Rainfall Prediction Model Using Exponential Smoothing Seasonal Planting Index (ESSPI) For Determination of Crop Planting Pattern

K. Hartomo, S. Y. Prasetyo, M. T. Anwar, H. Purnomo
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引用次数: 7

Abstract

The traditional crop farmers rely heavily on rain pattern to decide the time for planting crops. The emerging climate change has caused a shift in the rain pattern and consequently affected the crop yield. Therefore, providing a good rainfall prediction models would enable us to recommend best planting pattern (when to plant) in order to give maximum yield. The recent and widely used rainfall prediction model for determining the cropping patterns using exponential smoothing method recommended by the Food and Agriculture Organization (FAO) suffered from short-term forecasting inconsistencies and inaccuracies for long-term forecasting. In this study, the authors developed a new rainfall prediction model which applied exponential smoothing onto seasonal planting index as the basis for determining planting pattern. The results show that the model gives better accuracy than the original exponential smoothing model.
利用指数平滑季节种植指数(ESSPI)确定作物种植模式的降雨预测模型
传统的种植农民严重依赖降雨模式来决定种植作物的时间。正在出现的气候变化导致了降雨模式的转变,从而影响了作物产量。因此,提供一个良好的降雨预测模型将使我们能够推荐最佳的种植模式(何时种植),以获得最大的产量。最近广泛使用的联合国粮农组织(FAO)推荐的指数平滑法确定种植模式的降雨预测模型存在短期预测不一致和长期预测不准确的问题。本文建立了一种新的降雨预测模型,将指数平滑法应用于季节种植指数,作为确定种植模式的基础。结果表明,该模型比原始的指数平滑模型具有更好的精度。
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