IOT AUDIO SENSOR NETWORKS AND DECISION TREES FOR ENHANCED RAIN SOUND CLASSIFICATION

Abstract

Accurately classifying rain sounds is essential in the field of climate investigation and environmental monitoring for understanding rainfall patterns, intensity, and how it affects ecosystems and urban infrastructure. This research presents a new method for rain sound classification combines decision trees (DTs) algorithms with networks of Internet of Things (IoT) audio sensors. To record ambient noises, particularly those caused by precipitation, the system makes use of a dispersed network of inexpensive IoT audio sensors placed in different places. A DTs algorithm, trained on a broad dataset including varying rain intensities and background sounds, is then applied by a central processing unit (CPU) to these recordings. When compared to more conventional approaches, experimental findings show the technique significantly improves rain sound classification accuracy, especially when it comes to differentiating between moderate and mild rain sounds and ambient noise. Automated weather alarm systems, urban drainage management, agricultural planning, and real-time rainfall monitoring are some of the potential uses for the proposed system. It helps advance environmental science, meteorology, and smart city projects by using IoT and machine learning to provide more accurate and faster rainfall data, which is essential for infrastructure planning and decision-making.

Keywords

• acoustic sensing, environmental acoustics, iot deployment, classification algorithms, rainfall patterns, data analytics, sensor fusion, ecological research.