Seasonal rainfall forecast for cropping pattern planning using a modified k-nearest neighbor model
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Abstract
Drought phenomena have recently occurred in Thailand causing severe damage to the agricultural sector, especially in the Central, Northern and Northeastern regions. A reliable seasonal rainfall forecast model is needed to provide useful information for effective crop planning and water resource management. The study aims to develop a seasonal rainfall forecast model using a stochastic model k-nearest neighbor technique for an upcoming year. The Mun River basin, located in the Northeastern region, was selected as a case study. Monthly rainfall data from 152 stations, distributed throughout the river basin, were collected over a period of 37 years from 1975 to 2011. Analysis of correlation between large scale atmosphere variables (LAV) around the study basin and seasonal rainfall over the river basin revealed that the surface air temperature (SAT), sea level pressure (SLP), surface zonal wind (U) and surface meridional wind (V) over the China Sea and Pacific Ocean influence seasonal rainfall over the basin. These four LAV variables were used as predictors in a modified k-nearest neighbor model to forecast seasonal rainfall. The likelihood skill score (LLH) was adopted as a technique to evaluate model performance. A test of model performance, using seasonal rainfall for a period of 37 years (1975 to 2011), revealed that the model is able to predict seasonal rainfall with a reliability of around 60%, providing sufficient information for appropriate crop pattern planning in the area.
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