Classification Methods for Inland Excess Water Modeling
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Abstract
Inland excess water floodings are a common problem in the Carpathian Basin. Nearly every year large areas are covered by water due to lack of natural runoff of superfluous water. To study the development of this phenomenon it is necessary to determine where these inundations are occurring. This research evaluates different methods to classify inland excess water occurrences on a study area covering south-east Hungary and northern Serbia. The region is susceptible to this type of flooding due to its geographical circumstances. Three separate methods are used to determine their applicability to the problem. The methods use the same input data set but differ in approach and complexity. The input data set consists of a mosaic of RapidEye medium resolution satellite images. The results of the classifications show that all three methods can be applied to the problem and provide high quality satellite based inland excess water maps over a large area.
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European Commission
Grant numbers HUSRB/1002/121/088
References
Atkinson, P.M., Tatnall, A.R.L. 1997. Introduction neural networks in remote sensing. International Journal of Remote Sensing 18, 699-709.
Benyhe, B., Kiss, T. 2012. Morphometric analysis of agricultural landforms in lowland ploughed fields using high resolution digital elevation models, Carpathian Journal of Earth and Environmental sciences 7 (3), 71-78.
Bukurov, B. 1975. Fizičko-geografski problemi Bačke. SANU, Odeljenje prirodno-matematičkih nauka, Posebna izdanja 43 (Physical geographic problems of Backa. Serbian Academy of Sciences and Arts, Natural Sciences Section, Special editions 43), Belgrade, 209 p.
Cohen, J. 1960. A coefficient of agreement for nominal scale. Educational and Psychological Measurement 20, 37-46.
Davidović, R., Miljković Lj., Ristanović B. 2003. Reljef Banata. Univerzitet u Novom Sadu, Prirodno-matematički fakultet, Departman za geografiju, turizam i hotelijerstvo, Novi Sad (Relief of the Banat. University of Novi Sad, Faculty of Science, Department of Geography, Tourism and Hotel management), Novi Sad, 188 p
Dawson, C.W., Wilby, R.L. 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography 25 (1), 80-108.
Demuth, H., Beale, M., Hagan, M. 2010. Neural Network Toolbox 6. User’s Guide. The Mathworks, 901 p.
Freeman, J.A., Skapura, D.M. 1991. Neural Networks: Algorithms, Applications and Programming Techniques. Addison- Wesley, Reading (MA), 550 p.
Green, A.A., Berman, M., Switzer, P., Craig, M.D. 1988. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactionson Geoscience and Remote Sensing 26, 65-74.
Hagan, M.T., Demuth, H.B., Beale, M.H. 1996. Neural Network Design. Boston, MA., PWS Publishing, 734 p.
Lillesand, T.M., Kiefer, R.W., Chipman, J.W. 2004. Remote Sensing and Image Interpretation. Wiley, 784 p.
Košćal, M., Menković, Lj., Mijatović, M., Knežević, M. 2005. Geomorfološka karta AP Vojvodine 1:200.000. Geozavod- Gemini Beograd. (Geomorphologic map of the Vojvodina Autonomous province 1:200000. Geoinstitute- Gemini, Belgrade)
Mezősi, G. 1983. Szeged geomorfológiai vázlata (Geomorphological features of Szeged). Alföldi Tanulmányok 7, 59-75. (in Hungarian)
Moore, G.K. 1980. Satellite remote sensing of water turbidity. Hydrological Sciences-Bulletin-des Hydrologiques 25 (4), 407-421.
Pálfai, I. 2004. Belvízek és Aszályok Magyarországon (Inland excess water and drought in Hungary). Hidrológiai tanulmányok, Budapest, 492 p. (In Hungarian)
Pijanowski, B.C., Brown, D.G., Shellito, B.A., Manik, G.A. 2002. Using neural networks and GIS to forecast land use changes: a Land Transformation Model, Computers, Environmentand Urban Systems 26, 553-575.
Pradhan, B., Lee, S., Buchroithner, M.F. 2010. A GIS-based back-propagation neural network model and its crossapplication and validation for landslide susceptibility analyses, Computers, Environment and Urban Systems 34 (3), 216-235.
Rashed, T., Weeks, J.R., Gallada, M.S. 2001. Revealing the anatomy of cities through spectral mixture analysis of multispectral satellite imagery: a case study of the greater Cairo region, Egypt. Geocarto International 16 (4), 5-15.
RapidEye 2012. RapidEye Standard Image Product Specification, Version 3.0, Germany, www.rapideye.de [Last accessed: January 2013].
Smith, M.O., Johnson, P.E., Adams, J.B. 1985. Quantitative determination of mineral types and abundances from reflectance spectra using principal component analysis. Journal of Geophysical Research 90, 792- 804.
Tso, B., Mather, P. 2009. Classification Methods for Remotely Sensed Data, Second Edition. CRC Press, 376 p.
van Leeuwen, B., Mezősi, G., Tobak, Z., Szatmári, J., Barta, K. 2012. Identification of inland excess water floodings using an artificial neural network. Carpathian Journal of Earth and Environmental sciences 7 (4), 173-180.
Yang, Y., Rosenbaum, M.S. 2001. Artificial networks linked to GIS for determining sedimentology in harbours. Journalof Petroleum Science and Engineering 29, 213-220.