Forecasting Upwelling Phenomena in Lake Laut Tawar: A Semi-Supervised Learning Approach

Authors

  • Muhammad Zia Ulhaq Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Muhammad Farid Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Zahra Ifma Aziza Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Teuku Muhammad Faiz Nuzullah Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Fakhrus Syakir Department of Electrical Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
  • Novi Reandy Sasmita Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/ijds.v2i2.211

Keywords:

Upwelling, Historical climate data, K-Means clustering, Support Vector Machine

Abstract

The current climate change is causing the upwelling phenomenon to occur frequently in lakes and reservoirs. As a result of this phenomenon, thousands of fish die, causing floating net cage fish farmers to suffer losses. From existing studies, temperature sensors are used to determine the current condition of a body of water experiencing upwelling or not. Therefore, this study applies clustering to historical climate data from 2017-2023 using a semi-supervised learning approach that produces two labels: "potential for upwelling" and "no potential for upwelling." In the clustering process, the data is divided into two clusters using K-Means Clustering, and Support Vector Machine (SVM) is chosen to classify them. The performance of the proposed algorithm is expressed with accuracy, precision, recall, and F1-score values of 0.99, 0.995, 0.970, and 0.985, respectively. The analysis results show that this model has excellent performance in identifying upwelling potential. By using this method, information about upwelling potential can be obtained more quickly and accurately, allowing fish farmers to take appropriate preventive measures. This study also shows that the combination of K-Means Clustering and Support Vector Machine (SVM) can be effectively used to analyze historical climate data and generate useful predictions.

Downloads

Download data is not yet available.

