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  List of Accepted Contributions - HS46 Hydroinformatics: computational intelligence and technological developments in water science applications (co-listed in NH & GI)

Please, click Abstract Number to find the corresponding abstract as PDF file; if necessary, download Adobe Acrobat Reader 4.0 first to open the file. Any abstract may be freely reproduced for non-commercial, scientific purposes; however, the moral right of the author(s) to be identified as the author(s) of such abstracts is asserted.

Mavrova-Guirguinova, M; Gualev, K
Wind Wave Dimensions Estimation based on ANNs

Jain, A.; Narain, S.
Rainfall runoff modeling using single GN model (withdrawn)

Pshenichny, C.; Fedukov, R.; Nikolenko, S.
Formal treatment of knowledge in water science by means of event bush

Parasuraman, K; Elshorbagy, A
Model structure uncertainty in characterizing hydrological processes and its quantification using genetic-programming

Dharwadkar, N.; Narain, S.; Jain, A.
Generalized neuron models for hydrological forecasting (withdrawn)

See, L; Heppenstall, A
Applying an instance-based learning approach to the Bird Creek dataset

Szucs, P.; Roland, R.N.
The Application of the ACE Algorithm to Interpret Karst Aquifer Monitoring Data

Parasuraman, K; Elshorbagy, A; Bachu, L; Keshta, N
Evaluating the Performance of Neural Networks in Modeling Soil Moisture

Bayer, P.; Finkel, M.
Combination of Automated Learning and Evolutionary Computation for fast stochastic Optimization of Groundwater Management Problems

Chen, S.T.; Yu, P.S.
Pruning of hidden nodes in support vector networks on flood forecasting

Patil, S.; Bárdossy, A.
Assessment of aptness of purely data driven and data-plus-knowledge driven techniques to derive transfer function for precipitation loss

Peters, J.; Verhoest, N.; De Baets, B.; Samson, R.
The random forests technique: an application in eco-hydrologic distribution modelling

Abrahart, R.J.; Heppenstall, A.J.; See, L.M.
Neural network forecasting of suspended sediment load in the Schuylkill River

Heppenstall, A.J.; Abrahart, R.J.
Neuroevolution modelling applied to the HFC Bird Creek Data Set

Kim, N. W.; Chung, I. M.; Lee, J.; Won, Y. S.
Application of combined SWAT-MODFLOW model to the Musim River Basin in Korea

Kim, N. W.; Lee, J. E.; Lee, B. J.
On the characteristics of flow duration curve according to the operation of multi-purpose dams in Han-river basin

Mediero, L.; Garrote, L.; Llasat, M.C.
Probabilistic calibration of a distributed rainfall-runoff model for the generation of synthetic flood events

Jacquin, A.P.; Shamseldin, A.Y.
Analysis of preditive uncertainiy of environmetal models using a possibilistic approach

Usai, M.; Gessa, S.; Fanni, A.
Feature extraction and data reduction techniques for groundwater monitoring based on neural networks

Latu, K.; Shamseldin, A.Y.
A neural network model for forecasting daily water demand in the Auckland region

Shamseldin, A.Y.
Development of neural network based models for real-time river forecasting in the Bird Creek catchment

Hauer , C; Habersack, H
Description and Evaluation of decisive morphodynamic paramters for sucessful spawning including different case studies of Austrian Rivers

Shrestha, D.L.; Solomatine, D.
Comparing machine learning approaches in estimating model uncertainty of hydrological conceptual models

Dawson, CW; Abrahart, RJ
Backpropagation of error modelling applied to the HFC Bird Creek Data Set

Dawson, CW; Abrahart, RJ
Hydroinformatics Forecasting Contest 1: comparison of results and construction of ensemble forecasts

Abrahart, R.J.; Dawson, C.W.; Han, D.; Coulibaly, P.; Jain, A.; Shamseldin, A.Y.
Hydroinformatics Forecasting Contest I: motivation, catchment description and performance benchmarking

Dawson, CW; Abrahart, RJ
Evaluation of two different methods for using the antecedent precipitation index in neural network river stage forecasting

Cannas, B.; Fanni, A.; Montisci, A.; Sias, G.; Usai, M.
Adapting neural networks for river flow forecasting

Teschl, R.; Randeu, W. L.; Teschl, F.
A feed forward neural network model for river flow forecasting

Bray, M
Support Vector Machines for flood forecasting

Nigro, F.; Pisciotta, A.; Favara, R.; Renda, P.
The runoff map of Sicily

Pisciotta, A.; Nigro, F.; Favara, R.; Renda, P.
Hydrogeological model of the central-eastern sector of Sicily

Märker, M.; Pelacani, S.; Rodolfi, G.
Regionalizzation of soil hydrological characteristics in an intramontane basin in the Nothern Apennines (Tuscany,Italy). (withdrawn)

Abrahart, R.J.; See, L.M.
Let's accentuate the negative: using feedback loops to examine and compare four different neural network river discharge forecasters

Gerald Corzo, G.C; Dimitri Solomatine, D.S
Exhaustive optimization of modular ANN models in flow forecasting

Pierleoni, A.; Bellezza, M.; Casadei, S.; Manciola, P.
Decision support systems nested in a common base of complex datasets: experiences in Central Italy

Rogers, D; Gastaldi, M; Figliolini, A
Application of neural networks to manage leakage from water distribution networks (withdrawn)

Bürger, C.; Finkel, M.; Kolditz, O.
Evolutionary optimization of an in-situ remediation system – Problem encoding and uncertainty

Siek, M.; Solomatine, D.
Tree-like machine learning models in hydrologic forecasting: optimality and expert knowledge

Bravo, J. M.; Uvo, C. B.; Collischonn, W.
River flow forecast based on previous precipitation and streamflow information using artificial neural networks

Abrahart, R.J.; See, L.M.
M5 model tree applied to the HFC Bird Creek Data Set

Parviz, L; Kholghi, M
Streamflow Forecasting Using Temporal And Spatial Disaggregation Method

Zarkami, R.; Goethals, PLM.; De Pauw, N.
Predictive pike (Esox lucius) and tench (Tinca tinca ) population models based on classification trees

Dakhlaoui, H.; Bargaoui, Z.
A Hybrid SCE-UA-KNN optimisation method applied to the Calibration of HBV model

Kourakos, G.; Mantoglou, A.
Management of coastal aquifers using variable density models and neural network approximations

Gaitán, C.; Obregón, N.; Vanegas, M.
Long Term Rainfall Predictive Model by Using ANN and Preciptation Data Sets Gathered at Multiple Raingauges of the Northwestern Coast of South-America

van den Acker, O.; van Dijk, M.; Donchyts, G.; Heynert, K.; Werner, M.
The application of Service-Oriented Architecture (SOA) as a basis for Delft Flood Early Warning System (Delft-FEWS) development

Vargas, A.; Obregon, N.
Application of genetic programming to synthetic unit hydrograph estimation

Markus, M.; Bajcsy, P.; Hejazi, M.; Yang, L.
Prediction of weekly fluctuations of nitrate-N in a small agricultural watershed in Illinois

Abrahart, R.J.
Hydroinformatics: moving from rags to riches

Alfonso, L.; Jonoski, A.; Solomatine, D.
Optimisation of operational responses to non-deliberate contamination events in water distribution networks

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