# Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniques

arXiv: https://arxiv.org/abs/2310.11001 · HTML: https://mlsanity.com/paper/2310.11001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Continuous online sequence learning with an unsupervised neural network model](https://mlsanity.com/paper/1512.05463/) (2015)
- [Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory](https://mlsanity.com/paper/1503.07469/) (2015)
- [The Internet of Things: Secure Distributed Inference](https://mlsanity.com/paper/1806.09199/) (2018)
- [EDIMA: Early Detection of IoT Malware Network Activity Using Machine Learning Techniques](https://mlsanity.com/paper/1906.09715/) (2019)

## Abstract (arXiv)

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.