Currently existing there are two types of remote wildfire detection technological approaches — camera-based systems, employing multiband IR imagery and thermal analysis; Wireless Sensor Network (WSN’s) designs, employing environmental parameter sensory elements and long distance wireless communication modules. Within this article a novel fusion-based terrestrial system is proposed as an alternative — with the design structure centered around utilizing triple IR band analysis via single-pixel optoelectronic detectors and long distance LoRa-based wireless communication. In addition two types of low-level data analysis are used: based on the Dempster-Shafer theory — a generalization of Bayesian theory; Linear Discriminant Analysis (LDA) — a linear classification method. The system utilizes a centralized Data Stream Association Rule Mining (DSARM) framework with an Adaptive Multiple Regression (AMR) extension. The aim of the proposed design is to make use of the advantages in both currently existing approaches, as well as reducing false positive detection, resulting from external environmental factors, by limiting their parasitic effect onto the wildfire detection device.
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