Leveraging Machine Learning based Ensemble Time Series Prediction Model for Rainfall Using SVM, KNN and Advanced ARIMA+ E-GARCH

Main Article Content

C. Nagesh
Koushik Reddy Chaganti
Sathvik Chaganti
S.K. Khaleelullah
P. Naresh
M.I.Thariq Hussan


Today's precipitation is growing increasingly variable, making forecasting increasingly difficult. The Indian Meteorological Department (IMD) currently employs Composite and Stochastic approaches to forecast spring storm precipitation in Asia. As a corollary, planners are unlikely to predict the macroeconomic effects of disasters (due to excessive precipitation) or famine (less precipitation). The amount of precipitation that drops dependent on a variety of factors, including the temperature of the atmosphere, humidity, velocity, mobility, and weather conditions. This paper would then employ the Hybrid time-series predictive ARIMA+ E-GARCH (Exponential Generalized Auto-Regressive Conditional Heteroskedasticity) to predict precise runoff by taking into account different climatic considerations such as maritime tension, water content, relative dampness, min-max heat, heavy ice, geostrophic tallness, breeze patterns, soil dampness, and barometric force. In perspective of RMSE, MAE, and MSE, the proposed hybrid ARIMA+E-GARCH paradigm outperformed single simulations and latest hybrid techniques.

Article Details

How to Cite
Nagesh, C., Chaganti, K. R. ., Chaganti, S. ., Khaleelullah, S., Naresh, P., & Hussan, M. . (2023). Leveraging Machine Learning based Ensemble Time Series Prediction Model for Rainfall Using SVM, KNN and Advanced ARIMA+ E-GARCH. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 353–358. https://doi.org/10.17762/ijritcc.v11i7s.7010


J. Niu and W. Zhang, "Comparative analysis of statistical models in rainfall prediction," 2015 IEEE International Conference on Information and Automation, 2015, pp. 2187-2190, doi: 10.1109/ICInfA.2015.7279650.

A. Kala and S. G. Vaidyanathan, "Prediction of Rainfall Using Artificial Neural Network," 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 2018, pp. 339-342, doi: 10.1109/ICIRCA.2018.8597421.

Mondal , D. . (2021). Remote Sensing Based Classification with Feature Fusion Using Machine Learning Techniques. Research Journal of Computer Systems and Engineering, 2(1), 28:32. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/16

C. Thirumalai, K. S. Harsha, M. L. Deepak and K. C. Krishna, "Heuristic prediction of rainfall using machine learning techniques," 2017 International Conference on Trends in Electronics and Informatics (ICEI), 2017, pp. 1114-1117, doi: 10.1109/ICOEI.2017.8300884.

P.Naresh,et.al., “Implementation of Map Reduce Based Clustering for Large Database in Cloud”, Journal For Innovative Development in Pharmaceutical and Technical Science,vol.1,pp 1-4,2018.

C. Z. Basha, N. Bhavana, P. Bhavya and S. V, "Rainfall Prediction using Machine Learning & Deep Learning Techniques," 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020, pp. 92-97, doi: 10.1109/ICESC48915.2020.9155896.

R. K. Grace and B. Suganya, "Machine Learning based Rainfall Prediction," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), 2020, pp. 227-229, doi: 10.1109/ICACCS48705.2020.9074233.

S. Kaushik, A. Bhardwaj and L. Sapra, "Predicting Annual Rainfall for the Indian State of Punjab Using Machine Learning Techniques," 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2020, pp. 151-156, doi: 10.1109/ICACCCN51052.2020.9362742.

N. Tiwari and A. Singh, "A Novel Study of Rainfall in the Indian States and Predictive Analysis using Machine Learning Algorithms," 2020 International Conference on Computational Performance Evaluation (ComPE), 2020, pp. 199-204, doi: 10.1109/ComPE49325.2020.9200091.

S. Srivastava, N. Anand, S. Sharma, S. Dhar and L. K. Sinha, "Monthly Rainfall Prediction Using Various Machine Learning Algorithms for Early Warning of Landslide Occurrence," 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1-7, doi: 10.1109/INCET49848.2020.9154184.

U. Harita, V. U. Kumar, D. Sudarsa, G. R. Krishna, C. Z. Basha and B. S. S. P. Kumar, "A Fundamental Study on Suicides and Rainfall Datasets Using basic Machine Learning Algorithms," 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020, pp. 1239-1243, doi: 10.1109/ICECA49313.2020.9297440.

H. A. Y. Ahmed and S. W. A. Mohamed, "Rainfall Prediction using Multiple Linear Regressions Model," 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 2021, pp. 1-5, doi: 10.1109/ICCCEEE49695.2021.9429650.

S. Khaleelullah, P. Marry, P. Naresh, P. Srilatha, G. Sirisha and C. Nagesh, "A Framework for Design and Development of Message sharing using Open-Source Software," 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2023, pp. 639-646, doi: 10.1109/ICSCDS56580.2023.10104679.

Naresh, P., & Suguna, R. (2019). Association Rule Mining Algorithms on Large and Small Datasets: A Comparative Study. 2019 International Conference on Intelligent Computing and Control Systems (ICCS). DOI:10.1109/iccs45141.2019.9065836.

S. J. Basha, G. L. V. Prasad, K. Vivek, E. S. Kumar and T. Ammannamma, "Leveraging Ensemble Time-series Forecasting Model to Predict the amount of Rainfall in Andhra Pradesh," 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP), 2022, pp. 1-7, doi: 10.1109/AISP53593.2022.9760553.

Prof. Barry Wiling. (2018). Identification of Mouth Cancer laceration Using Machine Learning Approach. International Journal of New Practices in Management and Engineering, 7(03), 01 - 07. https://doi.org/10.17762/ijnpme.v7i03.66

A. Samad, Bhagyanidhi, V. Gautam, P. Jain, Sangeeta and K. Sarkar, "An Approach for Rainfall Prediction Using Long Short Term Memory Neural Network," 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 2020, pp. 190-195, doi: 10.1109/ICCCA49541.2020.9250809.

M. I. Thariq Hussan, D. Saidulu, P. T. Anitha, A. Manikandan and P. Naresh (2022), Object Detection and Recognition in Real Time Using Deep Learning for Visually Impaired People. IJEER 10(2), 80-86. DOI: 10.37391/IJEER.100205.

B. Narsimha, Ch V Raghavendran, Pannangi Rajyalakshmi, G Kasi Reddy, M. Bhargavi and P. Naresh (2022), Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application. IJEER 10(2), 87-92. DOI: 10.37391/IJEER.100206.

Omondi, P., Rosenberg, D., Almeida, G., Soo-min, K., & Kato, Y. A Comparative Analysis of Deep Learning Models for Image Classification. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/128

I. Prakaisak, E. Phaisangittisagul, M. Maleewong, K. Sarinnapakorn and C. Chantrapornchai, "Detecting Anomaly and Replacement Prediction for Rainfall Open Data in Thailand," 2021 18th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2021, pp. 1-6, doi: 10.1109/JCSSE53117.2021.9493814.

T. Aruna, P. Naresh, A. Rajeshwari, M. I. T. Hussan and K. G. Guptha, "Visualization and Prediction of Rainfall Using Deep Learning and Machine Learning Techniques," 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, 2022, pp. 910-914, doi: 10.1109/ICTACS56270.2022.9988553.