To Investigate Of Change in Waves Height under the influence of climate change using Artificial neural network and wavelet
Subject Areas : Irrigation and Drainage
گلرخ
منصوری واجاری
1
(گروه عمران- آب، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، ایران.)
ابوالفضل
شمسایی
2
(گروه مهندسی عمران- آب، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، ایران.)
بهرام
تقیان
3
(گروه عمران- آب، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، ایران.)
Keywords: Neural network, Ecological Scripts, Variation in Waves Height, Wavelet, DWNN,
Abstract :
Prediction of the waves’ specifications that is one of the key factors effective on transformation ofcoasts, production of renewable energies and design of marine structures, has always been importante.Height of the waves is one of the most important and effective parameters of the wave. Differentfactors are effective in variation of the waves’ height. In this research, variation in waves height underchange of universal climate and heating – that as one of the consequences of collection of greenhousegases,isconsideredas one of the most important environmental challenges in the world – has beenstudied. The Effect of weather changes in variation of waves height using the ecological data gainedfrom CGCM2 Model that is one of the types of GCM Models, was investigated under two scripts: A2and B2. In order to predict the waves height using the step by step regression method out of weathervariants stimulated in CGCM2 Ecological Model, those sets of variants that had the most correlationwith variation of waves height, have been selected. Two ANN and DWNN Models were made in orderto study the relationship between the variants of climate and waves height, and DWNN Model that is acombination of ANN Model and Wavelet Theory, showed better and more accurate results. The resultsfor years 2089 to 2100 express an increase from 10 to 46 cm at minimum of daily average of wavesheight and also increase from 10 to 36 cm at maximum of daily average of wave, gained in the regionof Chabahar. With consideration to noticeable increase of average of waves height, this subject mustbe considered in different affairs such as management of coastal regions.
ابراهیمی،ل و بارانی،غ. (1384). معرفی مدل تلفیقی تبدیل موجکی و شبکه های عصبی برای پیش بینی خشکسالی حوزه های آبخیز سدها. مجموعه مقالات دومین کنفرانس سراسری آبخیزداری مدیریت منابع آب و خاک، دانشگاه کرمان. ص.45-56.
کیا، س. م. (1390). شبکه های عصبی مصنوعی در MATLAB. انتشارات کیان رایانه سبز.
Adamowski, J. (2008). Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. Journal of Hydrology, 353,pp. 247–266.
Adamowski, J. (2008). River flow forecasting using wavelet and cross-wavelet transform models. Hydrological Processes, 22, pp. 4877–4891.
Cannas, B., Fanni, A., See.and L., Sias, G. (2006). Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Physics and Chemistry of the Earth. 31 (18), pp.1164–1171.
Chini,N., Stansby.P., Leake.J., Wolf.J., Rpberts-Jones.J.and Lowe,J.(2010). The impact of sea level rise and climate change on inshore wave climate: A case study for East Angelia (UK). Coastal Engineering, 57, pp.973-984.
Cutforth, h., Gwoodvin,b., Mcconkey,R.j., smith,D.j and Jefferson,P.G.(1999). Climate change in theplant.sci.plant.sci.79, pp.343-353.
Deo, M.C., Jha, A., Chaphekar, A.S. and Ravikant, K. (2001). Neural network for wave forecasting.Ocean Engineering, 28, pp. 889–898.
Deo, M.C. and Naidu,C.S.(1999). Real time wave forecasting using neural networks. Ocean Engineering 26, pp.191–203.
Etemad-Shahidi, A. and Mahjoobi, J. (2009). Comparison between M5– model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Engineering 36, pp.1175–1181.
Kazeminezhad, M.H., Etemad-Shahidi, A. and Mousavi, S.J. (2005). Application of fuzzy inference system in the prediction wave parameters. Ocean Engineering 32, pp.1709–1725.
Kamranzad,B., Etemad-Shahidi, A. and Kazeminejad,M.H. (2011). Wave height forecasting inDayyer, the Persian Gulf. Ocean Engineering, 38, pp.248–255.
Liang, S.X., Li, M.C. and Sun, Z.C. (2008). Prediction models for tidal level including strong meteorologic effects using a neural network, Ocean Engineering, 35(7), pp. 666-675.
Makarynskyy. O., Makarynska. D., Kuhn, M. and Featherstone, W.E. (2004). Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia, Estuarine, Coastal and Shelf Science. pp.351–360.
Mark, J., Koetse, P.and Rietveld, g. (2009). The impact of climate change and weather on transport: An overview of empirical findings. Transportation Research, Part D 14, pp. 205–221.
Mesa, O.and Poveda, G. (1993). The Hurst Effect: The scale of fluctuation approach. Water Resources Research, vol.29, No.12, 3995-4002.
Partal, T.and Cigizoglu, H.K. (2008). Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. Journal of Hydrology 358 (3–4), pp. 317–331.
Partal, T.and Kisi, O. (2007). Wavelet and neuro fuzzy conjunction model for precipitation forecasting. Journal of Hydrology 342, 199–212.
Lionello,p., Galati, M.B. and Elvini,E. (2012). Extreme storm surge and wind wave climate scenario simulations at the Venetian littoral. Physics and Chemistry of the Earth, Pages pp.86-92.
Tsai, C.P., Lin, C. and Shen, J.N. (2002). Neural network for wave forecasting among multi- stations.Ocean Engineering 29, pp.1683–1695.
Wang, D.and Ding, J. (2003). Wavelet network model and its application to the prediction of hydrology. Nature and Science 1,pp. 67– 71.