Forecasting Climate Change Patterns to Improving Rice Harvest Using SVR for Achieving Green Economy

Carles Juliandy; Kelvin Kelvin; Apriyanto Halim; Sio Jurnalis Pipin; Frans Mikael Sinaga; Wulan Sri Lestari

doi:10.24014/ijaidm.v7i2.32393

Forecasting Climate Change Patterns to Improving Rice Harvest Using SVR for Achieving Green Economy

Carles Juliandy, Kelvin Kelvin, Apriyanto Halim, Sio Jurnalis Pipin, Frans Mikael Sinaga, Wulan Sri Lestari

Abstract

The consistently declining rice harvest will cause several economic and environmental problems. The unstable and unpredictable climate change was believed as the main problem of the declining rice harvest. We proposed a method for forecasting climate change to help the farmer in their rice cultivation. We used Support Vector Regression (SVR) to improve algorithm steps such as normalizing the data and applying an Adaptive Linear Combiner (ALC) to optimize the dataset before we processed it with the algorithm. Our model gets 95% accuracy as measured with the confusion matrix. We believe our model will help the farmers in their rice cultivation with good climate forecasting. A further benefit of this research we belief that with the well-forecasted climate, the usage of pesticides will decrease and will help the vision of the Indonesian government with a green economy

Keywords

Climate Change; Climate Forecasting; Green Economy; Rice Harvest; Support Vector Regression

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References

C. M. Annur, “Produksi Padi Indonesia Cenderung Menurun dalam 10 Tahun Terakhir.”

Badan Pusat Statistik, “Luas Panen dan Produksi Padi di Indonesia 2023,” 2023. Accessed: Mar. 26, 2024. [Online]. Available: https://www.bps.go.id/id/pressrelease/2024/03/01/2375/pada-2023--luas-panen-padi-mencapai-sekitar-10-21-juta-hektare-dengan-produksi-padi-sebesar-53-98-juta-ton-gabah-kering-giling--gkg-.html

M. L. Hidayatullah and B. U. Aulia, “Identifikasi Dampak Perubahan Iklim Terhadap Produksi Padi,” Jurnal Teknik ITS, vol. 8, no. 2, pp. 43–48, 2019, doi: 10.12962/j23373539.v8i2.49241.

A. B. Santoso, T. Supriana, and M. A. Girsang, “Pengaruh Curah Hujan pada Produksi Padi Gogo di Indonesia,” Jurnal Ilmu Pertanian Indonesia, vol. 27, no. 4, pp. 606–613, Oct. 2022, doi: 10.18343/jipi.27.4.606.

D. Hastari, S. Winanda, A. R. Pratama, N. Nurhaliza, and E. S. Ginting, “Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 1, no. 2, Feb. 2024, doi: 10.57152/predatecs.v1i2.865.

Makmun, “Green Economy: Kosep, Implementasi, dan Peranan Kementrian Keuangan”.

S. Skendžić, M. Zovko, I. P. Živković, V. Lešić, and D. Lemić, “Review: The impact of climate change on agricultural insect pests,” Insects, vol. 12, no. 5, May 2021, doi: 10.3390/insects12050440.

T. Mulyaqin, “The Impact of El Niño and La Nina on Fluctuation of Rice Production in Banten Province,” Agromet, vol. 34, no. 1, pp. 34–41, May 2020, doi: 10.29244/j.agromet.34.1.34-41.

G. S. Malhi, M. Kaur, and P. Kaushik, “Impact of climate change on agriculture and its mitigation strategies: A review,” Sustainability (Switzerland), vol. 13, no. 3, pp. 1–21, Feb. 2021, doi: 10.3390/su13031318.

N. T. Luchia, E. Tasia, I. Ramadhani, A. Rahmadeyan, and R. Zahra, “Performance Comparison Between Artificial Neural Network, Recurrent Neural Network and Long Short-Term Memory for Prediction of Extreme Climate Change,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 1, no. 2, pp. 62–70, Feb. 2024, doi: 10.57152/predatecs.v1i2.864.

M. Chantry, H. Christensen, P. Dueben, and T. Palmer, “Opportunities and challenges for machine learning in weather and climate modeling: Hard, medium and soft AI,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 379, no. 2194, Apr. 2021, doi: 10.1098/rsta.2020.0083.

S. Thirumal and R. Latha, “Machine Learning based Predictive Assessments of Impacts of Influential Climatic Conditions for the Sustainable Productivity of Paddy Crops,” SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 5, pp. 116–127, May 2023, doi: 10.14445/23488549/IJECE-V10I5P111.

