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@article{170984,
        author = {Dr S AJITHA and Rajendra Prasad M and Ms Reshma K J},
        title = {PREDICTING CLIMATE VARIATIONS: MACHINE LEARNING APPROACH USING WEATHER FACTORS AND THE SOCIO-ECONOMIC DRIVERS OF TEMPERATURE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {2959-2969},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170984},
        abstract = {Variations in the climate are now a serious problem that have an impact on economies, cultures, and ecosystems worldwide. As essential elements of climate systems, wind speed and temperature are greatly impacted by a variety of environmental and socioeconomic factors. Because these climatic factors are dynamic and nonlinear, it is still difficult to anticipate them. The need for more precise models that incorporate socioeconomic factors that affect temperature changes in temperature prediction is the research topic this work attempts to solve. Through the use of deep learning techniques, this work seeks to further our knowledge of these impacts. The study uses a deep learning approach, integrating data from meteorological stations, socio-economic datasets, and climate-related factors, to improve the accuracy of long-term memory networks in capturing temporal dependencies in climate data. Deep learning models, particularly LSTM networks, improve temperature prediction when incorporating socio-economic variables. Industrial activity and urbanization patterns enhance accuracy. Future research should expand datasets and explore policy interventions' impact.},
        keywords = {Regression Analysis, Time-Series Forecasting, Meteorological Data, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), LSTM (Long Short-Term Memory), SVM (Support Vector Machine0, Temperature Forecasting, Principal Component Analysis (PCA), Random Forest Analysis.},
        month = {December},
        }