Evaluating Machine Learning Models To Enhance Rainfall Prediction
Keywords:
Machine learning, Logistic regression, Decision Tree, KNN, MAE, MSE, RMSE
Abstract
Rainfall forecast plays an important role in elevating consciousness about the possible hazards related with it and helping individuals take dynamic measures for their safety. It is used for various purposes like agriculture, water conservation, and seismic reinforcing. Heavy rainfall forecast is a significant problem for the meteorological department. It combines meteorological data, historical weather patterns, and complex computer models. They rely on historical data to identify patterns between rainfall and other agents like temperature, humidity, wind, etc. Statistical techniques are also used to analyze vast datasets and find relationships. The major aim of this study is to recognize the pertinent atmospheric features that create rainfall. This paper investigates the performance of the ML models, namely Decision tree, KNN(K-Nearest Neighbor), and Logistic regression. These model’s performances have been calculated through the evaluation metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The Standard and Min-Max correlation technique was used to select applicable climatic parameters that were used as input for the ML model. The data record was gathered from Kaggle to measure the performance of ML models. The study revealed that KNN surpassed the other models by an accuracy rate of 84.183% compared to Decision Tree with the rate of 83.762%.
References
1. Hassan, M.M., M.A.T. Rony, M.A.R. Khan,et al.,“Machine learning-based rainfall prediction: Unveiling insights and forecasting for improved preparedness,” IEEE access: practical innovations, open solutions, 11, pp. 132196–132222 (2023).
2. Liyew, C.M., and H.A. Melese, “Machine learning Techniques to predict daily rainfall amount,” Journal of big data, 8 (1) (2021). https://doi.org/10.1186/s40537-021-00545-4
3. “Indian summer monsoon rainfall prediction: A comparison of iterative and non-iterative approaches,” Applied soft computing, 70, pp. 1122–1134 (2018). https://doi.org/10.1016/j.asoc.2017.08.055
4. Zoremsanga, C., and J. Hussain, “Particle swarm optimize deep learning models for rainfall prediction: A case study in aizawl, Mizoram,” IEEE access: practical innovations, open solutions, 12, pp. 57172–57184 (2024).10.1109/ACCESS.2024.3390781
5. Balamurugan, M.S., and R. Manojkumar, “Study of short term rain forecasting using machine learning based approach,” Wireless Networks, 27 (8), pp. 5429–5434(2019). https://link.springer.com/article/10.1007/s11276-019-02168-3
6. Tharun, V.P., R. Prakash, and S. Renuga Devi, “Prediction of Rainfall Using Data Mining Techniques,” http://dx.doi.org/10.1109/ICICCT.2018.8473177 Zhang, X., S.N. Mohanty, A.K. Parida, S.K. Pani, B. Dong, and
7. X. Cheng, “Annual and non-monsoon rainfall prediction modelling using SVR-MLP: An empirical study from odisha,” IEEE access: practical innovations, open solutions, 8, pp 30223– 30233 (2020). 10.1109/ACCESS.2020.2972435
8. Natarajan, S., and A. Jegan Bharath Kumar, “Generating Spatial Distribution and Forecasting the Rainfall by Suitable ML Models- A Case Study of Aiyar River Basin,Tiruchirappalli District,” MATEC Web of Conferences, 400, p.02011 (2024). http://dx.doi.org/10.1051/matecconf/20244000201
9. Kagabo, J., G.R. Kattel, J. Kazora, C.N. Shangwe, and F. Habiyakare, “Application of machine learning algorithms in predicting extreme rainfall events in Rwanda,” Atmosphere,15 (6), p. 691(2024).https://doi.org/10.3390/atmos15060691
10. Sulistiyono, M., B. Satria, A. Sidauruk, and R. Wardhana, “Rainfall prediction using multiple linear regression algorithm,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), 9 (1), pp. 17–22(2023). http://dx.doi.org/10.33480/jitk.v9i1.4203
