Determining Salt Production Season Based on Rainfall Forecasting Using Weighted Fuzzy Time Series
|Published in:||Issue 2, (Vol. 14) / 2020|
|Author(s):||MUHANDHIS Isnaini, SUSANTO Heri, ASFARI Ully|
|Abstract.||Most of the salt production in Indonesia uses the solar evaporation method, so salt production is very dependent on the weather. As one of the salt production centers in Indonesia, rainfall forecasting on the Sumenep Regency is very important to prepare salt production as soon as possible and to prevent crop failure. This study aims to predict rainfall to determine the beginning and end of the dry season using the weighted fuzzy time series method. The weighted fuzzy time series assign different weights in trends of the fuzzy relationships process to improve forecasting accuracy. The results showed that the weighted fuzzy time series method is able to predict rainfall with good accuracy with a MAPE value of 5.64% and RMSE 33.64. The beginning and end of dry season testing results have a small error value of about 1.5 periods. Therefore, the weighted fuzzy time series method has good accuracy for determining the dry season on the Sumenep Regency|
|Keywords:||Decision Support System, Seasonal Time Series, Prediction, Time Series Analysis, Weather Forecasting|
J. I. Kuwajima, F. M. Fan, D. Schwanenberg, A. A. Dos Reis, A. Niemann, and F. F. Mauad, ‘Climate change, water-related disasters, flood control and rainfall forecasting: a case study of the São Francisco River, Brazil’, Geol. Soc. Lond. Spec. Publ., vol. 488, no. 1, pp. 259–276, 2019.
2. P. Roudier, A. Alhassane, C. Baron, S. Louvet, and B. Sultan, ‘Assessing the benefits of weather and seasonal forecasts to millet growers in Niger’, Agric. For. Meteorol., vol. 223, pp. 168–180, 2016.
3. A. Kocharekar, B. V. Nemade, C. G. Patil, D. D. Sapkale, and S. G. Salunke, ‘Weather Prediction for Tourism Application using ARIMA’, Weather, vol. 6, no. 11, 2019.
4. Q. Song and B. S. Chissom, ‘Fuzzy time series and its models’, Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.
5. H. Guney, M. A. Bakir, and C. H. Aladag, ‘A novel stochastic seasonal fuzzy time series forecasting model’, Int. J. Fuzzy Syst., vol. 20, no. 3, pp. 729–740, 2018.
6. S.-H. Cheng, S.-M. Chen, and W.-S. Jian, ‘Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures’, Inf. Sci., vol. 327, pp. 272–287, 2016.
7. C.-H. Cheng, T.-L. Chen, H. J. Teoh, and C.-H. Chiang, ‘Fuzzy time-series based on adaptive expectation model for TAIEX forecasting’, Expert Syst. Appl., vol. 34, no. 2, pp. 1126–1132, 2008.
8. T. Y. Christyawan, M. S. Haris, R. Rody, and W. F. Mahmudy, ‘Optimization of Fuzzy Time Series Interval Length Using Modified Genetic Algorithm for Forecasting’, in 2018 International Conference on Sustainable Information Engineering and Technology (SIET), 2018, pp. 60–65.
9. E. Munadi et al., ‘Info Komoditi Garam’, Salim Z Munadi E Eds, 2016. Available at : http://bppp.kemendag.go.id/media_content/2017/08/Isi_BRIK_Garam.pdf
10. I. Muhandhis, H. Susanto, and U. Asfari, ‘Development of System Dynamics Model to Increase Salt Fulfillment Ratio’, Procedia Comput. Sci., vol. 161, pp. 867–875, 2019.
11. I. Muhandhis, H. Susanto, and U. Asfari, ‘Dynamic simulation model of salt supply chain to increase farmers income’, in IOP Conference Series: Materials Science and Engineering, 2020, vol. 732, p. 012075.
12. Q. Song and B. S. Chissom, ‘Forecasting Enrollments with Fuzzy Time Series.’, 1991.
13. C. H. Aladag, E. Egrioglu, U. Yolcu, and V. R. Uslu, ‘A high order seasonal fuzzy time series model and application to international tourism demand of Turkey’, J. Intell. Fuzzy Syst., vol. 26, no. 1, pp. 295–302, 2014.
14. A. Bhat, K. Sharma, and U. Banday, ‘Impact of Climatic Variability on Salt Production in Sambhar Lake, a Ramsar Wetland of Rajasthan, India’, Middle-East J. Sci. Res., vol. 23, no. 9, pp. 2060–2065, 2015.
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