Self-organizing radial basis neural network for predicting typhoon-induced losses to rice

Alavi N, Nozari V, Mazloumzadeh SM, Nezamabadi-pour H (2010) Irrigation water quality evaluation using adaptive network-based fuzzy inference system. Paddy Water Environ 8(3):259–266 Chang FJ, Chen YC (2003) Estuary water-stage forecasting by using radial basis function neural network. J Hydrol 270(1–2):158–166 Chang FJ, Chang LC, Wang YS (2007) Enforced self-organizing map neural networks for river flood forecasting. Hydrol Process 21(6):741–749 Chang LC, Chang FJ, Wang YP (2009) Auto-configuring radial basis function networks for chaotic time series and flood forecasting. Hydrol Process 23(17):2450–2459 Chang FJ, Chang LC, Kao HS, Wu GR (2010a) Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network. J Hydrol 384(1–2):118–129 Chang LC, Shen HY, Wang YF, Huang JY, Lin YT (2010b) Clustering-based hybrid inundation model for forecasting flood inundation depths. J Hydrol 385(1–4):257–268 Chen CC, Chang CC (2005) The impact of weather on crop yield distribution in Taiwan: some new evidence from panel data models and implications for crop insurance. Agric Econ 33:503–511 Chen YH, Chang FJ (2009) Evolutionary artificial neural networks for hydrological systems forecasting. J Hydrol 367:125–137 Chiang YM, Chang FJ (2009) Integrating hydrometeorological information for rainfall-runoff modelling by artificial neural networks. Hydrol Process 23(11):1650–1659 Chiang YM, Chang LC, Chang FJ (2004) Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff modeling. J Hydrol 290(3–4):297–311 Chiang YM, Chang FJ, Jou BJD, Lin PF (2007a) Dynamic ANN for precipitation estimation and forecasting from radar observations. J Hydrol 334(1–2):250–261 Chiang YM, Hsu KL, Chang FJ, Hong Y, Sorooshian S (2007b) Merging multiple precipitation sources for flash flood forecasting. J Hydrol 340(3–4):183–196 Chiang YM, Chang LC, Tsai MJ, Wang YF, Chang FJ (2011) Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks. Hydrol Earth Syst Sci 15(1):185–196 Gauthier TD (2001) Detecting trends using Spearman’s rank correlation coefficient. Environ Forensics 2(4):359–362 Gholizadeh S, Salajegheh E (2010) Optimal design of structures for earthquake loading by self organizing radial basis function neural networks. Adv Struct Eng 13(2):339–356 Girosi F, Poggio T (1990) Networks and the best approximation property. Biol Cybern 63(3):169–176 Ham FM, Kostanic I (2001) Principles of neurocomputing for science and engineering. McGraw Hill, New York Jahanbani H, El-Shafie AH (2011) Application of artificial neural network in estimating monthly time series reference evapotranspiration with minimum and maximum temperatures. Paddy Water Environ 9(2):207–220 Khalil B, Ouarda TBMJ, St-Hilaire A (2011) Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis. J Hydrol 405(3–4):277–287 Khatibi R, Ghorbani MA, Kashani MH, Kisi O (2011) Comparison of three artificial intelligence techniques for discharge routing. J Hydrol 403(3–4):201–212 Kuo SF, Chen FW, Liao PY, Liu CW (2011) A comparative study on the estimation of evapotranspiration using backpropagation neural network: Penman–Monteith method versus pan evaporation method. Paddy Water Environ 9(4):413–424 Kwedlo W (2011) A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn Lett 32(12):1613–1621 Lin GF, Wu MC (2011) An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model. J Hydrol 405(3–4):439–450 Mainardi S (2011) Cropland use, yields, and droughts: spatial data modeling for Burkina Faso and Niger. Agric Econ 42(1):17–33 Márquez AL, Baños R, Gil C, Montoya MG, Montoya FG (2011) Multi-objective crop planning using pareto-based evolutionary algorithms. Agric Econ 42(6):649–656 Moore PH, Osgood RV (1987) Evaluation of a model for predicting sucrose yields following hurricane damage to sugarcane in Hawaii. Agric For Meteorol 40(2):191–197 Orhan U, Hekim M, Ozer M (2011) EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst Appl 38(10):13475–13481 Tabari H, Sabziparvar AA, Ahmadi M (2011) Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorol Atmos Phys 110(3–4):135–142 Traore S, Wang YM, Kan CE, Kerh T, Leu JM (2010) A mixture neural methodology for computing rice consumptive water requirements in Fada N’Gourma Region, Eastern Burkina Faso. Paddy Water Environ 8(2):165–173 Vesely A (2011) Economic classification and regression problems and neural networks. Agric Econ Zemedelska Ekonomika 57(3):150–157 Wang J, Mendelsohn R, Dinar A, Rozelle S, Zhang L (2009) The impact of climate change on China’s agriculture. Agric Econ 40(3):323–337 Xiang S, Griffiths JF (1988) A survey of agrometeorological disasters in South China. Agric For Meteorol 43(3–4):261–276 Yilmaz I, Erik NY, Kaynar O (2010) Different types of learning algorithms of artificial neural network (ANN) models for prediction of gross calorific value (GCV) of coals. Sci Res Essays 5(16):2242–2249