11. Bouziane, S., B. Aghoutane, A. Moumen, A. Sahlaoui,and A. El Ouali, “Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches,” E3S Web of Conferences, 489, p. 04010 (2024).http://dx.doi.org/10.1051/e3sconf/202448904010
12. Singh, S., A. Mukherjee, J. Panda, A. Choudhury, and S.Bhattacharyya, “Analysis and forecasting of temporal rainfall variability over hundred Indian cities using deep learning approaches,” Earth systems and environment,(2024). http://dx.doi.org/10.1007/s41748-024-00396-y
13. Kang, J., B. Wan, Z. Gao, S. Zhou, H. Chen, and H.Shen, “Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province,” Scientific reports, 14 (1), pp. 1–12(2024). http://dx.doi.org/10.1038/s41598-024-646790
14. Chang, M.-J., I.-H. Huang, C.-T. Hsu, S.-J. Wu, J.-S.Lai, and G.- F. Lin, “Long-Term Flooding Maps Forecasting System Using Series Machine Learning and Numerical Weather Prediction System,” WATER,
15. 14(20),p.3346(2022).http://dx.doi.org/10.3390/w14203346
16. Al Duhayyim, M., H.G. Mohamed, J.S. Alzahrani, et al.,“Modeling of Fuzzy Cognitive Maps with a MetaheuristicsBased Rainfall Prediction System,” Sustainability: Science Practice and Policy, 15 (1), p. 25(2022). https://doi.org/10.3390/su15010025
17. Anaraki, M.V., M. Kadkhodazadeh, A. Morshed-Bozorgdel, and S. Farzin, “Predicting rainfall response to climate change and uncertainty analysis: Introducing a novel downscaling CMIP6 models technique based on the stacking ensemble machine learning,” Journal of water and climate change, 14 (10), pp. 3671–3691 (2023). https://dx.doi.org/10.2166/wcc.2023.477
18. Asha, P., A. Jesudoss, S.P. Mary, K.V.S. Sandeep, and K.H. Vardhan, “An efficient hybrid Machine Learning classifier for rainfall prediction,” Journal of physics. Conference series, 1770 (1), p. 012012 (2021). 10.1088/1742-6596/1770/1/012012
19. Abuhammad, H., “Rainfall prediction using deep learning Algorithms,” Journal of electrical systems, 20(7s),pp.1696– 1704(2024). http://dx.doi.org/10.52783/jes.3765
20. Andriyana, Y., A.N. Falah, B.N. Ruchjana, et al., “Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island,Indonesia,” Science in China, Series A: Mathematics,12(15),p.2304(2024).http://dx.doi.org/10.3390/ma th12152304
21. Aïzansi, A.N., K.O. Ogunjobi, and F.K. Ogou, “Monthly rainfall prediction using artificial neural network (case study: Republic of Benin),” Environmental Data Science, 3, p. e11 (2024). http://dx.doi.org/10.1017/eds.2024.10
22. Gaol, G.A.M.L., M. Syafrullah, and S. Supardi, “Comparison of monthly rainfall prediction using Long Short Term Memory and Multi Layer Perceptron methods in South Tangerang City,” Jurnal Sisfokom (Sistem Informasi dan Komputer), 13 (2), pp. 293–297 (2024). http://dx.doi.org/10.32736/sisfokom.v13i2.2149
23. Vinta, S.R., and R. Peeriga, “Rainfall Prediction using XGB Model with the Australian Dataset,” EAI Endorsed Transactions on Energy Web, 11, (2024). http://dx.doi.org/10.4108/ew.5386 [23] Aryastana, P., L. Dewi, and P.I. Wahyuni, “A study of rainfall thresholds for landslides in Badung Regency using satellitederived rainfall grid datasets,” International Journal of Advances in Engineering Sciences and Applied Mathematics, 13 (2),p.197(2024). 10.11591/ijaas.v13.i2.pp197-208
24. Küllahcı, K., and A. Altunkaynak, “Maximizing daily rainfall prediction accuracy with maximum overlap discrete wavelet transform‐based machine learning models,” International Journal of Climatology, 44 (10), pp. 3405–3426 (2024). https://doi.org/10.1002/joc.8530
25. He, Y., B. Wang, J. Liu, et al., “Assessment of the decadal prediction skill of Sahel rainfall in CMIP5 and CMIP6,” Journal of climate, 37 (8), pp. 2471–2490 (2024). https://doi.org/10.1175/JCLI-D-23-0051.1
