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The models are adaptable to any location if key input data is available, particularly average monthly rainfall and storm frequency duration data.
The aim of this paper was to derive agroclimatic regions of Ireland using hydro-thermal climate (as expressed by climographs using mean monthly rainfall and temperature) in conjunction with a statistical clustering technique (k-means clustering) to relate crop yield, estimated using mathematical simulation models for grass, barley, maize, potato and soybean to hydro-thermal climate data.
In this paper, a hybrid wavelet neural network (HWNN) model is developed for effectively forecasting monthly rainfall from antecedent monthly rainfall and climate indices by incorporating the multiresolution analysis (MRA), mutual information (MI) and particle swarm optimization (PSO) into artificial neural network (ANN) models.
Fig. 2 Mean monthly rainfall and mean monthly temperature records of study area.
Upper graphs: Monthly rainfall and surface temperature oC (at 1000 hPa).
Figure 3 Monthly rainfall and NDVI values for the Ilmotiok group ranch over a five-year period (2007 to 2012).
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Fig. 2 Mean monthly rainfall (mm) and maximum and minimum air temperatures (°C) of the Girar Jarso District, North Shoa Zone.
Fig. 2 Total monthly rainfall (mm) and snowfall (cm) and mean monthly river discharge rate of the Kita River (m3/s) in 2013.
Monthly rainfall (mm) and temperature (maximum and minimum) data in the study area, provided by Agro-metrological field unit (AMFU), Lembucherra (Tripura), are given in Fig. 1.
Average monthly annual rainfall and temperature patterns during the study period are given in Figure 1. Figure 1 Total monthly rainfall (mm) and mean monthly maximum and minimum temperature (°C) at the dry deciduous forest of Bhadra wildlife sanctuary during the study period.
The considered variables were total annual, seasonal and monthly rainfall; annual, seasonal and monthly maximum rainfall; and the number of rainy days per year, season and month.
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