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Rainfall Prediction Model Using Exponential Smoothing Seasonal Planting Index (ESSPI) For Determination of Crop Planting Pattern
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Author(s): Kristoko Dwi Hartomo (Satya Wacana Christian University, Indonesia), Sri Yulianto Joko Prasetyo (Satya Wacana Christian University, Indonesia), Muchamad Taufiq Anwar (Satya Wacana Christian University, Indonesia)and Hindriyanto Dwi Purnomo (Chung Yuan Christian University, Taiwan & Satya Wacana Christian University, Indonesia)
Copyright: 2019
Pages: 22
Source title:
Computational Intelligence in the Internet of Things
Source Author(s)/Editor(s): Hindriyanto Dwi Purnomo (Satya Wacana Christian University, Indonesia)
DOI: 10.4018/978-1-5225-7955-7.ch010
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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.
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