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Linkage Between In-Stream Total Phosphorus and Land Cover in Chugoku District, Japan: An Ann Approach Cover

Linkage Between In-Stream Total Phosphorus and Land Cover in Chugoku District, Japan: An Ann Approach

Journal of Hydrology and Hydromechanics
Volume 60 (2012): Issue 1 (March 2012)
By: ,   and    
Open Access
|Mar 2012
References

References

  1. 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.,, 234-247.
    Open DOISearch in Google Scholar
  2. ALLAN J. D., ERICKSON D. L., FAY J., 1997: The influence of catchment land use on stream integrity across multiple spatial scales. Freshwater Biol.,, 149-161.
    Open DOISearch in Google Scholar
  3. 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
  4. 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
  5. BEVEN K., BINLEY A., 1992: The future of distributed models: Model calibration and uncertainty prediction. Hydrol. Processes, 6, 279-298.
    Open DOISearch in Google Scholar
  6. CAMPOLO M., ANDREUSSI P., SOLDATI A., 1999: River flood forecasting with neural network model. Water Resour. Res.,, 4, 1191-1197.
    Open DOISearch in Google Scholar
  7. 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,, 9, 1169, doi:10.1029/2001WR000478.
    Open DOISearch in Google Scholar
  8. 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.,, 991-1010.
    Open DOISearch in Google Scholar
  9. EBERHART R. C., DOBBINS R. W., 1990: Neural Network PC Tools: A Practical Guide, Academic Press, New York.
    Search in Google Scholar
  10. 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.
    Open DOISearch in Google Scholar
  11. GROSS L., THIRIA S., FROUIN R., 1999: Applying artificial neural network methodology to ocean colour remote sensing. Ecol. Modell., 120, 237-246.
    Open DOISearch in Google Scholar
  12. HAAN C. T., 2002: Statistical method in hydrology. Iowa State Press, Ames, Iowa.
    Search in Google Scholar
  13. 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.)
    Open DOISearch in Google Scholar
  14. HEM J. D., 1985: Study and interpretation of the chemical characteristics of natural water. USGS Supply Paper 2254, Washington DC.
    Search in Google Scholar
  15. HILL A. R., 1998: Stream phosphorus exports from watersheds with contrasting land uses in southern Ontario. Water Resour. Bul.,, 627-634.
    Open DOISearch in Google Scholar
  16. HSIEH C., 1993: Some potential applications of artificial neural networks in financial management. J. of Sys. Manage.,, 4, 12-15.
    Search in Google Scholar
  17. JOHNSON L. B., RICHARDS C., HOST G. E., ARTHUR J. W., 1997: Landscape influences on water chemistry in Midwestern stream ecosystems. Freshwater Biol.,, 193-208.
    Open DOISearch in Google Scholar
  18. 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.,, 153-165.
    Open DOISearch in Google Scholar
  19. KHAN M. S., COULIBALY P., 2006: Bayesian neural network for rainfall-runoff modeling. Water Resour. Res., 42, W07409, doi:10.1029/2005WR003971.
    Open DOISearch in Google Scholar
  20. KIESEL J., SCHMALZ B., FOHRER N., 2009: SEPAL - A simple GIS-based tool to estimate sediment pathways in lowland catchment. Adv. Geosci.,, 25-32.
    Open DOISearch in Google Scholar
  21. KINGSTON G. B., LAMBERT M. F., MAIER H. R., 2005: Bayesian training of artificial neural network used for water resources modelling. Water Resour. Res.,, W12409, doi: 10.1029/2005WR004152.
    Open DOISearch in Google Scholar
  22. 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,, 4, 557-562.
    Open DOISearch in Google Scholar
  23. LAM Q. D., SCHMALZ B., FOHRER N., 2009: Ecohydrological modelling of water discharge and nitrate loads in a mesoscale lowland catchment, Germany. Adv. Geosci.,, 49-55.
    Open DOISearch in Google Scholar
  24. 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.
    Open DOISearch in Google Scholar
  25. 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.,, 3, 785-794.
    Open DOISearch in Google Scholar
  26. 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.,, 2-3, 1340-1346, doi: 10.1016/j.jhazmat.2008.06.059.
    Open DOISearch in Google Scholar