26. Vigil A, Bharathi S. Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel NeuralNetwork Techniques. Traitement du Signal. 2021 Oct 1;38(5). https://doi.org/10.18280/ts.380532
27. Vigil A, Pa thak P, Upadhyay S, Singh D, Garg V Blockchain Over Transaction System. In2018 3rd International Conference on Communication and ElectronicsSystems (ICCES) 2018 Oct 15 (pp. 1111-1117). IEEE https://doi.org/10.1109/CESYS.2018.8723962
28. Vigil MA, Mirutuhula M, Sarvagna S, Supraja R, Reddy GP. DNA Sequencing Using Machine Learning Algorithms. In2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) 2022 Dec 8 (Vol. 1, pp. 1-4). IEEE. https://doi.org/10.1109/ICDSAAI55433.2022.10028805
2. Liyew, C.M., and H.A. Melese, “Machine learning Techniques to predict daily rainfall amount,” Journal of big data, 8 (1) (2021). https://doi.org/10.1186/s40537-021-00545-4
3. “Indian summer monsoon rainfall prediction: A comparison of iterative and non-iterative approaches,” Applied soft computing, 70, pp. 1122–1134 (2018). https://doi.org/10.1016/j.asoc.2017.08.055
4. Zoremsanga, C., and J. Hussain, “Particle swarm optimize deep learning models for rainfall prediction: A case study in aizawl, Mizoram,” IEEE access: practical innovations, open solutions, 12, pp. 57172–57184 (2024).10.1109/ACCESS.2024.3390781
5. Balamurugan, M.S., and R. Manojkumar, “Study of short term rain forecasting using machine learning based approach,” Wireless Networks, 27 (8), pp. 5429–5434(2019). https://link.springer.com/article/10.1007/s11276-019-02168-3
6. Tharun, V.P., R. Prakash, and S. Renuga Devi, “Prediction of Rainfall Using Data Mining Techniques,” http://dx.doi.org/10.1109/ICICCT.2018.8473177 Zhang, X., S.N. Mohanty, A.K. Parida, S.K. Pani, B. Dong, and
7. X. Cheng, “Annual and non-monsoon rainfall prediction modelling using SVR-MLP: An empirical study from odisha,” IEEE access: practical innovations, open solutions, 8, pp 30223– 30233 (2020). 10.1109/ACCESS.2020.2972435
8. Natarajan, S., and A. Jegan Bharath Kumar, “Generating Spatial Distribution and Forecasting the Rainfall by Suitable ML Models- A Case Study of Aiyar River Basin,Tiruchirappalli District,” MATEC Web of Conferences, 400, p.02011 (2024). http://dx.doi.org/10.1051/matecconf/20244000201
9. Kagabo, J., G.R. Kattel, J. Kazora, C.N. Shangwe, and F. Habiyakare, “Application of machine learning algorithms in predicting extreme rainfall events in Rwanda,” Atmosphere,15 (6), p. 691(2024).https://doi.org/10.3390/atmos15060691
10. Sulistiyono, M., B. Satria, A. Sidauruk, and R. Wardhana, “Rainfall prediction using multiple linear regression algorithm,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), 9 (1), pp. 17–22(2023). http://dx.doi.org/10.33480/jitk.v9i1.4203
11. Bouziane, S., B. Aghoutane, A. Moumen, A. Sahlaoui,and A. El Ouali, “Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches,” E3S Web of Conferences, 489, p. 04010 (2024).http://dx.doi.org/10.1051/e3sconf/202448904010
12. Singh, S., A. Mukherjee, J. Panda, A. Choudhury, and S.Bhattacharyya, “Analysis and forecasting of temporal rainfall variability over hundred Indian cities using deep learning approaches,” Earth systems and environment,(2024). http://dx.doi.org/10.1007/s41748-024-00396-y
13. Kang, J., B. Wan, Z. Gao, S. Zhou, H. Chen, and H.Shen, “Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province,” Scientific reports, 14 (1), pp. 1–12(2024). http://dx.doi.org/10.1038/s41598-024-646790
14. Chang, M.-J., I.-H. Huang, C.-T. Hsu, S.-J. Wu, J.-S.Lai, and G.- F. Lin, “Long-Term Flooding Maps Forecasting System Using Series Machine Learning and Numerical Weather Prediction System,” WATER,
15. 14(20),p.3346(2022).http://dx.doi.org/10.3390/w14203346
