Vol 19, Num 6, pp 378-387, issn 1436-3240, 10 p.Article

The strategy of building a flood forecast model by neuro-fuzzy networkCHEN, Shen-Hsien; LIN, Yong-Huang; CHANG, Li-Chiu et al.Hydrological processes. 2006, Vol 20, Num 7, pp 1525-1540, issn 0885-6087, 16 p.Article

Auto-configuring radial basis function networks for chaotic time series and flood forecastingCHANG, Li-Chiu; CHANG, Fi-John; WANG, Yuan-Peng et al.Hydrological processes (Print). 2009, Vol 23, Num 17, pp 2450-2459, issn 0885-6087, 10 p.Article

Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling systemJUAN WU; GUIHUA LU; ZHIYONG WU et al.Natural hazards (Dordrecht). 2014, Vol 74, Num 2, pp 325-340, issn 0921-030X, 16 p.Article

Using combined raingauge and high-resolution radar data in an operational flood forecast system in FlandersDE JONGH, Inge; QUINTELIER, Els; CAUWENBERGHS, Kris et al.IAHS-AISH publication. 2012, Num 351, pp 472-477, issn 0144-7815, isbn 978-1-907161-26-1, 1Vol, 6 p.Conference Paper

Methods and examples for remote sensing data assimilation in land surface process modelingBACH, Heike; MAUSER, Wolfram.IEEE transactions on geoscience and remote sensing. 2003, Vol 41, Num 7, pp 1629-1637, issn 0196-2892, 9 p., 1Conference Paper

Real-time forecast of the 2005 and 2007 summer severe floods in the Huaihe River Basin of ChinaLIN, Charles A; LEI WEN; GUIHUA LU et al.Journal of hydrology (Amsterdam). 2010, Vol 381, Num 1-2, pp 33-41, issn 0022-1694, 9 p.Article

Modular learning models in forecasting natural phenomenaSOLOMATINE, D. P; SIEK, M. B.Neural networks. 2006, Vol 19, Num 2, pp 215-224, issn 0893-6080, 10 p.Article

Designing a flood forecasting and inundation-mapping system integrated with spatial data infrastructures for TurkeyAKINCI, Halil; ERDOGAN, Sevsin.Natural hazards (Dordrecht). 2014, Vol 71, Num 1, pp 895-911, issn 0921-030X, 17 p.Article

Using typhoon characteristics to improve the long lead-time flood forecasting of a small watershedLIN, Gwo-Fong; HUANG, Pei-Yu; CHEN, Guo-Rong et al.Journal of hydrology (Amsterdam). 2010, Vol 380, Num 3-4, pp 450-459, issn 0022-1694, 10 p.Article

Probabilistic flood forecasting and decision-making: an innovative risk-based approachDALE, Murray; WICKS, Jon; MYLNE, Ken et al.Natural hazards (Dordrecht). 2014, Vol 70, Num 1, pp 159-172, issn 0921-030X, 14 p.Article

Parallelisation of a distributed hydrologic modelZHENGTAO CUI; VIEUX, Baxter E; NEEMAN, Henry et al.International journal of computer applications in technology. 2005, Vol 22, Num 1, pp 42-52, issn 0952-8091, 11 p.Article

Assessment of flood forecasting lead time based on generalized likelihood uncertainty estimation approachHEIDARI, A; SAGHAFIAN, B; MAKNOON, R et al.Stochastic environmental research and risk assessment (Print). 2006, Vol 20, Num 5, pp 363-380, issn 1436-3240, 18 p.Article

Reinforced recurrent neural networks for multi-step-ahead flood forecastsCHEN, Pin-An; CHANG, Li-Chiu; CHANG, Fi-John et al.Journal of hydrology (Amsterdam). 2013, Vol 497, pp 71-79, issn 0022-1694, 9 p.Article

Evaluation of real-time flash flood forecasts for Haiti during the passage of Hurricane Tomas, November 4-6, 2010SHAMIR, E; GEORGAKAKOS, K. P; SPENCER, C et al.Natural hazards (Dordrecht). 2013, Vol 67, Num 2, pp 459-482, issn 0921-030X, 24 p.Article

Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs)MUKESH KUMAR TIWARI; CHATTERJEE, Chandranath.Journal of hydrology (Amsterdam). 2010, Vol 382, Num 1-4, pp 20-33, issn 0022-1694, 14 p.Article

Pruning of support vector networks on flood forecastingCHEN, Shien-Tsung; YU, Pao-Shan.Journal of hydrology (Amsterdam). 2007, Vol 347, Num 1-2, pp 67-78, issn 0022-1694, 12 p.Article

Application of wavelet-based multi-model Kalman filters to real-time flood forecastingCHOU, Chien-Ming; WANG, Ru-Yih.Hydrological processes. 2004, Vol 18, Num 5, pp 987-1008, issn 0885-6087, 22 p.Article

Satellite precipitation in southeastern South America: how do sampling errors impact high flow simulations?DEMARIA, Eleonora M. C; NIJSSEN, Bart; VALDES, Juan B et al.International journal of river basin management. 2014, Vol 12, Num 1, pp 1-13, issn 1571-5124, 13 p.Article

Hierarchical neurofuzzy model for real-time flood forecastingRATH, Sagarika; PURNA CHANDRA NAYAK; CHATTERJEE, Chandranath et al.International journal of river basin management. 2013, Vol 11, Num 3, pp 253-268, issn 1571-5124, 16 p.Article

Adaptive space-time sampling with wireless sensor nodes for flood forecastingNEAL, Jeffrey C; ATKINSON, Peter M; HUTTON, Craig W et al.Journal of hydrology (Amsterdam). 2012, Vol 414-15, pp 136-147, issn 0022-1694, 12 p.Article

Potential of radar data for flood forecasting and warning in lowland catchments in IrelandDESTA, M. B; O'LOUGHLIN, F; BRUEN, M et al.IAHS-AISH publication. 2012, Num 351, pp 484-489, issn 0144-7815, isbn 978-1-907161-26-1, 1Vol, 6 p.Conference Paper

Fuzzy Rule Based Models Modification by New Data: Application to Flood Flow ForecastingAKBARI, M; AFSHAR, A; REZAEI SADRABADI, M et al.Water resources management. 2009, Vol 23, Num 12, pp 2491-2504, issn 0920-4741, 14 p.Article

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