[
  {
    "id": "10.1109/AISummit66170.2025.11411099",
    "type": "paper-conference",
    "title": "Hyperlocal Temperature and Humidity Prediction Using Supervised Machine Learning",
    "author": [
      {
        "family": "Gigov",
        "given": "Lyuboslav"
      },
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      },
      {
        "family": "Stoilov",
        "given": "Anton"
      }
    ],
    "container-title": "2025 2nd Global AI Summit – International Conference on Artificial Intelligence and Emerging Technology (AI Summit)",
    "issued": {
      "date-parts": [
        [
          2025
        ]
      ]
    },
    "page": "940-945",
    "publisher": "IEEE",
    "ISBN": "979-8-3315-5379-1",
    "DOI": "10.1109/AISummit66170.2025.11411099",
    "URL": "https://www.narasimharao.net/research/hyperlocal-weather-temperature-humidity-prediction-machine-learning/",
    "abstract": "Machine learning offers computationally efficient alternatives to traditional numerical weather prediction (NWP) models and global forecasting systems (GFS), which struggle with high computational costs and coarse spatial resolution. This study focuses on hyperlocal weather prediction, comparing linear regression (LR), polynomial regression (PR), decision tree regression (DTR), and random forest regression (RFR) for forecasting average temperature and relative humidity. Using ten years of historical data (2015-2025) from Sofia, Berlin, and Tokyo, we achieved substantial predictive accuracy across climatically diverse regions. For temperature prediction, linear regression and random forest demonstrated statistically equivalent performance (R² > 0.994), with linear regression achieving faster execution times (0.001s vs 0.022-0.040s). For humidity prediction, linear regression demonstrated clear superiority (R² > 0.940) over random forest (R² 0.913-0.933), while decision trees showed the poorest performance (R² 0.741-0.814). Decision trees performed competitively for temperature (R² 0.992-0.993) but struggled significantly with humidity's non-linear dynamics. These results demonstrate that model selection should prioritize the relationship structure in data rather than algorithmic complexity, with linear regression offering a balance of accuracy and computational efficiency. The findings of this study challenge the assumption that complex ensemble methods are necessary for accurate weather prediction, offering practical applications in precision agriculture, smart city infrastructure, event planning, and resource-constrained IoT deployments where real-time predictions are essential.",
    "keyword": "artificial intelligence, machine learning, weather prediction, regression analysis, hyperlocal weather forecasting, temperature prediction, humidity prediction, supervised learning, weather forecasting, hyperlocal weather prediction, relative humidity prediction, linear regression, polynomial regression, decision tree regression, random forest regression, Open-Meteo historical data, Sofia, Berlin and Tokyo, precision agriculture, IoT",
    "language": "en"
  }
]