RAINFALL PREDICTION USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GEOGRAPHIC INFORMATION SYSTEM APPROACH
Abstract
Rainfall is one indicator to determine the estimated adequacy of groundwater on agricultural land. The groundwater availability produced by rain can determine cropping patterns in an area. The availability of rainfall data depends on the accuracy of information on current climate conditions. This case causes the related parties to find difficulty determining the classification of cropping patterns in the future. Accurate rainfall prediction models are needed to overcome the problem of shifting rain patterns. Rainfall prediction models in determining cropping patterns are recommended by FAO, such as linear regression, which is still widely used today. This study aims to develop a new model of rainfall prediction by using the method SARIMA to determine cropping patterns to increase crop yields. Rainfall data was used from 2010 to 2020 from seven rainfall collection stations in Sleman Regency, and they are used as training data to predict future rainfall. The output of the data analysis is a prediction of rainfall in the range of January-April, which is predicted to be high, May-August, which is predicted to be low; and September-December, which is predicted to be moderate. In addition, based on the identified cropping patterns, recommendations can be given to farmers to set cropping schedules and strategies to increase the productivity of the farmland. The testing of accuracy forecasting used relative mean absolute error (RMAE) for 12 months. The results of the forecasting accuracy test for 12 months in Sleman Regency showed RMAE average of 1.46 was considered low, for it was still below 10%.
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References
L. Q. Avia, “Change in rainfall per-decades over Java Island, Indonesia,” IOP Conf Ser Earth Environ Sci, vol. 374, no. 1, pp. 1–11, 2019, doi: 10.1088/1755-1315/374/1/012037.
M. Zele et al., “Spatial and Temporal Variability of Rainfall Trends in Response to Climate Change — A Case Study : Syria,” 2022.
G. Shrivastava et al., “Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review,” Int J Comput Appl, vol. 51, no. 18, pp. 17–29, 2012, doi: 10.5120/8142-1867.
K. Alotaibi, A. R. et al., “Future predictions of rainfall and temperature using GCM and ANN for arid regions: A case study for the Qassim region, Saudi Arabia,” Water (Switzerland), vol. 10, no. 9, 2018, doi: 10.3390/w10091260.
A. Q. Munir, S. Hartati, and A. Musdholifah, “Early identification model for dengue haemorrhagic fever (DHF) outbreak areas using rule-based stratification approach,” International Journal of Intelligent Engineering and Systems, vol. 12, no. 2, pp. 246–260, 2019, doi: 10.22266/IJIES2019.0430.24.
A. Motamedi, A. Gohari, and A. T. Haghighi, “Three-decade assessment of dry and wet spells change across Iran, a fingerprint of climate change,” Sci Rep, vol. 13, no. 1, pp. 1–12, 2023, doi: 10.1038/s41598-023-30040-0.
Munir AQ et al., “GEOGRAPHIC INFORMATION SYSTEMS FOR AGRICULTURAL SUITABLE LAND,” vol. 4, no. 1, pp. 97–105, 2023.
M. Dhanaraju, et al“Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture,” Agriculture (Switzerland), vol. 12, no. 10, pp. 1–26, 2022, doi: 10.3390/agriculture12101745.
M. Mokarram and F. Aminzadeh, “Gis-Based Multicriteria Land Suitability Evaluation Using Ordered Weight Averaging With Fuzzy Quantifier : a Case Study in Shavur Plain , Iran,” Archives, vol. 38, pp. 508–512, 1996.
I. Buyung, A. Q. Munir, and P. Wanda, “Effective Breast Cancer Detection using Novel Deep Learning Algorithm,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 8, no. 2, pp. 98–104, 2023, doi: 10.33480/jitk.v8i2.4077.
M. Zhu et al., “A review of the application of machine learning in water quality evaluation,” Eco-Environment & Health, vol. 1, no. 2, pp. 107–116, 2022, doi: 10.1016/j.eehl.2022.06.001.
A. Y. Barrera-Animas et al., “Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting,” Machine Learning with Applications, vol. 7, no. November 2021, p. 100204, 2022, doi: 10.1016/j.mlwa.2021.100204.
F. Hafeez et al., “Distant temperature and humidity monitoring: Prediction and measurement,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 24, no. 3, pp. 1405–1413, 2021, doi: 10.11591/ijeecs.v24.i3.pp1405-1413.
S. Y. Siswanto and M. I. S. Sule, “The Impact of slope steepness and land use type on soil properties in Cirandu Sub-Sub Catchment, Citarum Watershed,” IOP Conf Ser Earth Environ Sci, vol. 393, no. 1, 2019, doi: 10.1088/1755-1315/393/1/012059.
J. Gu et al., “A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China,” Water (Switzerland), vol. 14, no. 3, pp. 1–20, 2022, doi: 10.3390/w14030492.
M. Champness, L. Vial, and C. Ballester, “Evaluating the Performance and Opportunity Cost of a Smart-Sensed Automated Irrigation System for Water-Saving Rice Cultivation in Temperate Australia,” 2023.
I. L. Selley et al., Encyclopedia of Geology - 1-5 Volume - Elsevier Academy. 2005.
A. Syed, T. Raza, T. T. Bhatti, and N. S. Eash, “Climate Impacts on the agricultural sector of Pakistan: Risks and solutions,” Environmental Challenges, vol. 6, no. July 2021, p. 100433, 2022, doi: 10.1016/j.envc.2021.100433.
J. L. Hatfield and J. H. Prueger, “Temperature extremes: Effect on plant growth and development,” Weather Clim Extrem, vol. 10, pp. 4–10, 2015, doi: 10.1016/j.wace.2015.08.001.
C. M. Godde et al.,, “Impacts of climate change on the livestock food supply chain; a review of the evidence,” Glob Food Sec, vo. 28, no. January, p. 100488, 2021, doi: 10.1016/j.gfs.2020.100488.
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