Spatially-resolved hyperlocal weather prediction and anomaly detection
  using IoT sensor networks and machine learning techniques
Anita B. Agarwal Rohit Rajesh Nitin Arul
Accurate and timely hyperlocal weather predictions are essential for various
applications, ranging from agriculture to disaster management. In this paper,
we propose a novel approach that combines hyperlocal weather prediction and
anomaly detection using IoT sensor networks and advanced machine learning
techniques. Our approach leverages data from multiple spatially-distributed yet
relatively close locations and IoT sensors to create high-resolution weather
models capable of predicting short-term, localized weather conditions such as
temperature, pressure, and humidity. By monitoring changes in weather
parameters across these locations, our system is able to enhance the spatial
resolution of predictions and effectively detect anomalies in real-time.
Additionally, our system employs unsupervised learning algorithms to identify
unusual weather patterns, providing timely alerts. Our findings indicate that
this system has the potential to enhance decision-making.