References

  1. Lam, V. W. Y., Allison, E. H., Bell, J. D., Blythe, J., Cheung, W. W. L., Frölicher, T. L., Gasalla, M. A., and Sumaila, U. R. (2020). Climate Change, Tropical Fisheries and Prospects for Sustainable Development, Nature Reviews Earth & Environment, Vol. 1, No. 9, 440–454. doi:10.1038/s43017-020-0071-9.
  2. Hewindati, Y. T., and Novita Sari, D. (2023). Effect of Life History on Alopias pelagicus Overexploitation Vulnerability: A Literature Review, Leuser Journal of Environmental Studies, Vol. 1, No. 2, 39–46. doi:10.60084/ljes.v1i2.54.
  3. Agronet.id. (2017). Ribuan Ikan Mati di Danau Laut Tawar Aceh, from https://www.agronet.co.id/detail/indeks/berita/818-Ribuan-Ikan-Mati-diDanau-Laut-Tawar-Aceh, accessed 10-8-2024.
  4. Vinolia. (2023). Lagi, Puluhan Ton Ikan di Danau Maninjau Mati, from https://www.mongabay.co.id/2023/05/30/lagi-puluhan-ton-ikan-di-danau-maninjau-mati/, accessed 8-8-2024.
  5. Kompas.com. (2018). Ikan Mati di Danau Toba Mencapai 180 Ton, Ini Penyebabnya..., from https://nasional.kompas.com/read/2018/08/29/11290411/ikan-mati-di-danau-toba-mencapai-180-ton-ini-penyebabnya, accessed 9-8-2024.
  6. Kompas.com. (2024). Cuaca Ekstrem, Belasan Ton Ikan Mati di Keramba Waduk Saguling, from https://bandung.kompas.com/read/2024/11/05/055942178/cuaca-ekstrem-belasan-ton-ikan-mati-di-keramba-waduk-saguling, accessed 5-11-2024.
  7. Chang, P., Xu, G., Kurian, J., Small, R. J., Danabasoglu, G., Yeager, S., Castruccio, F., Zhang, Q., Rosenbloom, N., and Chapman, P. (2023). Uncertain Future of Sustainable Fisheries Environment in Eastern Boundary Upwelling Zones under Climate Change, Communications Earth & Environment, Vol. 4, No. 1, 19. doi:10.1038/s43247-023-00681-0.
  8. Hidayat, A. I. (2021). Sistem Pendeteksi Dan Peringatan Dini Upwelling, Jurnal Ilmiah Ilmu Komputer, Vol. 7, No. 1, 55–61. doi:10.35329/jiik.v7i1.187.
  9. Susanti, S. (2020). Kadar Senyawa Toksik Dalam Darah Ikan Nila (Oreochromis niloticus) Saat Musim Up-Welling Di Danau Maninjau, Sumatra Barat, Jurnal Sain Veteriner, Vol. 38, No. 1, 25. doi:10.22146/jsv.29322.
  10. Dang, V.-T., Vu, V.-V., Do, H.-Q., and Oanh Le, T. K. (2021). Graph Based Clustering with Constraints and Active Learning, Journal of Computer Science and Cybernetics, Vol. 37, No. 1, 71–89. doi:10.15625/1813-9663/37/1/15773.
  11. C A Padmanabha Reddy, Y., Viswanath, P., and Eswara Reddy, B. (2018). Semi-Supervised Learning: A Brief Review, International Journal of Engineering & Technology, Vol. 7, No. 1.8, 81. doi:10.14419/ijet.v7i1.8.9977.
  12. Sun, F., Yu, F., Si, G., Wang, J., Xu, A., Pan, J., and Tang, Y. (2022). Characteristics and Influencing Factors of Frontal Upwelling in the Yellow Sea in Summer, Acta Oceanologica Sinica, Vol. 41, No. 7, 84–96. doi:10.1007/s13131-021-1967-z.
  13. Kim, D., Choi, J.-G., Park, J., Kwon, J.-I., Kim, M.-H., and Jo, Y.-H. (2023). Upwelling Processes Driven by Contributions from Wind and Current in the Southwest East Sea (Japan Sea), Frontiers in Marine Science, Vol. 10. doi:10.3389/fmars.2023.1165366.
  14. Prediction of Dam Horizontal Displacement Based on CNN-LSTM and Attention Mechanism. (2021). Academic Journal of Architecture and Geotechnical Engineering, Vol. 3, No. 1. doi:10.25236/AJAGE.2021.030103.
  15. Cho, B., Dayrit, T., Gao, Y., Wang, Z., Hong, T., Sim, A., and Wu, K. (2020). Effective Missing Value Imputation Methods for Building Monitoring Data, 2020 IEEE International Conference on Big Data (Big Data), IEEE, 2866–2875. doi:10.1109/BigData50022.2020.9378230.
  16. Lee, J. Y., and Styczynski, M. P. (2018). NS-kNN: a modified k-nearest neighbors approach for imputing metabolomics data, Metabolomics, Vol. 14, No. 12, 153. doi:10.1007/s11306-018-1451-8.
  17. Jadhav, A., Pramod, D., and Ramanathan, K. (2019). Comparison of performance of data imputation methods for numeric dataset, Applied Artificial Intelligence, Vol. 33, No. 10, 913–933.
  18. Fadlil, A., Herman, and Praseptian M, D. (2022). K Nearest Neighbor Imputation Performance on Missing Value Data Graduate User Satisfaction, Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), Vol. 6, No. 4, 570–576. doi:10.29207/resti.v6i4.4173.
  19. Saeipourdizaj, P., Sarbakhsh, P., and Gholampour, A. (2021). Application of Imputation Methods for Missing Values of PM 10 and O 3 Data: Interpolation, Moving Average and K-Nearest Neighbor Methods, Environmental Health Engineering and Management, Vol. 8, No. 3, 215–226. doi:10.34172/EHEM.2021.25.
  20. Chung, H., and Lee, J. (2022). Iterative Semi-Supervised Learning Using Softmax Probability, Computers, Materials & Continua, Vol. 72, No. 3, 5607–5628. doi:10.32604/cmc.2022.028154.
  21. Taherdoost, H. (2024). Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems, Information, Vol. 15, No. 8, 491. doi:10.3390/info15080491.
  22. Noviandy, T. R., Hardi, I., Zahriah, Z., Sofyan, R., Sasmita, N. R., Hilal, I. S., and Idroes, G. M. (2024). Environmental and Economic Clustering of Indonesian Provinces: Insights from K-Means Analysis, Leuser Journal of Environmental Studies, Vol. 2, No. 1, 41–51. doi:10.60084/ljes.v2i1.181.
  23. Lund, B., and Ma, J. (2021). A Review of Cluster Analysis Techniques and Their Uses in Library and Information Science Research: K-Means and K-Medoids Clustering, Performance Measurement and Metrics, Vol. 22, No. 3, 161–173. doi:10.1108/PMM-05-2021-0026.
  24. Meng, C., Trinh, L., Xu, N., Enouen, J., and Liu, Y. (2022). Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset, Scientific Reports, Vol. 12, No. 1, 7166. doi:10.1038/s41598-022-11012-2.
  25. Noviandy, T. R., Idroes, G. M., Mohd Fauzi, F., and Idroes, R. (2024). Application of Ensemble Machine Learning Methods for QSAR Classification of Leukotriene A4 Hydrolase Inhibitors in Drug Discovery, Malacca Pharmaceutics, Vol. 2, No. 2, 68–78. doi:10.60084/mp.v2i2.217.
  26. Noviandy, T. R., Idroes, G. M., Tallei, T. E., Handayani, D., and Idroes, R. (2024). QSAR Modeling for Predicting Beta-Secretase 1 Inhibitory Activity in Alzheimer’s Disease with Support Vector Regression, Malacca Pharmaceutics, Vol. 2, No. 2, 79–85. doi:10.60084/mp.v2i2.226.
  27. Rad, J. A., Parand, K., and Chakraverty, S. (Eds.). (2023). Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines, Springer Nature Singapore, Singapore. doi:10.1007/978-981-19-6553-1.
  28. Rochim, A. F., Widyaningrum, K., and Eridani, D. (2021). Performance Comparison of Support Vector Machine Kernel Functions in Classifying COVID-19 Sentiment, 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), IEEE, 224–228. doi:10.1109/ISRITI54043.2021.9702845.

Downloads

Published

2024-11-19

How to Cite

Ulhaq, M. Z., Farid, M., Aziza, Z. I., Nuzullah, T. M. F., Syakir, F., & Sasmita, N. R. (2024). Forecasting Upwelling Phenomena in Lake Laut Tawar: A Semi-Supervised Learning Approach. Infolitika Journal of Data Science, 2(2), 53–61. https://doi.org/10.60084/ijds.v2i2.211

Issue

Vol. 2 No. 2 (2024): November 2024

Section

Articles

License

Copyright (c) 2024 Muhammad Zia Ulhaq, Muhammad Farid, Zahra Ifma Aziza, Teuku Muhammad Faiz Nuzullah, Fakhrus Syakir, Novi Reandy Sasmita

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.