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

Author:

Hartomo Kristoko Dwi1,Prasetyo Sri Yulianto Joko1,Anwar Muchamad Taufiq1,Purnomo Hindriyanto Dwi2

Affiliation:

1. Satya Wacana Christian University, Indonesia

2. Chung Yuan Christian University, Taiwan & Satya Wacana Christian University, Indonesia

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.

Publisher

IGI Global

Reference31 articles.

1. Ai, T. J. (2004). Optimalisasi Prediksi Pemulusan Eksponensial Satu Variabel Dengan Menggunakan Algoritma Non Linear Programming. Jurnal Teknologi Industri, 3(3).

2. Boer, R., Buono, A., Sumaryanto, E., Surmaini, A., Rakhman, W., Estiningtyas, K., & Kartikasari, F. (2009). Technical Report on Vulnerability and Adaptation Assessment to Climate Change for Indonesia’s Second National Communication. Ministry of Environment and United Nations Development Programme.

3. Burkom, S.H., Muphy, S.P., & dan Shmuelli, G. (2006). Automated Time Series Forecasting for Rainfall. The Johns Hopkins University Applied Physics Laboratory, Department of Decision and Information Technologies, Robert H. Smith School, University of Maryland College Park.

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