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@article{198295,
        author = {Cheruvu Samivulla and Dr. M. Humera Khanam and Adoni Ghousia Zeeshan and Veluru Monica and N.Keerthi and Virdala Hema Chandrika},
        title = {Quantum and Machine Learning Techniques for Rainfall Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9317-9324},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198295},
        abstract = {Rainfall prediction is a critical challenge in meteorology due to the highly non-linear and complex nature of atmospheric data. This project presents a Comparative Analysis of Classical and Quantum Machine Learning Models to evaluate their predictive accuracy and computational efficiency. While classical models like Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks have been standard, this study explores the potential of Variational Quantum Classifiers (VQC). By leveraging quantum entanglement and superposition, the proposed quantum approach aims to identify patterns that classical algorithms may overlook. Our results demonstrate that while classical models remain robust for large datasets, Quantum Machine Learning (QML) shows promising potential for high-dimensional feature mapping in localized rainfall forecasting.},
        keywords = {Quantum Machine Learning (QML), Variational Quantum Classifier (VQC), PennyLane, Rainfall Prediction, Meteorological Forecasting, Logistic Regression, Hybrid Quantum-Classical Algorithms, Feature Mapping, Qubit Embedding, NISQ Technology, Comparative Analysis.},
        month = {April},
        }