Y. Wang, X. Wang, X. Li, W. Liu, and Y. Yang, “Future Climate Prediction Based on Support Vector Machine Optimization in Tianjin, China,” Atmosphere (Basel), vol. 14, no. 8, Aug. 2023, doi: 10.3390/atmos14081235.

P. Khatri, T. Arjariya, and N. S. Mitra, “Climate change forecasting using data mining algorithms,” Aqua Water Infrastructure, Ecosystems and Society, vol. 72, no. 6, pp. 1065–1083, Jun. 2023, doi: 10.2166/aqua.2023.046.

A. Malik, Y. Tikhamarine, D. Souag-Gamane, O. Kisi, and Q. B. Pham, “Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction,” Stochastic Environmental Research and Risk Assessment, vol. 34, no. 11, pp. 1755–1773, Nov. 2020, doi: 10.1007/s00477-020-01874-1.

N. W. Azani, C. P. Trisya, L. M. Sari, H. Handayani, and M. R. M. Alhamid, “Performance Comparison of ARIMA, LSTM and SVM Models for Electric Energy Consumption Analysis,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 1, no. 2, Feb. 2024, doi: 10.57152/predatecs.v1i2.869.

E. Khosla, R. Dharavath, and R. Priya, “Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression,” Environ Dev Sustain, vol. 22, no. 6, pp. 5687–5708, Aug. 2020, doi: 10.1007/s10668-019-00445-x.

Z. Zhang and W. C. Hong, “Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads,” Knowl Based Syst, vol. 228, Sep. 2021, doi: 10.1016/j.knosys.2021.107297.

Q. Quan, Z. Hao, H. Xifeng, and L. Jingchun, “Research on water temperature prediction based on improved support vector regression,” Neural Comput Appl, vol. 34, no. 11, pp. 8501–8510, Jun. 2022, doi: 10.1007/s00521-020-04836-4.

R. Rodríguez-Pérez and J. Bajorath, “Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery,” J Comput Aided Mol Des, vol. 36, no. 5, pp. 355–362, May 2022, doi: 10.1007/s10822-022-00442-9.

H. Xu and C. Jiang, “Deep belief network-based support vector regression method for traffic flow forecasting,” Neural Comput Appl, vol. 32, no. 7, pp. 2027–2036, Apr. 2020, doi: 10.1007/s00521-019-04339-x.

G. F. Fan, M. Yu, S. Q. Dong, Y. H. Yeh, and W. C. Hong, “Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling,” Util Policy, vol. 73, Dec. 2021, doi: 10.1016/j.jup.2021.101294.

X. Qu et al., “A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection”.

S. Sinsomboonthong, “Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification,” Int J Math Math Sci, vol. 2022, 2022, doi: 10.1155/2022/3584406.

A. Ambarwari, Q. J. Adrian, and Y. Herdiyeni, “Analisis Pengaruh Data Scaling Terhadap Performa Algoritme Machine Learning untuk Identifikasi Tanaman,” Jurnal RESTI, vol. 1, no. 3, pp. 117–122, 2020.

B. Han and B. Bae, “Novel phase-locked loop using adaptive linear combiner,” IEEE Transactions on Power Delivery, vol. 21, no. 1, pp. 513–514, Jan. 2006, doi: 10.1109/TPWRD.2005.860436.

Y. Singh, I. Hussain, B. Singh, and S. Mishra, “Single-phase solar grid-interfaced system with active filtering using adaptive linear combiner filter-based control scheme,” IET Generation, Transmission and Distribution, vol. 11, no. 8, pp. 1976–1984, Jun. 2017, doi: 10.1049/iet-gtd.2016.1392.

R. Taghizadeh-Mehrjardi et al., “Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models,” Geoderma, vol. 383, Feb. 2021, doi: 10.1016/j.geoderma.2020.114793.

J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf Sci (N Y), vol. 507, pp. 772–794, 2020, doi: 10.1016/j.ins.2019.06.064.

F. Mikael Sinaga, P. Sirait, and A. Halim, “Optimization of SV-kNNC using Silhouette Coefficient and LMKNN for Stock Price Prediction,” in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 326–331. doi: 10.1109/ISRITI51436.2020.9315516.

K. Bali and S. Maggu, “Weather Forecasting Using KNN in Machine Learning,” International Journal of Advances in Engineering and Management (IJAEM), vol. 3, no. 6, pp. 2395–5252, 2021, doi: 10.35629/5252-0306249255.

M. Yunus, M. K. Biddinika, and A. Fadlil, “Classification of Stunting in Children Using the C4.5 Algorithm,” Jurnal Online Informatika, vol. 8, no. 1, pp. 99–106, Jun. 2023, doi: 10.15575/join.v8i1.1062.

DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.32393

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