[AHEARN D. S., SHEIBLEY R. S., DAHLGREN R. A., ANDERSON M., JONSON J., TATE K. W., 2005: Land use and land cover influence on water quality in the last freeflowing river draining the western Sierra Nevada, California. J. Hydrol., 313, 234-247.10.1016/j.jhydrol.2005.02.038]Search in Google Scholar
[ALLAN J. D., ERICKSON D. L., FAY J., 1997: The influence of catchment land use on stream integrity across multiple spatial scales. Freshwater Biol., 37, 149-161.10.1046/j.1365-2427.1997.d01-546.x]Search in Google Scholar
[AMIRI B. J., 2007: Modelling the Relationship between Environmental Factors of the Catchments and River Water Quality in Chugoku District of Japan, PhD thesis, Hiroshima University, pp. 102.]Search in Google Scholar
[BARUAH P. J., TUMURA M., OKI K., NISHIMURA H., 2001: Neural network modelling of lake surface chlorophyll and sediment content from Landsat TM imagery, Proc. of the 22nd Asian Conference on Remote Sensing, 5-9, November, Singapore.]Search in Google Scholar
[BEVEN K., BINLEY A., 1992: The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Processes, 6, 279-298.10.1002/hyp.3360060305]Search in Google Scholar
[CAMPOLO M., ANDREUSSI P., SOLDATI A., 1999: River flood forecasting with neural network model. Water Resour. Res., 35, 4, 1191-1197.10.1029/1998WR900086]Search in Google Scholar
[CHRISTIAENS K., FEYEN J., 2002: Use of sensitivity and uncertainty measures in distributed hydrological modelling with an application to the MIKE SHE model. Water Resour. Res, 38, 9, 1169, doi:10.1029/2001WR000478.10.1029/2001WR000478]Search in Google Scholar
[DEAN S., FREER J., BEVEN K., WADE A. J., BUTTERFIELD D., 2009: Uncertainty assessment of a process based integrated catchment model of phosphorous. Stochastic Environ. Res. and Risk Assess., 23, 991-1010.10.1007/s00477-008-0273-z]Search in Google Scholar
[EBERHART R. C., DOBBINS R. W., 1990: Neural Network PC Tools: A Practical Guide, Academic Press, New York.]Search in Google Scholar
[GATTS C. E. N., OVALLE A. R. C., SILVA C. F., 2005: Neural pattern recognition and multivariate data: water topology of the Paraiba do Sul River, Brazil. Environ. Modell. and Software, 20, 883-889. doi: 101016/j.envsoft.2004.03.018.10.1016/j.envsoft.2004.03.018]Search in Google Scholar
[GROSS L., THIRIA S., FROUIN R., 1999: Applying artificial neural network methodology to ocean colour remote sensing. Ecol. Modell., 120, 237-246.10.1016/S0304-3800(99)00105-2]Search in Google Scholar
[HAAN C. T., 2002: Statistical method in hydrology. Iowa State Press, Ames, Iowa.]Search in Google Scholar
[HAN D., KWONG T., LI S.: Uncertainties in real-time flood forecasting with neural networks. Hydrol. Processes, Published online in Wiley InterScience, doi: 10.1002/hyp.6184. (In press.)10.1002/hyp.6184]Search in Google Scholar
[HEM J. D., 1985: Study and interpretation of the chemical characteristics of natural water. USGS Supply Paper 2254, Washington DC.]Search in Google Scholar
[HILL A. R., 1998: Stream phosphorus exports from watersheds with contrasting land uses in southern Ontario. Water Resour. Bul., 17, 627-634.10.1111/j.1752-1688.1981.tb01269.x]Search in Google Scholar
[HSIEH C., 1993: Some potential applications of artificial neural networks in financial management. J. of Sys. Manage., 44, 4, 12-15.]Search in Google Scholar
[JOHNSON L. B., RICHARDS C., HOST G. E., ARTHUR J. W., 1997: Landscape influences on water chemistry in Midwestern stream ecosystems. Freshwater Biol., 37, 193-208.10.1046/j.1365-2427.1997.d01-539.x]Search in Google Scholar
[KEINER L. E., YAN X. H., 1998: A neural network model for estimating sea surface chlorophyll and sediments from Thematic mapper imagery. Remote Sensing of Environ., 66, 153-165.10.1016/S0034-4257(98)00054-6]Search in Google Scholar
