Çankırı Acıçay-Tatlıçay Havzalarında arazi kullanım türlerinin Bayes Ağları yöntemiyle tahmin edilmesi

Abstract

Son yıllarda küresel ısınma ve iklim değişikliğinin dereler üzerindeki rejim bozukluğunu ortaya çıkardığı uzmanlar tarafından tespit edilmiştir. Bu rejim bozuklukları dere, ırmak ve nehirlerin hidromorfolojilerinde de değişimlere sebep olarak zaman zaman sel ve taşkınların oluşmasına sebep olmaktadır. Özellikle yarı-kurak havzalarda bölge yapısının ve özelliklerinin bilinmesi muhtemel felaketleri engellemede önemli bir faktördür. Bu çalışmanın amacı, Çankırı ilinde bulunan Tatlıçay ve Acıçay havzalarında belirlenmiş 513 noktadaki ölçümler ile derelerdeki hidromorfolojik yapının belirlenmesi, arazi kullanma türünün (AKT) hangi parametrelere göre değiştiğinin incelenmesi ve arazi yapısının tahmin edilmesidir. Bu amaçla dört farklı Bayes Ağ senaryosu belirlenmiştir. Bu senaryolarda, farklı parametreler belirlendiğinde AKT’nin yüzde kaç olasılıkla tahmin edildiği saptanmıştır. Bu sonuçlara göre en yüksek olasılıkla belirlenen AKT tipi iğne yapraklı orman olup, bu oran %97 olarak bulunmuştur.

Keywords

• Bayes Ağları,Arazi Kullanma Türü,Rosgen Dere Sınıfı,Çankırı

Using Bayesian Network to predict the watershed land use type of Çankırı Acıçay-Tatlıçay

Abstract

In recent years, experts have identified that climate change and global warming affects stream flow regime. These changes cause floods and erosion in creeks, streams, rivers etc. Especially in semi-arid watersheds, the structure of the land usage type is an important factor in preventing possible disasters. The aim of this study is to determine watershed land usage type by using hydro-morphological structure of stream and some physical water quality parameters. To do so, hydro-morphological observations and some physical water quality parameters were collected from 513 different points in Acıçay and Tatlıçay watershed. For this purpose, four different Bayesian network scenarios were considered to see the changes in the type of the land use. In this scenario, the prediction probability of the watershed land usage type was determined with different parameters. In conclusion, coniferous forest was predicted with the highest probability rate of %97.

Keywords

• Çankırı,Bayesian network,Land use type,Rosgen stream classification,Çankırı

References

1. Aalders, I. 2008. Modeling land-use decision behavior with Bayesian belief networks. Ecology and Society, 13(1). Ames, D.P., Neilson, B.T., Stevens, D.K., Lall, U., 2005. Using Bayesian networks to model watershed management decisions: an East Canyon Creek case study. Journal of Hydroinformatics, 7(4): 267-282.
2. Briassoulis, H. 2000. Analysis of land use change: theoretical and modeling approaches. Regional Research Institute, West Virginia University.
3. Brown, L.E., Hannah, D.M., Milner, A.M. 2009. ARISE: a classification tool for Alpine River and Stream Ecosystem. Freshwater Biology, 54-6. London. DOI: 10.1111/j.1365-2427.2008.02161.x
4. Dodkins, I., Rippey, B., Harrington, T.J., Bradley, C., Chathain, B.N., Kelly-Quinn, M., McGarrigle, M., Hodge, S., Trigg, D. 2005. Developing an optimal river typology for biological elements within the Water Framework Directive. Water Research, 39-15. DOI:10.1016/j.watres.2005.06.008
5. Fogg, J., Wells, G. 1998. Stream corridor restoration: Principles, processes and practices. Federal Interagency Stream Restoration Working Group. Washington D.C.
6. Friedman, N., Nachman, I., Peéer D., 1999. Learning Bayesian Network Structure fromMassive Datasets: The “Sparse Candidate” Algorithm. Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI).
7. Frissell, C.A., Liss, W.J., Warren, C.E., Hurley, M.D., 1986. A hierarchical framework for stream habitat classification: Viewing streams in a watershed context. Environmental Management, 10(2):199-214. DOI:10.1007/BF01867358
8. Gooseff, M.N., R.O. Hall, Jr., J.L., Tank. 2007. Relating transient storage to channel complexity in streams of varying land use in Jackson Hole, Wyoming. Water Resour. Res., 43, W01417, doi:10.1029/2005WR004626.

