Spatio-Temporal Rainfall Forecasting in Uttarakhand Using Ensemble Machine Learning Models
Keywords:
Rainfall Prediction, Machine Learning, LSTM Network, Uttarakhand Weather Forecasting, Time-Series AnalysisAbstract
Precise rainfall prediction is crucial for efficient agricultural planning, efficient disaster management, and efficient water resource management, particularly in ecologically fragile regions like Uttarakhand, India. The state's diversified topography and complex climatic conditions make traditional forecasting methods less accurate. In order to create an effective rainfall prediction model tailored to Uttarakhand, this paper compares and contrasts a number of machine learning algorithms, including Linear Regression, Support Vector Machine (SVM), Random Forest, Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM). Each model was trained and validated using performance metrics such as R2 score, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) based on historical weather records. With an R2 score of 0.91, an RMSE of 11.92 mm, and an MAE of 8.21 mm, the LSTM network was the most predictive model among those examined. The outcomes demonstrate how well deep learning methods—particularly LSTM—model temporal weather patterns in challenging geographic contexts. In Uttarakhand and other similar areas, this paper offers an accurate and extensible prediction model that can facilitate prompt decision-making in flood control, agriculture, and climate risk assessment.
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Alone, Ashish & Pattanaik, D. & Shukla, Anoop. (2024). Evaluation of Machine-Learning Algorithm’s Skill for Convective Weather Forecasting in Uttarakhand. 10.1007/978-981-97-3173-2_1.
Bajpai, V., Bansal, A., Verma, K., & Agarwal, S. (2020). Prediction of rainfall in Rajasthan, India using deep and wide neural network. arXiv preprint arXiv:2010.11787.
Basha CZ, Bhavana N, Bhavya P, and Sowmya V (2020). Rainfall prediction using machine learning and deep learning techniques. In the International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, Coimbatore, India: 92-97. https://doi.org/10.1109/ICESC48915.2020.9155896
Bai, H., & Ran, W. (2022). Analysis of the Vulnerability and Resilience of the Tourism Supply Chain under the Uncertain Environment of COVID-19: Case Study Based on Lijiang. Sustainability, 14(5), 2571. https://doi.org/10.3390/su14052571
Business Bliss Consultants FZE. (November 2018). Rainfall Prediction Using Machine Learning Algorithms. Retrieved from https://ukdiss.com/examples/rainfall-prediction-machine-learning.php?vref=1
Dong, N., Hao, H., Yang, M., Wei, J., Xu, S., and Kunstmann, H.: Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River, Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025, 2025.
Ghosh, S., Gourisaria, M.K., Sahoo, B. et al. A pragmatic ensemble learning approach for rainfall prediction. Discov Internet Things 3, 13 (2023). https://doi.org/10.1007/s43926-023-00044-3
Kainthura P, Sharma N. Hybrid machine learning approach for landslide prediction, Uttarakhand, India. Sci Rep. 2022 Nov 22;12(1):20101. doi: 10.1038/s41598-022-22814-9. PMID: 36418362; PMCID: PMC9684430.
Kanchan, P., & Kumar Bakkappa Shardoor, N. (2021). Rainfall Analysis and Forecasting Using Deep Learning Technique. Journal of Informatics Electrical and Electronics Engineering (JIEEE), 2(2), 1–11. https://doi.org/10.54060/JIEEE/002.02.015
Kumar, V., Kedam, N., Kisi, O. et al. A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting. Water Resour Manage 39, 271–290 (2025). https://doi.org/10.1007/s11269-024-03969-8
Kumar, V., Kedam, N., Sharma, K. V., Khedher, K. M., & Alluqmani, A. E. (2023). A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities. Sustainability, 15(18), 13724. https://doi.org/10.3390/su151813724
Li H, Li S and Ghorbani H (2024) Data-driven novel deep learning applications for the prediction of rainfall using meteorological data. Front. Environ. Sci. 12:1445967. doi: 10.3389/fenvs.2024.1445967
Mohd Usman Saeed Khan, Khan Mohammad Saifullah, Ajmal Hussain, Hazi Mohammad Azamathulla, Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India,Results in Engineering,Volume 22,2024,102093,ISSN 2590-1230,https://doi.org/10.1016/j.rineng.2024.102093
Ojha SS, Singh V, and Roshni T (2023). Comparison of machine learning techniques for rainfall-runoff modeling in Punpun river basin, India. International Journal of Advanced and Applied Sciences, 10(4): 114-120 https://doi.org/10.21833/ijaas.2023.04.014
Patil, R., Kulkarni, P., Kulkarni, S., Bhatt, A. (2024). A Hybrid Model for Rain Prediction Using Machine Learning Algorithm. In: Pant, M., Deep, K., Nagar, A. (eds) Proceedings of the 12th International Conference on Soft Computing for Problem Solving. SocProS 2023. Lecture Notes in Networks and Systems, vol 994. Springer, Singapore. https://doi.org/10.1007/978-981-97-3180-0_57
Prathibha, & D. Leela Rani. (2025). Rainfall Forecasting in India Using Combined Machine Learning Approach and Soft Computing Techniques : A Hybrid Model. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.785
Shah, P.J., Timbadia, D.H., Sudhanvan, S., Agrawal, S. (2021). Advanced Rainfall Prediction Model for India Using Various Regression Algorithms. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_30
Sindhura, S., Dedeepya, P., Chowdary, N. S., Megha Shyam, K., & Krishna Thati, B. (2024). A Comparative Analysis of Rainfall-Prediction Using Optimized Machine Learning Algorithms. Environmental Industry Letters, 2(2), 44–56. https://doi.org/10.15157/eil.2024.2.2.44-56
Singh, A. K., Kumar, P., Ali, R., Al-Ansari, N., Vishwakarma, D. K., Kushwaha, K. S., Panda, K. C., Sagar, A., Mirzania, E., Elbeltagi, A., Kuriqi, A., & Heddam, S. (2022). An Integrated Statistical-Machine Learning Approach for Runoff Prediction. Sustainability, 14(13), 8209. https://doi.org/10.3390/su14138209
Singh, S., Kumar, D., Vishwakarma, D. et al. Seasonal rainfall pattern using coupled neural network-wavelet technique of southern Uttarakhand, India. Theor Appl Climatol 155, 5185–5201 (2024). https://doi.org/10.1007/s00704-024-04940-8
Tandon, A., Awasthi, A. & Pattnayak, K.C. Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states. Sci Rep 15, 10345 (2025). https://doi.org/10.1038/s41598-024-84360-w
Vijendra Kumar & Naresh Kedam & Ozgur Kisi & Saleh Alsulamy & Khaled Mohamed Khedher & Mohamed Abdelaziz Salem, 2025. "A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(1), pages 271-290, January. DOI: 10.1007/s11269-024-03969-8
Yadav, Pooja & Sagar, Atish. (2019). Rainfall Prediction Using Artificial Neural Network (ANN) for tarai Region of Uttarakhand. Current Journal of Applied Science and Technology. 1-7. 10.9734/cjast/2019/v33i530096.
Zhang, H., Liu, Y., Zhang, C., & Li, N. (2025). Machine Learning Methods for Weather Forecasting: A Survey. Atmosphere, 16(1), 82. https://doi.org/10.3390/atmos16010082
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