[KHAN M. S., COULIBALY P., 2006: Bayesian neural network for rainfall-runoff modeling. Water Resour. Res., 42, W07409, doi:10.1029/2005WR003971.10.1029/2005WR003971]Search in Google Scholar
[KIESEL J., SCHMALZ B., FOHRER N., 2009: SEPAL - A simple GIS-based tool to estimate sediment pathways in lowland catchment. Adv. Geosci., 21, 25-32.10.5194/adgeo-21-25-2009]Search in Google Scholar
[KINGSTON G. B., LAMBERT M. F., MAIER H. R., 2005: Bayesian training of artificial neural network used for water resources modelling. Water Resour. Res., 41, W12409, doi: 10.1029/2005WR004152.10.1029/2005WR004152]Search in Google Scholar
[KRUEGER T., FREER J., QUINTON J. N., MACLEOD C. J. A., 2007: Processes affecting transfer of sediment and colloids, with associated phosphorus, from intensively farmed grasslands: a critical note on modelling of phosphorus transfers. Hydrol. Process, 21, 4, 557-562.10.1002/hyp.6596]Search in Google Scholar
[LAM Q. D., SCHMALZ B., FOHRER N., 2009: Ecohydrological modelling of water discharge and nitrate loads in a mesoscale lowland catchment, Germany. Adv. Geosci., 21, 49-55.10.5194/adgeo-21-49-2009]Search in Google Scholar
[LAM Q. D., SCHMALZ B., FOHRER N., 2010: Modelling point and diffuse source pollution of nitrate in a rural lowland catchment using the SWAT model. Agric. Water Manage., 97, 317-325.10.1016/j.agwat.2009.10.004]Search in Google Scholar
[LENHART T., VAN ROMPAEY A., STEEGEN A., FOHRER N., FREDE H.-G., GOVERS G., 2005: Considering spatial distribution and deposition of sediment in lumped and semidistributed models. Hydr. Proc., 19, 3, 785-794.10.1002/hyp.5616]Search in Google Scholar
[LI S., LIU W., GU S., CHENG X., XU Z., ZHANG Q., 2009: Spatio-Temporal Dynamics of Nutrients in the Upper Han River Basin, China. J. of Hazardous Mater., 162, 2-3, 1340-1346, doi: 10.1016/j.jhazmat.2008.06.059.10.1016/j.jhazmat.2008.06.05918675508]Search in Google Scholar
[LI S., XU Z., CHENG X., ZHANG Q., 2008: Dissolved trace elements and heavy metals in the Danjiangkou Reservoir, China. Environ. Geol. doi:10.1007/s00254-007-1047-5.10.1007/s00254-007-1047-5]Search in Google Scholar
[LIU S. M., BRAZIER R., HEATHWAITE L., 2005: An investigation into the inputs controlling predictions froth a diffuse phosphorus loss model for the UK; the Phosphorus Indicators Tool (PIT). Sci. Total Environ, 344, 1-3, 211-223.10.1016/j.scitotenv.2005.02.017]Search in Google Scholar
[MAIER H. R., DANDY, G. C., 2000: Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environ. Modell. and Software, 15, 101-124.10.1016/S1364-8152(99)00007-9]Search in Google Scholar
[MAIER H. R., JAIN A., DANDY G. C., SUDHEER K. P., 2010: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions, Environ. Modell. and Software, 25, 8, 891-909, 2010 10.1016/j.envsoft.2010.02.003.10.1016/j.envsoft.2010.02.003]Search in Google Scholar
[NEITSCH S. L., ARNOLD J. G., KINIRY J. R., WILLIAMS J. R., KING K. W., 2002: Soil Water Assessment Tool theoretical documentation. Version 2000. Texas Water Resource Institute, college station, Texas. TWRI Report, TR-191.]Search in Google Scholar
[PAGE T., HAYGARTH P. M., BEVEN K. J., JOYNES A., BUTLER T., KEELER C., FREER J., OWENS P. N., WOOD G. A., 2005: Spatial variability of soil phosphorus in relation to the topographic index and critical source areas: sampling for assessing risk to water quality. J. Environ Q., 34, 6, 2263-2277.10.2134/jeq2004.039816275728]Search in Google Scholar
[PANDA S. S., GARG V., CHAUBEY I., 2004: Artificial neural network application in lake water quality estimation using satellite imagery. J. Environ. Informatics, 4, 2, 65-74.10.3808/jei.200400038]Search in Google Scholar
[RADWAN M., WILLEMS P., BERLAMONT J., 2004: Sensitivity and uncertainty analysis for river quality modelling. J. Hydroinformatics, 6, 83-99.10.2166/hydro.2004.0008]Search in Google Scholar