  27. 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.
    Open DOISearch in Google Scholar
  28. 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,, 1-3, 211-223.
    Open DOISearch in Google Scholar
  29. 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,, 101-124.
    Open DOISearch in Google Scholar
  30. 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,, 8, 891-909, 2010 10.1016/j.envsoft.2010.02.003.
    Open DOISearch in Google Scholar
  31. 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
  32. 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.,, 6, 2263-2277.
    Open DOISearch in Google Scholar
  33. PANDA S. S., GARG V., CHAUBEY I., 2004: Artificial neural network application in lake water quality estimation using satellite imagery. J. Environ. Informatics,, 2, 65-74.
    Open DOISearch in Google Scholar
  34. RADWAN M., WILLEMS P., BERLAMONT J., 2004: Sensitivity and uncertainty analysis for river quality modelling. J. Hydroinformatics, 6, 83-99.
    Open DOISearch in Google Scholar
  35. 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
  36. RODE M., SUHR U., WRIEDT G., 2007: Multi-objective calibration of a river water quality model - information content of calibration data. Ecol. Model.,, 1-2, 129-142.
    Open DOISearch in Google Scholar
  37. RUMELHART D. E., HINTON G. E., WILLIAMS R. J., 1986: Learning representations by back propagating errors. Nature,, 533-536.
    Open DOISearch in Google Scholar
  38. 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.,, 37-14.
    Open DOISearch in Google Scholar
  39. SCHMALZ B., TAVARES F., FOHRER N., 2007: Assessment of nutrient entry pathways and dominating hydrological processes in lowland catchments. Adv. Geosci., 11, 107-112.
    Open DOISearch in Google Scholar
  40. 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.
    Open DOISearch in Google Scholar
  41. SCHMIDT D. F., MAKALIC, E., 2009: Universal Models for the Exponential Distribution. IEEE Transactions on Information Theory,, 7, 3087-3090, doi:10.1109/TIT.2009.2018331.
    Open DOISearch in Google Scholar
  42. 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.,, 2, 515-532.
    Open DOISearch in Google Scholar
  43. SLIVA L., WILLIAMS D. D., 2001: Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Res.,, 14, 3462-3472.
    Open DOISearch in Google Scholar
  44. 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.
    Open DOISearch in Google Scholar
  45. 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.,, 10, Art. No. W10407.
    Open DOISearch in Google Scholar
  46. 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.,, 6, 1683-1695.
    Open DOISearch in Google Scholar
  47. 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
  48. THIRUMALAIAH K., DEO M. C., 2000: Hydrological forecasting using neural networks. J. Hydrol. Eng., ASCE,, 2, 180-189.
    Open DOISearch in Google Scholar
  49. TURNER R. E., RABALAIS N. N., 2003: Linking landscape and water quality in the Mississippi River Basin for 200 years. Bioscience,, 563-572.
    Open DOISearch in Google Scholar
  50. VAN GRIENSVEN A., MEIXNER T., 2006: Methods to quantify and identify the sources of uncertainty for river basin water quality models. Water Sci. Technol.,, 1, 51-59.
    Open DOISearch in Google Scholar
  51. WHITE H., 1989: Learning in artificial neural networks: a statistical perspective. Neural Computation, 1, 425-464.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.2478/v10098-012-0003-6 | Journal eISSN: 1338-4333 (formerly 0042-790X) | Journal ISSN: 0042-790X
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Language: English, Slovak
Page range: 33 - 44
Published on: Mar 6, 2012
Published by: Slovak Academy of Sciences, Institute of Hydrology
In partnership with: Paradigm Publishing Services
Keywords:
Water Quality Modeling,
Land Use,
Total Phosphorus,
ANN,
Uncertainty Analysis
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2012 Bahman Amiri, K. Sudheer, Nicola Fohrer, published by Slovak Academy of Sciences, Institute of Hydrology
This work is licensed under the Creative Commons License.

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