EFFECTIVE WEATHER PREDICTION FRAMEWORK FROM IOT SENSOR THROUGH MACHINE LEARNING

Abstract

Precise weather forecasting is required in applications like agriculture, disaster management, intelligent cities, and in the planning of renewable energy. The mass use of Internet of Things (IoT) sensors has facilitated continuous acquisition of fine-grained meteorological data, which has presented novel chances of offering localized and real-time weather forecasting. Nevertheless, the conventional forecasting tools and standard statistical models are not always effective in taking advantage of heterogeneous data provided by the IoT because of noise, data sparsity and insufficient flexibility to adapt to the changing environmental factors. In a bid to overcome these issues, this paper will come up with a capable weather forecasting system, which combines IoT-based sensing and sophisticated machine learning algorithms. The framework involves systematic collection of data, strong preprocessing in order to deal with missing and noisy sensor values and intelligent feature learning to represent multidimensional nonlinear projections of weather variables. The main goal is to enhance the accuracy, scalability, and reliability of short-term weather forecasting. The outcomes of experiments show that the suggested approach has a high-predictive performance in contrast to the baseline models that generate correct and timely weather forecasts. In general, the framework mentions the possibility of utilizing IOT sensor networks together with machine learning to create effective data-driven weather prediction systems applicable to the real-world implementation