[RECH G., 2002: Forecasting with Artificial Neural Network Models, SSE/EFI Working Paper Series in Economics and Finance, No. 491, 38 pp.]Search in Google Scholar
[RODE M., SUHR U., WRIEDT G., 2007: Multi-objective calibration of a river water quality model - information content of calibration data. Ecol. Model., 204, 1-2, 129-142.10.1016/j.ecolmodel.2006.12.037]Search in Google Scholar
[RUMELHART D. E., HINTON G. E., WILLIAMS R. J., 1986: Learning representations by back propagating errors. Nature, 323, 533-536.10.1038/323533a0]Search in Google Scholar
[SCHMALZ B., BIEGER K., FOHRER N., 2008: A method to assess instream water quality - the role of nitrogen entries in a North German lowland catchment. Adv. Geosci., 18, 37-14.10.5194/adgeo-18-37-2008]Search in Google Scholar
[SCHMALZ B., TAVARES F., FOHRER N., 2007: Assessment of nutrient entry pathways and dominating hydrological processes in lowland catchments. Adv. Geosci., 11, 107-112.10.5194/adgeo-11-107-2007]Search in Google Scholar
[SCHILLER H., DOERFFER, R., 1999: Neural network emulation of an inverse model-operational derivation of case II water properties from MERIS data. Int'l. J. of Remote Sensing, 20, 9, 1735-1746.10.1080/014311699212443]Search in Google Scholar
[SCHMIDT D. F., MAKALIC, E., 2009: Universal Models for the Exponential Distribution. IEEE Transactions on Information Theory, 55, 7, 3087-3090, doi:10.1109/TIT.2009.2018331.10.1109/TIT.2009.2018331]Search in Google Scholar
[SINGH A. P., GHOSH S. K., SHARMA P., 2007: Water quality management of a stretch of river Yamuna: an interactive fuzzy multi-objective approach. Water. Resour. Manage., 21, 2, 515-532.10.1007/s11269-006-9028-0]Search in Google Scholar
[SLIVA L., WILLIAMS D. D., 2001: Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Res., 35, 14, 3462-3472.10.1016/S0043-1354(01)00062-8]Search in Google Scholar
[SMART R. P., SOULSBY C., NEAL C., WADE A., CRESSER M. S., BILLETT M. F., LANGAN S. J., EDWARDS A. C., JARVIE H. P., OWEN R., 1998: Factors regulating the spatial and temporal distribution of solute concentrations in a major river system in NE Scotland. Sci. Total Environ., 221, 93-110.10.1016/S0048-9697(98)00196-X]Search in Google Scholar
[SRIVASTAV R. K., SUDHEER K. P., CHAUBEY, I., 2007: A Simplified Approach to Quantify Predictive and Parametric Uncertainty in Artificial Neural Network Hydrologic Models. Water Resour. Res., 43, 10, Art. No. W10407.10.1029/2006WR005352]Search in Google Scholar
[SUDHEER K. P., CHAUBEY I., GARG, V., 2006: Lake Water Quality Assessment from Landsat TM Data using Neural Network: An approach to optimal band combination selection. J. of Am. Water Resour. Soc., 42, 6, 1683-1695.10.1111/j.1752-1688.2006.tb06029.x]Search in Google Scholar
[TANAKA A., KISHINO M., OISHI T., DOERFFER R., SCILLER H., 2000: Application of the neural network method to case II water. In: SPIE Proceedings, Remote Sensing of Ocean and Sea Ice 2000, Barcelona, 4172, 144-152.]Search in Google Scholar
[THIRUMALAIAH K., DEO M. C., 2000: Hydrological forecasting using neural networks. J. Hydrol. Eng., ASCE, 5, 2, 180-189.10.1061/(ASCE)1084-0699(2000)5:2(180)]Search in Google Scholar
[TURNER R. E., RABALAIS N. N., 2003: Linking landscape and water quality in the Mississippi River Basin for 200 years. Bioscience, 53, 563-572.10.1641/0006-3568(2003)053[0563:LLAWQI]2.0.CO;2]Search in Google Scholar
[VAN GRIENSVEN A., MEIXNER T., 2006: Methods to quantify and identify the sources of uncertainty for river basin water quality models. Water Sci. Technol., 53, 1, 51-59.10.2166/wst.2006.00716532735]Search in Google Scholar
[WHITE H., 1989: Learning in artificial neural networks: a statistical perspective. Neural Computation, 1, 425-464.10.1162/neco.1989.1.4.425]Search in Google Scholar