9. Harvey, J.W., Conklin, M.H., Koelsch, R.S., 2003. Predicting changes in hydrologic retention in an evolving semi-arid alluvial stream. Advances in Water Resources, 26: 939–950. https://doi.org/10.1016/S0309-1708(03)00085-X.
10. Huang, J., Zhan, J., Yan, H., Wu, F., Deng, X., 2013. Evaluation of the impacts of land use on Water Quality: A case study in the Chaohu Lake Basin. The Scientific World Journal, Jul 22;2013:329187. doi: 10.1155/2013/329187
11. Knighton, A.D., 1998. Fluvial Forms and Processes: A New Perspective, Arnold, London., 383 p., ill., tabl, pl., 15, 5 x 23, 5 cm. ISBN 0 340 66313 8.
12. Kondolf, G.M., Montgomery, D.R., Piegay, H., Schmitt, L., 2003. Geomorphic Classification of Rivers and Streams. In: Kondolf, G.M., Piegay, H. Tools in fluvial geomorphology. London
13. Lenormand, M., Picornell, M., Cantú-Ros, O. G., Louail, T., Herranz, R., Barthelemy, M., Ramasco, J.J., 2015. Comparing and modelling land use organization in cities. Royal Society open science, 2(12): 150449.
14. Murphy, K. 1998. A brief introduction to graphical models and Bayesian networks. http://www.cs.ubc.ca/ ∼murphyk/Bayes/bnintro.html. Earlier version appears at Murphy K. (2001) The Bayes Net Toolbox for Matlab, Computing Science and Statistics, 33, 2001.Naiman, R.J., Lonzarich, D.G., Beechie, T.J., Ralph, S.C. 1992. General principles of classification and the assessment of conservation potential in rivers. In: Boon, P.J., Calow, P., Pets, G.E. River conservation and management. New York.
15. Overmars, K.P., Verburg, P.H., 2006. Multilevel modelling of land use from field to village level in the Philippines. Agricultural Systems, 89(2): 435-456.
16. Rosgen, D., 1996. Applied River Morphology. Pagosa Springs.
17. Salehin, M., Packman, A.I., Wörman, A., 2003. Comparison of transient storage in vegetated and unvegetated reaches of a small agricultural stream in Sweden: Seasonal variation and anthropogenic manipulation. Adv. Water Resour., 26: 951–964. https://doi.org/10.1016/S0309-1708(03)00084-8
18. Serengil, Y., İnan, M., Yurtseven, İ., Kılıç, Ü., Uygur, B., 2012. Stream corridors as indicators of watershed land use: A case study in Istanbul. Bosque, 33(3): 345-352.
19. Shields, F.D., Langendoen, E.J., Doyle, M.W., 2006. Adapting existing models to examine effects of agricultural conservation programs on stream habitat quality. Journal of the American Water Resources Association, 42: 25–33. doi:10.1111/j.1752-1688.2006.tb03820.x
20. Uriarte, M., Yackulic, C.B., Lim, Y., Nazario, J.A.A., 2011. Influence of land use on water quality in a tropical landscape: a multi-scale analysis. Landscape Ecol ogy., 26: 1151. doi:10.1007/s10980-011-9642-y
21. Varis, O., 1997. Bayesian decision analysis for environmental and resource management. Environmental Modelling & Software, 12(2): 177-185.
22. Waddell, P., Ulfarsson, G.F., 2004. Introduction to urban simulation: Design and development of operational models. In Handbook of transport geography and spatial systems (pp. 203-236). Emerald Group Publishing Limited.
23. Yu, S., Xu, Z., Wu, W., Zuo, D., 2016. Effect of land use types on stream water quality under seasonal variation and topographic characteristics in the Wei River basin, China. Ecological Indicators, Volume 60, January 202–212. https://doi.org/10.1016/j.ecolind.2015.06.029.