16. Al Duhayyim, M., H.G. Mohamed, J.S. Alzahrani, et al.,“Modeling of Fuzzy Cognitive Maps with a MetaheuristicsBased Rainfall Prediction System,” Sustainability: Science Practice and Policy, 15 (1), p. 25(2022). https://doi.org/10.3390/su15010025
17. Anaraki, M.V., M. Kadkhodazadeh, A. Morshed-Bozorgdel, and S. Farzin, “Predicting rainfall response to climate change and uncertainty analysis: Introducing a novel downscaling CMIP6 models technique based on the stacking ensemble machine learning,” Journal of water and climate change, 14 (10), pp. 3671–3691 (2023). https://dx.doi.org/10.2166/wcc.2023.477
18. Asha, P., A. Jesudoss, S.P. Mary, K.V.S. Sandeep, and K.H. Vardhan, “An efficient hybrid Machine Learning classifier for rainfall prediction,” Journal of physics. Conference series, 1770 (1), p. 012012 (2021). 10.1088/1742-6596/1770/1/012012
19. Abuhammad, H., “Rainfall prediction using deep learning Algorithms,” Journal of electrical systems, 20(7s),pp.1696– 1704(2024). http://dx.doi.org/10.52783/jes.3765
20. Andriyana, Y., A.N. Falah, B.N. Ruchjana, et al., “Spatial Durbin Model with Expansion Using Casetti’s Approach: A Case Study for Rainfall Prediction in Java Island,Indonesia,” Science in China, Series A: Mathematics,12(15),p.2304(2024).http://dx.doi.org/10.3390/ma th12152304
21. Aïzansi, A.N., K.O. Ogunjobi, and F.K. Ogou, “Monthly rainfall prediction using artificial neural network (case study: Republic of Benin),” Environmental Data Science, 3, p. e11 (2024). http://dx.doi.org/10.1017/eds.2024.10
22. Gaol, G.A.M.L., M. Syafrullah, and S. Supardi, “Comparison of monthly rainfall prediction using Long Short Term Memory and Multi Layer Perceptron methods in South Tangerang City,” Jurnal Sisfokom (Sistem Informasi dan Komputer), 13 (2), pp. 293–297 (2024). http://dx.doi.org/10.32736/sisfokom.v13i2.2149
23. Vinta, S.R., and R. Peeriga, “Rainfall Prediction using XGB Model with the Australian Dataset,” EAI Endorsed Transactions on Energy Web, 11, (2024). http://dx.doi.org/10.4108/ew.5386 [23] Aryastana, P., L. Dewi, and P.I. Wahyuni, “A study of rainfall thresholds for landslides in Badung Regency using satellitederived rainfall grid datasets,” International Journal of Advances in Engineering Sciences and Applied Mathematics, 13 (2),p.197(2024). 10.11591/ijaas.v13.i2.pp197-208
24. Küllahcı, K., and A. Altunkaynak, “Maximizing daily rainfall prediction accuracy with maximum overlap discrete wavelet transform‐based machine learning models,” International Journal of Climatology, 44 (10), pp. 3405–3426 (2024). https://doi.org/10.1002/joc.8530
25. He, Y., B. Wang, J. Liu, et al., “Assessment of the decadal prediction skill of Sahel rainfall in CMIP5 and CMIP6,” Journal of climate, 37 (8), pp. 2471–2490 (2024). https://doi.org/10.1175/JCLI-D-23-0051.1
26. Vigil A, Bharathi S. Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel NeuralNetwork Techniques. Traitement du Signal. 2021 Oct 1;38(5). https://doi.org/10.18280/ts.380532
27. Vigil A, Pa thak P, Upadhyay S, Singh D, Garg V Blockchain Over Transaction System. In2018 3rd International Conference on Communication and ElectronicsSystems (ICCES) 2018 Oct 15 (pp. 1111-1117). IEEE https://doi.org/10.1109/CESYS.2018.8723962
28. Vigil MA, Mirutuhula M, Sarvagna S, Supraja R, Reddy GP. DNA Sequencing Using Machine Learning Algorithms. In2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) 2022 Dec 8 (Vol. 1, pp. 1-4). IEEE. https://doi.org/10.1109/ICDSAAI55433.2022.10028805
Published
2024-09-05
How to Cite
Antony Vigil M S, Shwetha R, Swati Singh, & Shruthi R. (2024). Evaluating Machine Learning Models To Enhance Rainfall Prediction. Revista Electronica De Veterinaria, 25(1S), 646-652. https://doi.org/10.69980/redvet.v25i1S.805
Section
Articles
